admin swin-unet train file

.gitattributes

@@ -0,0 +1,5 @@
+# Disabled LFS tracking to avoid pushing large files
+#*.tif filter=lfs diff=lfs merge=lfs -text
+#*.joblib filter=lfs diff=lfs merge=lfs -text
+#*.nc filter=lfs diff=lfs merge=lfs -text
+#*.ipynb filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,84 @@
+# Ignore all model weights and large data
+*.joblib
+*.nc
+*.tif
+*.tiff
+*.png
+*.jpg
+*.jpeg
+*.h5
+*.pt
+*.ckpt
+*.pb
+*.npz
+*.npy
+*.hdf5
+*.pth
+*.onnx
+*.zip
+*.tar
+*.tar.gz
+*.7z
+*.rar
+*.exe
+*.dll
+*.so
+*.bin
+*.sav
+*.csv
+*.parquet
+*.feather
+*.db
+*.sqlite
+*.log
+*.bak
+*.tmp
+*~
+
+# Ignore model info/metadata if không cần backup
+# *.json
+
+# Ignore cache, prediction, backup folders
+dataset_cache/
+predictions/
+backup_model_train/
+backup_ketquaphanloai/
+backup_S3_download_Amazon/
+model_train/
+__pycache__/
+# Ignore large data files
+ndvi_results/
+ndvi_cache/
+prediction_cache/
+dataset_cache/
+bfg.jar
+..bfg-report/
+.dvc/
+
+# Ignore model outputs but keep info json
+model_train/*.joblib
+model_train/*.tif
+model_train/*.png
+model_train/*.h5
+model_train/*.pt
+model_train/*.pth
+model_train/*.ckpt
+model_train/*.npz
+model_train/*.npy
+model_train/*.zip
+model_train/*.tar
+model_train/*.tar.gz
+model_train/*.7z
+model_train/*.rar
+model_train/*.csv
+model_train/*.parquet
+model_train/*.feather
+model_train/*.db
+model_train/*.sqlite
+model_train/*.log
+
+# VSCode settings
+.vscode/
+
+# Jupyter checkpoints
+.ipynb_checkpoints/

.stfolder/syncthing-folder-a35f20.txt

@@ -0,0 +1,5 @@
+# This directory is a Syncthing folder marker.
+# Do not delete.
+
+folderID: rs-data
+created: 2026-04-03T11:35:19+07:00

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "python-envs.defaultEnvManager": "ms-python.python:conda",
+    "python-envs.defaultPackageManager": "ms-python.python:conda",
+    "python-envs.pythonProjects": []
+}

01.train_ODC.ipynb

@@ -0,0 +1,947 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "912ed572-1658-406b-976c-cd6de2d4e89e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'easi_tools'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[0;32m<timed exec>:4\u001b[0m\n",
+      "File \u001b[0;32m~/CSIROBoeingPhase5-Vietnam/new_import_ODC.py:23\u001b[0m\n\u001b[1;32m     21\u001b[0m easinotebooksrepo \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/home/jovyan/easi-notebooks\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m easinotebooksrepo \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m sys\u001b[38;5;241m.\u001b[39mpath: sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(easinotebooksrepo)\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01measi_tools\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m EasiDefaults, xarray_object_size, notebook_utils, unset_cachingproxy\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01measi_tools\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mload_s2l2a\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_s2l2a_with_offset\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdistributed\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m progress\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'easi_tools'"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import importlib\n",
+    "import new_import_ODC  \n",
+    "\n",
+    "importlib.reload(new_import_ODC)\n",
+    "\n",
+    "from new_import_ODC import *\n",
+    "\n",
+    "print(\"✅ All modules loaded successfully\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d824dc4f-994b-4d1c-8d24-ce6674da141c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ AWS credentials loaded from environment variables\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/x79/miniconda/envs/env_01/lib/python3.10/site-packages/distributed/node.py:187: UserWarning: Port 8787 is already in use.\n",
+      "Perhaps you already have a cluster running?\n",
+      "Hosting the HTTP server on port 41709 instead\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Dask cluster initialized\n",
+      "   Cluster: LocalCluster(9f2167a3, 'tcp://127.0.0.1:41233', workers=4, threads=24, memory=31.26 GiB)\n",
+      "✅ Datacube connected (metadata only)\n",
+      "\n",
+      "======================================================================\n",
+      "CPU times: user 4.98 s, sys: 831 ms, total: 5.81 s\n",
+      "Wall time: 7.69 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "print(\"✅ AWS credentials loaded from environment variables\")\n",
+    "\n",
+    "# Cấu hình Dask local\n",
+    "from dask.distributed import Client, LocalCluster\n",
+    "\n",
+    "cluster = LocalCluster(n_workers=4)\n",
+    "client = Client(cluster)\n",
+    "print(\"✅ Dask cluster initialized\")\n",
+    "print(f\"   Cluster: {cluster}\")\n",
+    "\n",
+    "# Khai báo Datacube (chỉ để lấy metadata, không dùng load())\n",
+    "import datacube\n",
+    "try:\n",
+    "    dc = datacube.Datacube()\n",
+    "    print(\"✅ Datacube connected (metadata only)\")\n",
+    "except Exception as e:\n",
+    "    print(f\"⚠️  Datacube connection not critical: {e}\")\n",
+    "    dc = None\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e113730",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "GETTING SENTINEL-2 SCENE METADATA\n",
+      "======================================================================\n",
+      "\n",
+      "[1] Loading metadata from datacube...\n",
+      "    ✅ Found 40 scenes\n",
+      "\n",
+      "[2] Selected scene: S2A_48PWR_20231226_0_L2A\n",
+      "    Date: 2023-12-26 03:35:26.919000+00:00\n",
+      "\n",
+      "[3] Available bands:\n",
+      "    - nir: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - red: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - scl: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - blue: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - green: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - nir08: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - nir09: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - swir16: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - swir22: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - coastal: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - rededge1: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - rededge2: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "    - rededge3: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/48/P/WR/20\n",
+      "======================================================================\n",
+      "CPU times: user 3.34 s, sys: 76.3 ms, total: 3.41 s\n",
+      "Wall time: 3.23 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 🔧 Get Sentinel-2 scene metadata from datacube\n",
+    "print(\"=\"*70)\n",
+    "print(\"GETTING SENTINEL-2 SCENE METADATA\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "date_range = (\"2023-03-01\", \"2023-12-31\")\n",
+    "longtitude_range = (105.5, 106.4)\n",
+    "latitude_range = (9.2, 10.0)\n",
+    "\n",
+    "try:\n",
+    "    print(f\"\\n[1] Loading metadata from datacube...\")\n",
+    "    datasets = list(dc.find_datasets(product='s2_l2a', time=date_range))\n",
+    "    print(f\"    ✅ Found {len(datasets)} scenes\")\n",
+    "    \n",
+    "    if datasets:\n",
+    "        selected = datasets[0]\n",
+    "        print(f\"\\n[2] Selected scene: {selected.metadata.label}\")\n",
+    "        scene_datetime = selected.time.begin if hasattr(selected.time, 'begin') else selected.time\n",
+    "        print(f\"    Date: {scene_datetime}\")\n",
+    "        \n",
+    "        # Display measurement paths\n",
+    "        print(f\"\\n[3] Available bands:\")\n",
+    "        for name, measurement in selected.measurements.items():\n",
+    "            print(f\"    - {name}: {measurement['path'][:80]}\")\n",
+    "            \n",
+    "except Exception as e:\n",
+    "    print(f\"❌ Error: {e}\")\n",
+    "    import traceback\n",
+    "    traceback.print_exc()\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3cd69645",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "CHECKING FOR CACHED DATASET\n",
+      "======================================================================\n",
+      "\n",
+      "⏳ Cache file not found: dataset_cache/sentinel2_timeseries_40scenes.nc\n",
+      "   Will download from S3 and save cache\n",
+      "   (Next run will use cache automatically)\n",
+      "======================================================================\n",
+      "CPU times: user 4.36 ms, sys: 3.7 ms, total: 8.06 ms\n",
+      "Wall time: 7.22 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 🔍 CHECK IF DATASET CACHE EXISTS (Skip download if available)\n",
+    "print(\"=\"*70)\n",
+    "print(\"CHECKING FOR CACHED DATASET\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "import os\n",
+    "import xarray as xr\n",
+    "\n",
+    "cache_dir = \"dataset_cache\"\n",
+    "cache_file = f\"{cache_dir}/sentinel2_timeseries_40scenes.nc\"\n",
+    "\n",
+    "use_cache = False\n",
+    "\n",
+    "if os.path.exists(cache_file):\n",
+    "    print(f\"\\n✅ Cache file found: {cache_file}\")\n",
+    "    \n",
+    "    # Get file info\n",
+    "    file_size_gb = os.path.getsize(cache_file) / (1024**3)\n",
+    "    print(f\"   File size: {file_size_gb:.2f} GB\")\n",
+    "    \n",
+    "    # Try to load\n",
+    "    try:\n",
+    "        print(f\"\\n🔄 Loading dataset from cache...\")\n",
+    "        data = xr.open_dataset(cache_file)\n",
+    "        \n",
+    "        print(f\"✅ Dataset loaded from cache!\")\n",
+    "        print(f\"   Total scenes: {len(data['time'])}\")\n",
+    "        print(f\"   Variables: {len(data.data_vars)}\")\n",
+    "        print(f\"   Dimensions: {dict(data.dims)}\")\n",
+    "        print(f\"\\n   ⏭️  Skipping S3 download (using cached data)\")\n",
+    "        \n",
+    "        use_cache = True\n",
+    "        \n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Error loading cache: {e}\")\n",
+    "        print(f\"   Will download fresh data from S3\")\n",
+    "        use_cache = False\n",
+    "else:\n",
+    "    print(f\"\\n⏳ Cache file not found: {cache_file}\")\n",
+    "    print(f\"   Will download from S3 and save cache\")\n",
+    "    print(f\"   (Next run will use cache automatically)\")\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "435f9f78-a9a4-4226-86ca-d4bec42d454e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "LOADING SENTINEL-2 DATA FROM S3 COGs (RASTERIO) - OPTIMAL ACCURACY\n",
+      "======================================================================\n",
+      "\n",
+      "📥 Downloading from S3...\n",
+      "\n",
+      "📦 Found 40 available scenes\n",
+      "   Date range: 2023-03-01 to 2023-12-31\n",
+      "\n",
+      "[LOADING] Loading ALL 40 scenes with ALL available bands...\n",
+      "   (Keeping NATIVE resolution - NO upsampling/magnification)\n",
+      "   Available bands: ['nir', 'red', 'scl', 'blue', 'green', 'nir08', 'nir09', 'swir16', 'swir22', 'coastal', 'rededge1', 'rededge2', 'rededge3']\n",
+      "\n",
+      "   [ 1/1] S2A_48PWR_20231226_0_L2A (2023-12-26)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      ✅ 13 bands loaded\n",
+      "\n",
+      "✅ Successfully loaded 1 scenes!\n",
+      "\n",
+      "[RESOLUTION NORMALIZATION] Aligning all bands to native resolution (NO magnification)...\n",
+      "   Reference resolution: 10980×10980 pixels (native nir)\n",
+      "      Resampling scl: 5490×5490 → 10980×10980\n",
+      "      Resampling nir08: 5490×5490 → 10980×10980\n",
+      "      Resampling nir09: 1830×1830 → 10980×10980\n",
+      "      Resampling swir16: 5490×5490 → 10980×10980\n",
+      "      Resampling swir22: 5490×5490 → 10980×10980\n",
+      "      Resampling coastal: 1830×1830 → 10980×10980\n",
+      "      Resampling rededge1: 5490×5490 → 10980×10980\n",
+      "      Resampling rededge2: 5490×5490 → 10980×10980\n",
+      "      Resampling rededge3: 5490×5490 → 10980×10980\n",
+      "✅ Resolution normalization complete! (9 bands resampled)\n",
+      "\n",
+      "[SPECTRAL INDICES] Calculating spectral indices for each scene...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<timed exec>:169: RuntimeWarning: divide by zero encountered in divide\n",
+      "<timed exec>:169: RuntimeWarning: invalid value encountered in divide\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Calculated 2 spectral indices per scene\n",
+      "\n",
+      "[STACKING] Stacking all 1 scenes to create time-series...\n",
+      "\n",
+      "[TEMPORAL FEATURES] Computing temporal features from time-series...\n",
+      "✅ Added 6 temporal/aggregate features\n",
+      "\n",
+      "[CACHE] Saving dataset to cache...\n",
+      "✅ Dataset saved to cache: dataset_cache/sentinel2_timeseries_40scenes.nc\n",
+      "   Cache size: 6.40 GB\n",
+      "\n",
+      "✅ OPTIMAL Dataset with native resolution + temporal features created!\n",
+      "   ======================================================================\n",
+      "   🎬 Total scenes (time steps): 1\n",
+      "   📊 Total bands/variables: 21\n",
+      "   🖼️  Spatial size: 10980 × 10980 pixels (NATIVE resolution)\n",
+      "   📏 Native resolution: 10m (Sentinel-2 L2A)\n",
+      "   ⏰ Temporal range: 2023-12-26 to 2023-12-26\n",
+      "❌ Error: name 'notebook_utils' is not defined\n",
+      "======================================================================\n",
+      "CPU times: user 9min, sys: 4min 16s, total: 13min 16s\n",
+      "Wall time: 16min 42s\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"<timed exec>\", line 262, in <module>\n",
+      "NameError: name 'notebook_utils' is not defined\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 💾 LOAD SENTINEL-2 DATA DIRECTLY FROM S3 COGS USING RASTERIO - WITH TEMPORAL FEATURES\n",
+    "print(\"=\"*70)\n",
+    "print(\"LOADING SENTINEL-2 DATA FROM S3 COGs (RASTERIO) - OPTIMAL ACCURACY\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "try:\n",
+    "    import rasterio\n",
+    "    import xarray as xr\n",
+    "    import numpy as np\n",
+    "    from scipy import ndimage\n",
+    "    \n",
+    "    # ===== CHECK IF SHOULD SKIP DOWNLOAD =====\n",
+    "    if use_cache and data is not None:\n",
+    "        print(f\"\\n✅ Using cached dataset - skipping download!\")\n",
+    "        print(f\"   Variables: {len(data.data_vars)}\")\n",
+    "        print(f\"   Shape: {data.dims}\")\n",
+    "        display(data)\n",
+    "    \n",
+    "    else:\n",
+    "        # ===== DOWNLOAD FROM S3 =====\n",
+    "        print(f\"\\n📥 Downloading from S3...\")\n",
+    "        \n",
+    "        # Get all scenes from datacube metadata\n",
+    "        datasets = list(dc.find_datasets(\n",
+    "            product='s2_l2a',\n",
+    "            time=date_range\n",
+    "        ))\n",
+    "        \n",
+    "        if not datasets:\n",
+    "            raise ValueError(\"No datasets found for date range\")\n",
+    "        \n",
+    "        print(f\"\\n📦 Found {len(datasets)} available scenes\")\n",
+    "        print(f\"   Date range: {date_range[0]} to {date_range[1]}\")\n",
+    "        \n",
+    "        # ===== LOAD ALL SCENES WITH ALL AVAILABLE BANDS (NO MAGNIFICATION) =====\n",
+    "        print(f\"\\n[LOADING] Loading ALL {len(datasets)} scenes with ALL available bands...\")\n",
+    "        print(f\"   (Keeping NATIVE resolution - NO upsampling/magnification)\")\n",
+    "        \n",
+    "        # num_scenes = len(datasets)  # Load ALL scenes\n",
+    "        num_scenes = 1 # Load ALL scenes\n",
+    "        all_data_dict = {}\n",
+    "        failed_scenes = []\n",
+    "        scene_dates = []\n",
+    "        \n",
+    "        # Discover all available bands from first scene\n",
+    "        first_scene = datasets[0]\n",
+    "        all_available_bands = list(first_scene.measurements.keys())\n",
+    "        print(f\"   Available bands: {all_available_bands}\")\n",
+    "        \n",
+    "        for scene_idx in range(num_scenes):\n",
+    "            selected = datasets[scene_idx]\n",
+    "            scene_label = selected.metadata.label\n",
+    "            scene_datetime = selected.time.begin if hasattr(selected.time, 'begin') else selected.time\n",
+    "            scene_dates.append(scene_datetime)\n",
+    "            \n",
+    "            # Print progress every 5 scenes\n",
+    "            if scene_idx % 5 == 0 or scene_idx == 0 or scene_idx == num_scenes - 1:\n",
+    "                print(f\"\\n   [{scene_idx + 1:2d}/{num_scenes}] {scene_label} ({scene_datetime.date()})\")\n",
+    "            \n",
+    "            # Load ALL available bands from S3 COGs\n",
+    "            scene_data_dict = {}\n",
+    "            \n",
+    "            for band_name in all_available_bands:\n",
+    "                if band_name in selected.measurements:\n",
+    "                    band_path = selected.measurements[band_name]['path']\n",
+    "                    \n",
+    "                    try:\n",
+    "                        with rasterio.open(band_path) as src:\n",
+    "                            data_band = src.read(1)\n",
+    "                            scene_data_dict[band_name] = data_band\n",
+    "                    except Exception as e:\n",
+    "                        if scene_idx % 5 == 0:\n",
+    "                            print(f\"      ⚠️ Error loading {band_name}: {str(e)[:30]}\")\n",
+    "                        failed_scenes.append((scene_idx, scene_label, band_name, str(e)))\n",
+    "            \n",
+    "            if scene_data_dict:\n",
+    "                all_data_dict[scene_idx] = scene_data_dict\n",
+    "                if scene_idx % 5 == 0 or scene_idx == num_scenes - 1:\n",
+    "                    print(f\"      ✅ {len(scene_data_dict)} bands loaded\")\n",
+    "            else:\n",
+    "                failed_scenes.append((scene_idx, scene_label, \"all\", \"No bands loaded\"))\n",
+    "        \n",
+    "        if not all_data_dict:\n",
+    "            raise ValueError(\"Could not load any bands from any scene\")\n",
+    "        \n",
+    "        print(f\"\\n✅ Successfully loaded {len(all_data_dict)} scenes!\")\n",
+    "        if failed_scenes:\n",
+    "            print(f\"⚠️  Failed to load {len(failed_scenes)} band instances (will be skipped)\")\n",
+    "        \n",
+    "        # ===== NORMALIZE RESOLUTION (No upsampling - just match to highest) =====\n",
+    "        print(f\"\\n[RESOLUTION NORMALIZATION] Aligning all bands to native resolution (NO magnification)...\")\n",
+    "        \n",
+    "        # Find max resolution\n",
+    "        ref_resolution = None\n",
+    "        max_size = 0\n",
+    "        max_band = None\n",
+    "        \n",
+    "        for scene_idx in all_data_dict.keys():\n",
+    "            for band_name, data_band in all_data_dict[scene_idx].items():\n",
+    "                size = data_band.shape[0]\n",
+    "                if size > max_size:\n",
+    "                    max_size = size\n",
+    "                    ref_resolution = size\n",
+    "                    max_band = band_name\n",
+    "        \n",
+    "        print(f\"   Reference resolution: {max_size}×{max_size} pixels (native {max_band})\")\n",
+    "        \n",
+    "        # Resample all bands to match reference resolution (both up and down)\n",
+    "        resampled_count = 0\n",
+    "        for scene_idx in all_data_dict.keys():\n",
+    "            for band_name in list(all_data_dict[scene_idx].keys()):\n",
+    "                band_data_arr = all_data_dict[scene_idx][band_name]\n",
+    "                current_size = band_data_arr.shape[0]\n",
+    "                \n",
+    "                if current_size != ref_resolution:\n",
+    "                    scale_factor = ref_resolution / current_size\n",
+    "                    \n",
+    "                    # Resample to match reference resolution (both up and down)\n",
+    "                    if band_name == 'scl':\n",
+    "                        resampled_data = ndimage.zoom(band_data_arr, scale_factor, order=0)\n",
+    "                    else:\n",
+    "                        resampled_data = ndimage.zoom(band_data_arr, scale_factor, order=1)\n",
+    "                    \n",
+    "                    all_data_dict[scene_idx][band_name] = resampled_data\n",
+    "                    new_size = resampled_data.shape[0]\n",
+    "                    if scene_idx == 0:  # Print for first scene only to reduce clutter\n",
+    "                        print(f\"      Resampling {band_name}: {current_size}×{current_size} → {new_size}×{new_size}\")\n",
+    "                    resampled_count += 1\n",
+    "        \n",
+    "        print(f\"✅ Resolution normalization complete! ({resampled_count} bands resampled)\")\n",
+    "        \n",
+    "        # ===== CALCULATE SPECTRAL INDICES FOR EACH SCENE =====\n",
+    "        print(f\"\\n[SPECTRAL INDICES] Calculating spectral indices for each scene...\")\n",
+    "        \n",
+    "        indices_count = 0\n",
+    "        for scene_idx in all_data_dict.keys():\n",
+    "            scene_data = all_data_dict[scene_idx]\n",
+    "            \n",
+    "            try:\n",
+    "                # NDVI: (NIR - Red) / (NIR + Red)\n",
+    "                if 'nir' in scene_data and 'red' in scene_data:\n",
+    "                    nir = scene_data['nir'].astype(float)\n",
+    "                    red = scene_data['red'].astype(float)\n",
+    "                    ndvi = (nir - red) / (nir + red + 1e-8)\n",
+    "                    scene_data['ndvi'] = ndvi.astype(np.float32)\n",
+    "                    indices_count += 1\n",
+    "                \n",
+    "                # NDBI: (SWIR - NIR) / (SWIR + NIR)\n",
+    "                if 'b11' in scene_data and 'nir' in scene_data:\n",
+    "                    swir = scene_data['b11'].astype(float)\n",
+    "                    nir = scene_data['nir'].astype(float)\n",
+    "                    ndbi = (swir - nir) / (swir + nir + 1e-8)\n",
+    "                    scene_data['ndbi'] = ndbi.astype(np.float32)\n",
+    "                    indices_count += 1\n",
+    "                \n",
+    "                # NDWI: (NIR - SWIR) / (NIR + SWIR)\n",
+    "                if 'nir' in scene_data and 'b11' in scene_data:\n",
+    "                    nir = scene_data['nir'].astype(float)\n",
+    "                    swir = scene_data['b11'].astype(float)\n",
+    "                    ndwi = (nir - swir) / (nir + swir + 1e-8)\n",
+    "                    scene_data['ndwi'] = ndwi.astype(np.float32)\n",
+    "                    indices_count += 1\n",
+    "                \n",
+    "                # EVI: Enhanced Vegetation Index\n",
+    "                if 'nir' in scene_data and 'red' in scene_data and 'blue' in scene_data:\n",
+    "                    nir = scene_data['nir'].astype(float)\n",
+    "                    red = scene_data['red'].astype(float)\n",
+    "                    blue = scene_data['blue'].astype(float)\n",
+    "                    evi = 2.5 * (nir - red) / (nir + 6*red - 7.5*blue + 1)\n",
+    "                    scene_data['evi'] = evi.astype(np.float32)\n",
+    "                    indices_count += 1\n",
+    "                    \n",
+    "            except Exception as e:\n",
+    "                pass\n",
+    "        \n",
+    "        print(f\"✅ Calculated {indices_count} spectral indices per scene\")\n",
+    "        \n",
+    "        # ===== STACK SCENES ALONG TIME DIMENSION =====\n",
+    "        print(f\"\\n[STACKING] Stacking all {len(all_data_dict)} scenes to create time-series...\")\n",
+    "        \n",
+    "        data_vars = {}\n",
+    "        band_names = list(all_data_dict[0].keys())\n",
+    "        \n",
+    "        for band_name in band_names:\n",
+    "            band_data_list = []\n",
+    "            for scene_idx in sorted(all_data_dict.keys()):\n",
+    "                if band_name in all_data_dict[scene_idx]:\n",
+    "                    band_data_list.append(all_data_dict[scene_idx][band_name])\n",
+    "            \n",
+    "            if band_data_list:\n",
+    "                stacked = np.stack(band_data_list, axis=0)\n",
+    "                data_vars[band_name] = (['time', 'y', 'x'], stacked)\n",
+    "        \n",
+    "        # Create xarray Dataset with time dimension\n",
+    "        first_band_data = list(all_data_dict[0].values())[0]\n",
+    "        y_size, x_size = first_band_data.shape\n",
+    "        \n",
+    "        data = xr.Dataset(\n",
+    "            data_vars,\n",
+    "            coords={\n",
+    "                'time': np.arange(len(all_data_dict)),\n",
+    "                'x': np.arange(x_size),\n",
+    "                'y': np.arange(y_size)\n",
+    "            }\n",
+    "        )\n",
+    "        \n",
+    "        # ===== CALCULATE TEMPORAL FEATURES FOR ACCURACY =====\n",
+    "        print(f\"\\n[TEMPORAL FEATURES] Computing temporal features from time-series...\")\n",
+    "        \n",
+    "        temporal_features_added = 0\n",
+    "        \n",
+    "        # For NDVI: temporal statistics\n",
+    "        if 'ndvi' in data.data_vars:\n",
+    "            ndvi_ts = data['ndvi']\n",
+    "            \n",
+    "            # Min NDVI (vegetation stress indicator)\n",
+    "            data['ndvi_min'] = ndvi_ts.min(dim='time')\n",
+    "            temporal_features_added += 1\n",
+    "            \n",
+    "            # Max NDVI (peak vegetation)\n",
+    "            data['ndvi_max'] = ndvi_ts.max(dim='time')\n",
+    "            temporal_features_added += 1\n",
+    "            \n",
+    "            # Mean NDVI\n",
+    "            data['ndvi_mean'] = ndvi_ts.mean(dim='time')\n",
+    "            temporal_features_added += 1\n",
+    "            \n",
+    "            # NDVI range (variability)\n",
+    "            data['ndvi_range'] = data['ndvi_max'] - data['ndvi_min']\n",
+    "            temporal_features_added += 1\n",
+    "            \n",
+    "            # NDVI std (temporal consistency)\n",
+    "            data['ndvi_std'] = ndvi_ts.std(dim='time')\n",
+    "            temporal_features_added += 1\n",
+    "        \n",
+    "        # For all indices: mean values (aggregate features)\n",
+    "        for band_name in ['ndbi', 'ndwi', 'evi']:\n",
+    "            if band_name in data.data_vars:\n",
+    "                band_ts = data[band_name]\n",
+    "                data[f'{band_name}_mean'] = band_ts.mean(dim='time')\n",
+    "                temporal_features_added += 1\n",
+    "        \n",
+    "        print(f\"✅ Added {temporal_features_added} temporal/aggregate features\")\n",
+    "        \n",
+    "        # ===== SAVE TO CACHE =====\n",
+    "        print(f\"\\n[CACHE] Saving dataset to cache...\")\n",
+    "        try:\n",
+    "            data.to_netcdf(cache_file, engine='netcdf4')\n",
+    "            cache_size = os.path.getsize(cache_file) / (1024**3)\n",
+    "            print(f\"✅ Dataset saved to cache: {cache_file}\")\n",
+    "            print(f\"   Cache size: {cache_size:.2f} GB\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"⚠️  Error saving cache: {e}\")\n",
+    "        \n",
+    "        print(f\"\\n✅ OPTIMAL Dataset with native resolution + temporal features created!\")\n",
+    "        print(f\"   {'='*70}\")\n",
+    "        print(f\"   🎬 Total scenes (time steps): {len(all_data_dict)}\")\n",
+    "        print(f\"   📊 Total bands/variables: {len(data.data_vars)}\")\n",
+    "        print(f\"   🖼️  Spatial size: {x_size} × {y_size} pixels (NATIVE resolution)\")\n",
+    "        print(f\"   📏 Native resolution: 10m (Sentinel-2 L2A)\")\n",
+    "        print(f\"   ⏰ Temporal range: {scene_dates[0].date()} to {scene_dates[-1].date()}\")\n",
+    "        print(f\"   💾 Total dataset size: {notebook_utils.xarray_object_size(data)}\")\n",
+    "        print(f\"   💿 Cached at: {cache_file}\")\n",
+    "        print(f\"   {'='*70}\")\n",
+    "        \n",
+    "        print(f\"\\n   Dataset dimensions:\")\n",
+    "        for dim, size in data.dims.items():\n",
+    "            print(f\"     {dim}: {size}\")\n",
+    "        \n",
+    "        print(f\"\\n   Variables ({len(data.data_vars)}):\")\n",
+    "        spatial_vars = []\n",
+    "        temporal_vars = []\n",
+    "        for var_name in sorted(data.data_vars):\n",
+    "            if len(data[var_name].shape) == 3:\n",
+    "                spatial_vars.append(f\"{var_name} {data[var_name].shape}\")\n",
+    "            else:\n",
+    "                temporal_vars.append(f\"{var_name} {data[var_name].shape}\")\n",
+    "        \n",
+    "        print(f\"   Spatial time-series ({len(spatial_vars)}):\")\n",
+    "        for v in spatial_vars:\n",
+    "            print(f\"     - {v}\")\n",
+    "        print(f\"   Temporal aggregates ({len(temporal_vars)}):\")\n",
+    "        for v in temporal_vars:\n",
+    "            print(f\"     - {v}\")\n",
+    "        \n",
+    "        print(f\"   {'='*70}\")\n",
+    "        \n",
+    "        display(data)\n",
+    "    \n",
+    "    # ===== EXTRACT NDVI FOR TRAINING =====\n",
+    "    print(f\"\\n[NDVI EXTRACTION] Extracting NDVI for model training...\")\n",
+    "    if 'ndvi_mean' in data.data_vars:\n",
+    "        # Use mean NDVI across time\n",
+    "        ndvi = data['ndvi_mean']\n",
+    "        print(f\"✅ NDVI extracted (mean across time)\")\n",
+    "        print(f\"   Shape: {ndvi.shape}\")\n",
+    "    elif 'ndvi' in data.data_vars:\n",
+    "        # Use first time step if mean not available\n",
+    "        ndvi = data['ndvi'].isel(time=0)\n",
+    "        print(f\"✅ NDVI extracted (first time step)\")\n",
+    "        print(f\"   Shape: {ndvi.shape}\")\n",
+    "    else:\n",
+    "        print(f\"❌ NDVI not found in dataset\")\n",
+    "        ndvi = None\n",
+    "    \n",
+    "except Exception as e:\n",
+    "    print(f\"❌ Error: {e}\")\n",
+    "    import traceback\n",
+    "    traceback.print_exc()\n",
+    "    data = None\n",
+    "    ndvi = None\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "TRAINING DATA SETUP\n",
+      "======================================================================\n",
+      "\n",
+      "[1] Loading training data: train/ST_training data_updated_1130points_new.shp\n",
+      "    ❌ Error: name 'load_train_data' is not defined\n",
+      "\n",
+      "[2] Label mapping:\n",
+      "    0: Lua tom\n",
+      "    1: Lua\n",
+      "    2: CHN\n",
+      "    3: CLN\n",
+      "    4: TS\n",
+      "    5: Song\n",
+      "    6: Dat xay dung\n",
+      "    7: Rung\n",
+      "\n",
+      "======================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 🎯 LOAD TRAINING DATA & EXTRACT FEATURES\n",
+    "print(\"=\"*70)\n",
+    "print(\"TRAINING DATA SETUP\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "# Load training points\n",
+    "train_path = \"train/ST_training data_updated_1130points_new.shp\"\n",
+    "print(f\"\\n[1] Loading training data: {train_path}\")\n",
+    "\n",
+    "try:\n",
+    "    train = load_train_data(train_path)\n",
+    "    print(f\"    ✅ Loaded {len(train)} training points\")\n",
+    "    print(f\"    Columns: {list(train.columns)}\")\n",
+    "    train.head()\n",
+    "except Exception as e:\n",
+    "    print(f\"    ❌ Error: {e}\")\n",
+    "    train = None\n",
+    "\n",
+    "# Label mapping\n",
+    "label_mapping = {\n",
+    "    \"Lua tom\": \"0\",\n",
+    "    \"Lua\": \"1\",\n",
+    "    \"CHN\": \"2\",\n",
+    "    \"CLN\": \"3\",\n",
+    "    \"TS\": \"4\",\n",
+    "    \"Song\": \"5\",\n",
+    "    \"Dat xay dung\": \"6\",\n",
+    "    \"Rung\": \"7\",\n",
+    "}\n",
+    "\n",
+    "print(f\"\\n[2] Label mapping:\")\n",
+    "for label, code in label_mapping.items():\n",
+    "    print(f\"    {code}: {label}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2e955884-d4af-422d-a8e6-d436199540e0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "MODEL TRAINING\n",
+      "======================================================================\n",
+      "❌ Missing training data or NDVI\n",
+      "======================================================================\n",
+      "CPU times: user 700 μs, sys: 0 ns, total: 700 μs\n",
+      "Wall time: 638 μs\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# 🤖 RANDOM FOREST MODEL TRAINING\n",
+    "print(\"=\"*70)\n",
+    "print(\"MODEL TRAINING\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "if train is not None and ndvi is not None:\n",
+    "    print(\"\\n[1] Extracting features from NDVI...\")\n",
+    "    try:\n",
+    "        # Extract NDVI values at training point locations\n",
+    "        X = []\n",
+    "        y = []\n",
+    "        \n",
+    "        for idx, point in train.iterrows():\n",
+    "            try:\n",
+    "                # Get NDVI value at point location (nearest neighbor)\n",
+    "                ndvi_val = float(ndvi.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values)\n",
+    "                label = label_mapping[point.Hientrang]\n",
+    "                \n",
+    "                X.append([ndvi_val])\n",
+    "                y.append(int(label))\n",
+    "            except Exception as e:\n",
+    "                print(f\"    ⚠️  Point {idx}: {e}\")\n",
+    "        \n",
+    "        if len(X) > 0:\n",
+    "            X = np.array(X)\n",
+    "            y = np.array(y)\n",
+    "            print(f\"    ✅ Extracted {len(X)} samples\")\n",
+    "            \n",
+    "            # Split data\n",
+    "            print(f\"\\n[2] Splitting data (80-20)...\")\n",
+    "            from sklearn.model_selection import train_test_split\n",
+    "            X_train, X_test, y_train, y_test = train_test_split(\n",
+    "                X, y, test_size=0.2, random_state=42\n",
+    "            )\n",
+    "            print(f\"    Train: {len(X_train)}, Test: {len(X_test)}\")\n",
+    "            \n",
+    "            # Train model\n",
+    "            print(f\"\\n[3] Training Random Forest...\")\n",
+    "            from sklearn.ensemble import RandomForestClassifier\n",
+    "            from sklearn.metrics import accuracy_score\n",
+    "            \n",
+    "            model = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)\n",
+    "            model.fit(X_train, y_train)\n",
+    "            \n",
+    "            # Evaluate\n",
+    "            y_pred = model.predict(X_test)\n",
+    "            accuracy = accuracy_score(y_test, y_pred)\n",
+    "            print(f\"    ✅ Model trained!\")\n",
+    "            print(f\"    Accuracy: {accuracy*100:.2f}%\")\n",
+    "            \n",
+    "        else:\n",
+    "            print(f\"    ❌ No samples extracted\")\n",
+    "            model = None\n",
+    "            \n",
+    "    except Exception as e:\n",
+    "        print(f\"    ❌ Error: {e}\")\n",
+    "        import traceback\n",
+    "        traceback.print_exc()\n",
+    "        model = None\n",
+    "else:\n",
+    "    print(\"❌ Missing training data or NDVI\")\n",
+    "    model = None\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f1a14379-ed6e-4897-9ca4-2669743fab40",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "MODEL SAVING\n",
+      "======================================================================\n",
+      "❌ No model to save\n",
+      "======================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 💾 SAVE MODEL\n",
+    "print(\"=\"*70)\n",
+    "print(\"MODEL SAVING\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "if model is not None:\n",
+    "    print(\"\\n🔄 Saving trained model...\")\n",
+    "    try:\n",
+    "        save_model(\"model_rasterio.joblib\", model)\n",
+    "        print(\"✅ Model saved to model_train/model_rasterio.joblib\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Error saving model: {e}\")\n",
+    "else:\n",
+    "    print(\"❌ No model to save\")\n",
+    "\n",
+    "print(\"=\"*70)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33dd516d-9824-499e-96b9-5cd9224c194c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "======================================================================\n",
+      "CLEANUP\n",
+      "======================================================================\n",
+      "\n",
+      "🔄 Closing Dask client and cluster...\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Cleanup complete\n",
+      "\n",
+      "======================================================================\n",
+      "✅ PIPELINE COMPLETE\n",
+      "======================================================================\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
+      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
+      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
+      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
+     ]
+    }
+   ],
+   "source": [
+    "# 🛑 CLEANUP\n",
+    "print(\"=\"*70)\n",
+    "print(\"CLEANUP\")\n",
+    "print(\"=\"*70)\n",
+    "\n",
+    "print(\"\\n🔄 Closing Dask client and cluster...\")\n",
+    "try:\n",
+    "    client.close()\n",
+    "    cluster.close()\n",
+    "    print(\"✅ Cleanup complete\")\n",
+    "except Exception as e:\n",
+    "    print(f\"⚠️  Error during cleanup: {e}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*70)\n",
+    "print(\"✅ PIPELINE COMPLETE\")\n",
+    "print(\"=\"*70)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env_01",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

COLAB_FRP_DEMO_GUIDE.md

@@ -0,0 +1,445 @@
+# Demo Website trên Google Colab với FRP
+
+Hướng dẫn này giúp bạn chạy toàn bộ hệ thống **remote-sensing** trên Google Colab  
+và expose ra internet qua **FRP (Fast Reverse Proxy)** — không cần ngrok, URL cố định, không giới hạn session.
+
+---
+
+## Yêu cầu
+
+| Thành phần | Mô tả |
+|---|---|
+| **Google Colab** | Tài khoản Google thông thường (free tier là đủ) |
+| **Google Drive** | Dùng để lưu project và models |
+| **1 VPS có IP public** | Chạy `frps` server — VPS $3–5/tháng là đủ |
+| **Port mở trên VPS** | 7000 (FRP control) + 8080 (web traffic) |
+
+---
+
+## Phần 1 — Chuẩn bị VPS (chạy 1 lần, giữ mãi)
+
+### Bước 1.1 — Download FRP lên VPS
+
+SSH vào VPS rồi chạy:
+
+```bash
+wget https://github.com/fatedier/frp/releases/download/v0.61.1/frp_0.61.1_linux_amd64.tar.gz
+tar -xzf frp_0.61.1_linux_amd64.tar.gz
+cd frp_0.61.1_linux_amd64
+```
+
+### Bước 1.2 — Tạo file cấu hình `frps.toml`
+
+```bash
+cat > frps.toml << 'EOF'
+bindPort = 7000
+auth.token = "your_secret_token_here"
+
+# Dashboard để theo dõi kết nối (tuỳ chọn)
+webServer.port = 7500
+webServer.user = "admin"
+webServer.password = "admin123"
+EOF
+```
+
+> ⚠️ Đặt `auth.token` thành chuỗi bí mật của bạn, ví dụ: `"rs_demo_2026_abc123"`. Phải giống với phía Colab.
+
+### Bước 1.3 — Chạy frps
+
+```bash
+# Test chạy foreground (Ctrl+C để dừng)
+./frps -c frps.toml
+
+# Chạy nền (production)
+nohup ./frps -c frps.toml > frps.log 2>&1 &
+
+# Kiểm tra đang chạy
+ps aux | grep frps
+```
+
+### Bước 1.4 — Mở firewall VPS
+
+```bash
+# Ubuntu/Debian
+ufw allow 7000    # FRP control port
+ufw allow 8080    # Web traffic port
+ufw allow 7500    # Dashboard (tuỳ chọn)
+ufw reload
+
+# CentOS/RHEL
+firewall-cmd --permanent --add-port=7000/tcp
+firewall-cmd --permanent --add-port=8080/tcp
+firewall-cmd --reload
+```
+
+### Bước 1.5 — Kiểm tra frps hoạt động
+
+```bash
+# Xem log
+tail -f frps.log
+
+# Kết quả kỳ vọng:
+# [frps] frp service started, listen on 0.0.0.0:7000
+```
+
+---
+
+## Phần 2 — Chuẩn bị Google Drive
+
+### Bước 2.1 — Upload project lên Drive
+
+Cấu trúc thư mục trên Google Drive:
+
+```
+My Drive/
+└── remote-sensing/
+    ├── api_server.py
+    ├── train_module.py
+    ├── feature_extractor.py
+    ├── model_manager.py
+    ├── cloud_removal.py
+    ├── report_generator.py
+    ├── generate_previews.py
+    ├── vietnam_provinces.py
+    ├── vietnam_provinces_merged.py
+    ├── utils.py
+    ├── model_train/            ← copy toàn bộ models đã train
+    │   ├── *.joblib
+    │   └── *.json
+    ├── cloud_removal_model/    ← copy nếu dùng cloud removal DL
+    │   └── *.pth
+    ├── predictions/            ← để trống, Colab sẽ tạo output vào đây
+    ├── reports/                ← để trống
+    └── training_interface.html ← và tất cả *.html
+```
+
+> 💡 Upload nhanh nhất: zip toàn bộ folder `remote-sensing`, upload 1 file zip lên Drive, rồi giải nén bằng Colab.
+
+---
+
+## Phần 3 — Notebook Google Colab
+
+Tạo notebook mới tại [colab.google.com](https://colab.google.com) và paste từng cell sau.
+
+---
+
+### Cell 1 — Mount Drive và di chuyển vào project
+
+```python
+from google.colab import drive
+drive.mount('/content/drive')
+
+import os
+
+PROJECT_PATH = '/content/drive/MyDrive/remote-sensing'
+os.chdir(PROJECT_PATH)
+
+print(f"Working directory: {os.getcwd()}")
+print("Files:", os.listdir()[:10])
+```
+
+---
+
+### Cell 2 — Giải nén nếu upload dạng zip (tuỳ chọn)
+
+```python
+# Chỉ chạy nếu bạn upload file zip
+import zipfile
+
+ZIP_PATH = '/content/drive/MyDrive/remote-sensing.zip'
+EXTRACT_TO = '/content/drive/MyDrive/'
+
+if os.path.exists(ZIP_PATH):
+    with zipfile.ZipFile(ZIP_PATH, 'r') as z:
+        z.extractall(EXTRACT_TO)
+    print("✅ Extracted successfully")
+else:
+    print("⏭️  No zip found, skipping")
+```
+
+---
+
+### Cell 3 — Cài dependencies (chạy lần đầu, ~8–12 phút)
+
+```python
+print("Installing core API dependencies...")
+!pip install -q fastapi uvicorn pydantic
+
+print("Installing geospatial + ML dependencies...")
+!pip install -q \
+    numpy pandas xarray rasterio rioxarray geopandas shapely \
+    scikit-learn xgboost joblib \
+    matplotlib pillow markdown
+
+print("Installing Planetary Computer dependencies...")
+!pip install -q pystac-client planetary-computer odc-stac
+
+print("Installing PyTorch (GPU)...")
+!pip install -q torch torchvision \
+    --extra-index-url https://download.pytorch.org/whl/cu118
+
+print("✅ All dependencies installed")
+```
+
+---
+
+### Cell 4 — Kiểm tra GPU và môi trường
+
+```python
+import torch
+
+print(f"PyTorch version : {torch.__version__}")
+print(f"GPU available   : {torch.cuda.is_available()}")
+if torch.cuda.is_available():
+    print(f"GPU name        : {torch.cuda.get_device_name(0)}")
+
+import rasterio, xarray, geopandas
+print(f"rasterio        : {rasterio.__version__}")
+print(f"xarray          : {xarray.__version__}")
+print(f"geopandas       : {geopandas.__version__}")
+```
+
+---
+
+### Cell 5 — Download và cấu hình frpc
+
+```python
+import subprocess, os
+
+# Download frpc
+!wget -q https://github.com/fatedier/frp/releases/download/v0.61.1/frp_0.61.1_linux_amd64.tar.gz \
+    -O /tmp/frp.tar.gz
+!tar -xzf /tmp/frp.tar.gz -C /tmp/
+!chmod +x /tmp/frp_0.61.1_linux_amd64/frpc
+
+# ============================================================
+# ⚠️  SỬA 2 DÒNG NÀY TRƯỚC KHI CHẠY
+VPS_IP    = "123.456.789.000"          # IP public của VPS bạn
+FRP_TOKEN = "your_secret_token_here"   # Phải giống frps.toml trên VPS
+# ============================================================
+
+frpc_config = f"""
+serverAddr = "{VPS_IP}"
+serverPort = 7000
+auth.token = "{FRP_TOKEN}"
+
+[[proxies]]
+name = "remote-sensing-web"
+type = "tcp"
+localIP = "127.0.0.1"
+localPort = 8000
+remotePort = 8080
+"""
+
+with open('/tmp/frpc.toml', 'w') as f:
+    f.write(frpc_config)
+
+print(f"✅ frpc configured → VPS: {VPS_IP}:8080")
+```
+
+---
+
+### Cell 6 — Khởi động FastAPI server
+
+```python
+import subprocess, time, os
+
+os.chdir('/content/drive/MyDrive/remote-sensing')
+
+# Khởi động FastAPI
+server = subprocess.Popen(
+    ["uvicorn", "api_server:app",
+     "--host", "127.0.0.1",
+     "--port", "8000",
+     "--log-level", "warning"],
+    stdout=subprocess.PIPE,
+    stderr=subprocess.PIPE
+)
+
+time.sleep(4)
+
+# Kiểm tra server đã lên chưa
+if server.poll() is None:
+    print("✅ FastAPI server is running on port 8000")
+else:
+    out, err = server.communicate()
+    print("❌ Server failed to start:")
+    print(err.decode()[:1000])
+```
+
+---
+
+### Cell 7 — Khởi động frpc tunnel
+
+```python
+import subprocess, time
+
+frpc = subprocess.Popen(
+    ["/tmp/frp_0.61.1_linux_amd64/frpc", "-c", "/tmp/frpc.toml"],
+    stdout=subprocess.PIPE,
+    stderr=subprocess.PIPE
+)
+
+time.sleep(3)
+
+if frpc.poll() is None:
+    print("✅ FRP tunnel is active")
+    print("=" * 55)
+    print(f"🌐  Main site    : http://{VPS_IP}:8080")
+    print(f"📊  Dashboard    : http://{VPS_IP}:8080/dashboard")
+    print(f"🏋️   Training     : http://{VPS_IP}:8080/training")
+    print(f"🔮  Prediction   : http://{VPS_IP}:8080/prediction")
+    print(f"📦  Batch        : http://{VPS_IP}:8080/batch")
+    print(f"🌿  NDVI         : http://{VPS_IP}:8080/ndvi")
+    print(f"📈  Reports      : http://{VPS_IP}:8080/reports")
+    print("=" * 55)
+else:
+    out, err = frpc.communicate()
+    print("❌ FRP failed:")
+    print(err.decode()[:500])
+```
+
+---
+
+### Cell 8 — Kiểm tra toàn bộ hệ thống
+
+```python
+import urllib.request, json
+
+BASE = "http://127.0.0.1:8000"
+checks = [
+    ("/api/network/check",     "Network connectivity"),
+    ("/api/models/list",       "Model list"),
+    ("/api/provinces/list",    "Province list"),
+    ("/api/cloud-removal/methods", "Cloud removal methods"),
+]
+
+for path, label in checks:
+    try:
+        r = urllib.request.urlopen(BASE + path, timeout=5)
+        data = json.loads(r.read())
+        status = "✅"
+    except Exception as e:
+        data = str(e)
+        status = "❌"
+    print(f"{status}  {label:<30} → {str(data)[:80]}")
+```
+
+---
+
+### Cell 9 — Xem log nếu có lỗi (tuỳ chọn)
+
+```python
+# Xem stderr của FastAPI
+import select, sys
+
+if server.poll() is not None:
+    _, err = server.communicate()
+    print("FastAPI stderr:")
+    print(err.decode())
+else:
+    # Đọc log không block
+    import os
+    flags = os.O_RDONLY | os.O_NONBLOCK
+    try:
+        fd = server.stderr.fileno()
+        os.set_blocking(fd, False)
+        print(server.stderr.read(2000).decode())
+    except:
+        print("Server is running (no errors captured)")
+```
+
+---
+
+### Cell 10 — Dừng server khi xong demo
+
+```python
+import subprocess
+
+server.terminate()
+frpc.terminate()
+
+print("✅ FastAPI stopped")
+print("✅ FRP tunnel closed")
+```
+
+---
+
+## Phần 4 — Dùng domain thay vì IP (nâng cao)
+
+Nếu VPS có domain riêng, bạn có thể truy cập bằng URL đẹp hơn.
+
+### Sửa `frps.toml` trên VPS
+
+```toml
+bindPort = 7000
+auth.token = "your_secret_token_here"
+vhostHTTPPort = 80
+```
+
+### Sửa Cell 5 (`frpc.toml`) trên Colab
+
+```toml
+serverAddr = "your-vps.com"
+serverPort = 7000
+auth.token = "your_secret_token_here"
+
+[[proxies]]
+name = "remote-sensing-web"
+type = "http"
+localPort = 8000
+customDomains = ["demo.your-vps.com"]
+```
+
+### DNS — trỏ subdomain về VPS
+
+```
+demo.your-vps.com  →  A record  →  123.456.789.000
+```
+
+→ Truy cập: `http://demo.your-vps.com` (không cần `:8080`)
+
+---
+
+## Phần 5 — Giữ session Colab sống lâu hơn
+
+Colab tự disconnect sau ~90 phút idle. Để tránh:
+
+```javascript
+// Paste vào console trình duyệt (F12 → Console)
+function keepAlive() {
+    document.querySelector('#connect button')?.click();
+    console.log('keep-alive ping:', new Date().toLocaleTimeString());
+}
+setInterval(keepAlive, 60000);
+```
+
+Hoặc dùng **Colab Pro** ($10/tháng) để không bị giới hạn session.
+
+---
+
+## Phần 6 — Tóm tắt so sánh FRP vs ngrok
+
+| Tiêu chí | FRP (self-host) | ngrok (free) |
+|---|---|---|
+| URL cố định | ✅ Có | ❌ Đổi mỗi session |
+| Session timeout | ✅ Không giới hạn | ⚠️ 2 giờ |
+| Chi phí | Free (cần VPS) | Free tier có giới hạn |
+| Dữ liệu qua server bên thứ 3 | ❌ Không | ✅ Qua server ngrok |
+| Cần setup | ⚠️ Cần cài frps trên VPS | ✅ Chạy ngay |
+| Phù hợp | Demo dài hạn, production | Demo nhanh 1 lần |
+
+---
+
+## Checklist trước khi demo
+
+- [ ] VPS đang chạy `frps` và port 7000, 8080 đã mở
+- [ ] Project đã upload đầy đủ lên Google Drive (kể cả `model_train/`)
+- [ ] Đã điền đúng `VPS_IP` và `FRP_TOKEN` trong Cell 5
+- [ ] Cell 3 (cài deps) đã chạy thành công
+- [ ] Cell 8 (health check) cho thấy tất cả ✅
+- [ ] Truy cập `http://VPS_IP:8080/dashboard` từ trình duyệt → hiện trang
+
+---
+
+*Tạo ngày: 03/04/2026 — dự án remote-sensing Vietnam Land Classification*

NEW_FEATURES.md

@@ -0,0 +1,252 @@
+# 🎉 Chức năng mới đã được phục hồi
+
+## 📊 1. Dashboard Tổng Quan & Visualization
+
+Dashboard cung cấp giao diện trực quan để theo dõi hiệu suất hệ thống.
+
+### Truy cập Dashboard
+```
+http://localhost:8000/dashboard
+```
+
+### Tính năng
+- **📈 Tổng Quan**: Hiển thị thống kê tổng hợp
+  - Số models đã train
+  - Số predictions đã tạo
+  - Số reports đã generate
+  - Accuracy của model mới nhất
+
+- **📊 Accuracy Trends**: Biểu đồ theo dõi accuracy qua thời gian
+  - Line chart: Accuracy, Precision, Recall
+  - Bar chart: F1-Score comparison
+  - Export PNG/PDF
+
+- **📊 Class Distribution**: Phân bố các lớp đất
+  - Bar chart: Số lượng mẫu mỗi lớp
+  - Chọn model để xem
+  - Export PNG/PDF
+
+### API Endpoints
+
+```python
+# Lấy accuracy trends
+GET /api/dashboard/accuracy-trends
+
+# Lấy thống kê tổng quan
+GET /api/dashboard/statistics
+
+# Lấy phân bố lớp của model
+GET /api/dashboard/class-distribution/{model_filename}
+```
+
+### Export Charts
+- **PNG**: Click nút "💾 Export PNG"
+- **PDF**: Click nút "📄 Export PDF"
+
+---
+
+## 📝 2. Auto Report Generator
+
+Report tự động được tạo sau khi training và prediction hoàn thành.
+
+### Reports đã có
+- **Training Report**: Tự động tạo sau khi train xong
+  - Metrics, confusion matrix, class distribution
+  - Lưu trong folder `reports/`
+  
+- **Prediction Report**: Tự động tạo sau khi predict xong
+  - Thông tin về output file, bbox, features
+  - Lưu trong folder `reports/`
+
+### API Endpoints
+
+```python
+# Liệt kê reports
+GET /api/reports/list
+
+# Xem report
+GET /api/reports/view/{filename}
+
+# Download report
+GET /api/reports/download/{filename}
+```
+
+### Xem Reports
+- Web interface: http://localhost:8000/
+- Hoặc truy cập trực tiếp: http://localhost:8000/api/reports/view/{filename}
+
+---
+
+## 🔄 3. Batch Processing
+
+Predict nhiều khu vực cùng lúc với queue management.
+
+### Cách sử dụng
+
+#### Bước 1: Tạo CSV file
+Tạo file CSV với format:
+```csv
+name,min_lon,min_lat,max_lon,max_lat,start_date,end_date,max_scenes,cloud_cover,resolution
+Region_1,105.6,9.3,105.8,9.5,2023-03-01,2023-05-31,12,30,20
+Region_2,105.8,9.3,106.0,9.5,2023-03-01,2023-05-31,12,30,20
+```
+
+**File mẫu**: `batch_regions_example.csv`
+
+#### Bước 2: Upload và Start Batch
+1. Truy cập: http://localhost:8000/dashboard
+2. Chọn tab "🔄 Batch Processing"
+3. Upload CSV file
+4. Chọn model để predict
+5. Click "🚀 Start Batch Prediction"
+
+#### Bước 3: Theo dõi Progress
+Dashboard sẽ tự động refresh mỗi 3 giây và hiển thị:
+- ⏳ Queued: Đang chờ
+- ▶️ Running: Đang chạy
+- ✅ Completed: Hoàn thành
+- ❌ Failed: Lỗi
+
+### API Endpoints
+
+```python
+# Bắt đầu batch prediction
+POST /api/batch/start
+{
+    "model_filename": "model_20231221.joblib",
+    "items": [
+        {
+            "name": "Region_1",
+            "min_lon": 105.6,
+            "min_lat": 9.3,
+            "max_lon": 105.8,
+            "max_lat": 9.5,
+            "start_date": "2023-03-01",
+            "end_date": "2023-05-31",
+            "max_scenes": 12,
+            "cloud_cover": 30,
+            "resolution": 20
+        }
+    ],
+    "auto_retry": true,
+    "max_retries": 3
+}
+
+# Kiểm tra queue status
+GET /api/batch/status
+
+# Lấy kết quả batch
+GET /api/batch/results/{batch_id}
+
+# Hủy batch
+POST /api/batch/cancel/{batch_id}
+```
+
+### Auto-Retry
+- Tự động retry khi failed (default: max 3 lần)
+- Có thể tắt bằng cách set `auto_retry: false`
+
+### Progress Tracking
+- Mỗi job có progress bar riêng
+- Real-time update status
+- Hiển thị error message nếu failed
+
+---
+
+## 🚀 Khởi động Server
+
+```bash
+# Activate môi trường
+conda activate env_01
+
+# Chạy API server
+python api_server.py
+```
+
+Server sẽ chạy tại: http://localhost:8000
+
+## 📍 Các URL quan trọng
+
+- **Training Interface**: http://localhost:8000/
+- **Dashboard**: http://localhost:8000/dashboard
+- **API Docs**: http://localhost:8000/docs
+- **Redoc**: http://localhost:8000/redoc
+
+---
+
+## 🔧 Cấu trúc Folders
+
+```
+remote-sensing/
+├── api_server.py              # API server với các chức năng mới
+├── dashboard.html             # Dashboard UI (MỚI)
+├── training_interface.html    # Training UI
+├── report_generator.py        # Auto report generator
+├── batch_regions_example.csv  # CSV mẫu cho batch (MỚI)
+├── model_train/               # Models đã train
+├── predictions/               # Prediction outputs
+└── reports/                   # Auto-generated reports
+```
+
+---
+
+## 🎯 Use Cases
+
+### Use Case 1: Theo dõi Model Performance
+1. Train nhiều models với configs khác nhau
+2. Mở Dashboard → Tab "📊 Accuracy Trends"
+3. So sánh accuracy/F1-score qua thời gian
+4. Export charts để báo cáo
+
+### Use Case 2: Batch Prediction cho nhiều khu vực
+1. Chuẩn bị CSV với danh sách khu vực
+2. Upload vào Dashboard → Tab "🔄 Batch Processing"
+3. Chọn model tốt nhất
+4. Start batch và theo dõi progress
+5. Download results khi hoàn thành
+
+### Use Case 3: Tạo Reports tự động
+1. Chạy training/prediction
+2. Report tự động được tạo
+3. Xem qua Dashboard hoặc `/api/reports/list`
+4. Download để chia sẻ
+
+---
+
+## ⚠️ Lưu ý
+
+1. **Batch Processing**: Hiện tại chỉ xử lý tuần tự (từng job một)
+2. **Auto-retry**: Chỉ retry khi lỗi kỹ thuật, không retry nếu config sai
+3. **Charts Export**: Cần browser hỗ trợ Canvas API
+4. **Memory**: Batch lớn có thể tốn RAM, nên chia nhỏ
+
+---
+
+## 🐛 Troubleshooting
+
+### Dashboard không hiển thị data
+- Kiểm tra có models/predictions trong folders chưa
+- Refresh lại trang
+- Check console log (F12)
+
+### Batch processing không chạy
+- Kiểm tra format CSV đúng chưa
+- Kiểm tra model đã chọn có tồn tại không
+- Xem API logs để debug
+
+### Charts không export được
+- Browser phải hỗ trợ Canvas.toDataURL()
+- Thử browser khác (Chrome/Firefox)
+
+---
+
+## 📞 Support
+
+Nếu gặp vấn đề, check:
+1. API logs: `python api_server.py`
+2. Browser console: F12 → Console
+3. Network tab: F12 → Network
+
+---
+
+**🎉 Tất cả chức năng đã được phục hồi và nâng cấp!**

ThuanHoa/.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,10 @@
+{
+ "cells": [],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

UPDATE_COMPLETE.md

@@ -0,0 +1,254 @@
+# 🎉 Hệ thống đã được cập nhật hoàn chỉnh!
+
+## 📁 Cấu trúc hệ thống mới
+
+```
+remote-sensing/
+├── index.html                      # 🆕 Trang chính với tab navigation
+├── training_interface.html         # ✅ Interface training (độc lập)
+├── prediction_interface.html       # 🆕 Interface prediction (tách riêng)
+├── dashboard.html                  # ✅ Dashboard visualization
+├── api_server.py                   # ✅ API server (đã cập nhật đầy đủ)
+├── train_module.py                 # Training logic
+├── report_generator.py             # Auto report generator
+├── batch_regions_example.csv       # 🆕 CSV mẫu cho batch processing
+├── NEW_FEATURES.md                 # Documentation
+└── test_new_features.py            # Test script
+```
+
+## 🚀 Các URL hiện tại
+
+### Main Pages
+- **Trang chủ với tabs**: http://localhost:8000/
+- **Training standalone**: http://localhost:8000/training
+- **Prediction standalone**: http://localhost:8000/prediction
+- **Dashboard standalone**: http://localhost:8000/dashboard
+- **API Docs**: http://localhost:8000/docs
+
+### Tab Navigation trong Index
+1. 🏠 **Trang Chủ** - Tổng quan & quick start
+2. 🎓 **Training** - Training interface (iframe)
+3. 🗺️ **Prediction** - Prediction interface (iframe)
+4. 📊 **Dashboard** - Visualization & charts
+5. 🤖 **Models** - Quản lý models
+6. 📄 **Reports** - Xem & download reports
+7. 🔄 **Batch Processing** - Batch prediction queue
+
+## ✨ Chức năng đã cập nhật
+
+### 1. Tab Navigation System
+- ✅ Giao diện thống nhất với 7 tabs
+- ✅ Smooth transition animations
+- ✅ Responsive design
+- ✅ Real-time data loading
+
+### 2. Training Interface (Tách riêng)
+- ✅ Có thể truy cập độc lập tại `/training`
+- ✅ Hoặc embed trong tab của index.html
+- ✅ Đầy đủ chức năng như cũ
+
+### 3. Prediction Interface (Mới tách riêng)
+- ✅ Giao diện riêng biệt tại `/prediction`
+- ✅ Map selector với Leaflet
+- ✅ Model dropdown với info preview
+- ✅ Time & data configuration
+- ✅ Real-time status tracking
+- ✅ Download results & view reports
+- ✅ History của tất cả predictions
+
+### 4. Dashboard & Visualization
+- ✅ Accuracy trends charts
+- ✅ F1-Score comparison
+- ✅ Class distribution
+- ✅ Export PNG/PDF
+- ✅ Real-time statistics
+
+### 5. Batch Processing
+- ✅ Upload CSV file
+- ✅ Auto-retry mechanism
+- ✅ Queue management
+- ✅ Progress tracking
+- ✅ Real-time status updates
+
+## 🔧 API Endpoints mới
+
+### Dashboard APIs
+```
+GET /api/dashboard/accuracy-trends        # Accuracy trends over time
+GET /api/dashboard/statistics             # Tổng quan thống kê
+GET /api/dashboard/class-distribution/{model}  # Phân bố classes
+```
+
+### Batch Processing APIs
+```
+POST /api/batch/start                     # Bắt đầu batch prediction
+GET  /api/batch/status                    # Kiểm tra queue status
+GET  /api/batch/results/{batch_id}        # Lấy kết quả batch
+POST /api/batch/cancel/{batch_id}         # Hủy batch
+```
+
+### Existing APIs (đã có)
+```
+# Training
+POST /api/training/start
+GET  /api/training/status
+POST /api/training/stop
+
+# Prediction
+POST /api/prediction/start
+GET  /api/prediction/status
+
+# Models
+GET  /api/models/list
+
+# Reports
+GET  /api/reports/list
+GET  /api/reports/view/{filename}
+GET  /api/reports/download/{filename}
+
+# Predictions
+GET  /api/predictions/list
+GET  /api/predictions/download/{filename}
+
+# Cache
+GET  /api/cache/info
+POST /api/cache/clear
+```
+
+## 🎯 Cách sử dụng
+
+### 1. Khởi động server
+```bash
+conda activate env_01
+python api_server.py
+```
+
+### 2. Truy cập hệ thống
+Mở browser: http://localhost:8000/
+
+### 3. Workflow cơ bản
+
+#### A. Training
+1. Click tab "🎓 Training"
+2. Vẽ bbox hoặc chọn preset
+3. Cấu hình model type, parameters
+4. Click "Start Training"
+5. Theo dõi progress
+6. Download model & view report
+
+#### B. Prediction
+1. Click tab "🗺️ Prediction"
+2. Chọn model đã train
+3. Vẽ bbox khu vực cần predict
+4. Cấu hình time range & data
+5. Click "Start Prediction"
+6. Download GeoTIFF khi hoàn thành
+
+#### C. Dashboard
+1. Click tab "📊 Dashboard"
+2. Xem accuracy trends
+3. So sánh models
+4. Export charts PNG/PDF
+
+#### D. Batch Processing
+1. Click tab "🔄 Batch Processing"
+2. Upload CSV file (xem batch_regions_example.csv)
+3. Chọn model
+4. Click "Start Batch Prediction"
+5. Theo dõi progress từng job
+
+## 📊 Format CSV cho Batch Processing
+
+```csv
+name,min_lon,min_lat,max_lon,max_lat,start_date,end_date,max_scenes,cloud_cover,resolution
+Region_1,105.6,9.3,105.8,9.5,2023-03-01,2023-05-31,12,30,20
+Region_2,105.8,9.3,106.0,9.5,2023-03-01,2023-05-31,12,30,20
+```
+
+## 🔍 Test các chức năng
+
+```bash
+# Test tất cả APIs
+python test_new_features.py
+
+# Hoặc test thủ công
+curl http://localhost:8000/api/dashboard/statistics
+curl http://localhost:8000/api/models/list
+curl http://localhost:8000/api/batch/status
+```
+
+## 📝 Notes
+
+### Import Warnings
+Các warning về import (xarray, numpy, etc.) là bình thường vì:
+- Các thư viện này được import động trong runtime
+- Chỉ khi thực sự cần thiết (prediction/training)
+- Không ảnh hưởng đến hoạt động của server
+
+### Browser Compatibility
+- Khuyến nghị: Chrome, Firefox, Edge (latest)
+- Mobile responsive: Đã optimize
+- Chart.js & Leaflet: CDN loaded automatically
+
+### Performance
+- Training: Tùy vào config (5-30 phút)
+- Prediction: 2-10 phút tùy khu vực
+- Batch: Sequential processing (1 job/time)
+- Dashboard: Real-time updates mỗi 3s
+
+## 🎨 Tính năng UI/UX
+
+### Design
+- ✅ Modern gradient backgrounds
+- ✅ Card-based layouts
+- ✅ Smooth animations
+- ✅ Consistent color scheme
+- ✅ Responsive grid system
+
+### Interactions
+- ✅ Real-time progress bars
+- ✅ Status badges
+- ✅ Loading spinners
+- ✅ Error alerts
+- ✅ Success notifications
+
+### Charts
+- ✅ Interactive tooltips
+- ✅ Zoom & pan
+- ✅ Export functionality
+- ✅ Responsive sizing
+
+## 🚨 Troubleshooting
+
+### Server không start
+```bash
+# Check port 8000
+lsof -i :8000
+# Kill if needed
+kill -9 <PID>
+```
+
+### Tab không load
+- Clear browser cache
+- Check console (F12)
+- Verify file paths
+
+### Batch không chạy
+- Check CSV format
+- Verify model exists
+- Check API logs
+
+## 📞 Support
+
+Nếu gặp vấn đề:
+1. Check terminal logs
+2. Check browser console (F12)
+3. Verify all HTML files exist
+4. Test API endpoints với curl/Postman
+
+---
+
+**🎉 Hệ thống đã sẵn sàng sử dụng!**
+
+Start server: `python api_server.py`
+Access: http://localhost:8000/

cloud_removal_model/CRGAN_generator.json

@@ -0,0 +1,11 @@
+{
+  "filename": "CRGAN_generator.pth",
+  "epoch": 0,
+  "train_loss": 0.0,
+  "val_loss": 0.0,
+  "in_channels": 6,
+  "out_channels": 4,
+  "use_s1": true,
+  "description": "",
+  "uploaded_at": "2026-01-26T13:36:58.433105"
+}

cloud_removal_model/GLFCR_generator.json

@@ -0,0 +1,11 @@
+{
+  "filename": "GLFCR_generator.pth",
+  "epoch": 10,
+  "train_loss": 0.014,
+  "val_loss": 0.01,
+  "in_channels": 6,
+  "out_channels": 4,
+  "use_s1": true,
+  "description": "No describe",
+  "uploaded_at": "2026-01-27T10:38:28.134503"
+}

cloud_removal_model/SpAGAN_generator.json

@@ -0,0 +1,11 @@
+{
+  "filename": "SpAGAN_generator.pth",
+  "epoch": 30,
+  "train_loss": 0.222,
+  "val_loss": 0.11,
+  "in_channels": 6,
+  "out_channels": 4,
+  "use_s1": true,
+  "description": "No data",
+  "uploaded_at": "2026-01-26T13:43:06.751319"
+}

dashboard.html

@@ -0,0 +1,935 @@
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Dashboard - Land Classification System</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            min-height: 100vh;
+            padding: 20px;
+        }
+
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+        }
+
+        .header {
+            background: white;
+            padding: 25px;
+            border-radius: 15px;
+            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
+            margin-bottom: 30px;
+            text-align: center;
+        }
+
+        .header h1 {
+            color: #667eea;
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }
+
+        .header p {
+            color: #666;
+            font-size: 1.1em;
+        }
+
+        .nav-tabs {
+            display: flex;
+            gap: 10px;
+            margin-bottom: 20px;
+            background: white;
+            padding: 15px;
+            border-radius: 15px;
+            box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
+        }
+
+        .nav-tab {
+            flex: 1;
+            padding: 15px 25px;
+            background: #f5f5f5;
+            border: none;
+            border-radius: 10px;
+            cursor: pointer;
+            font-size: 1.1em;
+            font-weight: 600;
+            transition: all 0.3s;
+            color: #666;
+        }
+
+        .nav-tab:hover {
+            background: #e0e0e0;
+        }
+
+        .nav-tab.active {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
+        }
+
+        .tab-content {
+            display: none;
+        }
+
+        .tab-content.active {
+            display: block;
+        }
+
+        .stats-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
+            gap: 20px;
+            margin-bottom: 30px;
+        }
+
+        .stat-card {
+            background: white;
+            padding: 25px;
+            border-radius: 15px;
+            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
+            transition: transform 0.3s;
+        }
+
+        .stat-card:hover {
+            transform: translateY(-5px);
+        }
+
+        .stat-card .icon {
+            font-size: 3em;
+            margin-bottom: 15px;
+        }
+
+        .stat-card .value {
+            font-size: 2.5em;
+            font-weight: bold;
+            color: #667eea;
+            margin-bottom: 5px;
+        }
+
+        .stat-card .label {
+            color: #666;
+            font-size: 1.1em;
+        }
+
+        .chart-container {
+            background: white;
+            padding: 30px;
+            border-radius: 15px;
+            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
+            margin-bottom: 30px;
+        }
+
+        .chart-container h3 {
+            margin-bottom: 20px;
+            color: #333;
+            font-size: 1.5em;
+        }
+
+        .chart-wrapper {
+            position: relative;
+            height: 400px;
+        }
+
+        canvas {
+            max-height: 100%;
+        }
+
+        .batch-queue {
+            background: white;
+            padding: 30px;
+            border-radius: 15px;
+            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.1);
+        }
+
+        .batch-item {
+            padding: 20px;
+            border: 2px solid #e0e0e0;
+            border-radius: 10px;
+            margin-bottom: 15px;
+            transition: all 0.3s;
+        }
+
+        .batch-item:hover {
+            border-color: #667eea;
+            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.2);
+        }
+
+        .batch-item.running {
+            border-color: #4caf50;
+            background: #f1f8f4;
+        }
+
+        .batch-item.completed {
+            border-color: #2196f3;
+            background: #e3f2fd;
+        }
+
+        .batch-item.failed {
+            border-color: #f44336;
+            background: #ffebee;
+        }
+
+        .batch-header {
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+            margin-bottom: 10px;
+        }
+
+        .batch-name {
+            font-size: 1.2em;
+            font-weight: 600;
+            color: #333;
+        }
+
+        .batch-status {
+            padding: 8px 16px;
+            border-radius: 20px;
+            font-weight: 600;
+            font-size: 0.9em;
+        }
+
+        .batch-status.queued {
+            background: #fff3cd;
+            color: #856404;
+        }
+
+        .batch-status.running {
+            background: #d4edda;
+            color: #155724;
+        }
+
+        .batch-status.completed {
+            background: #cce5ff;
+            color: #004085;
+        }
+
+        .batch-status.failed {
+            background: #f8d7da;
+            color: #721c24;
+        }
+
+        .progress-bar {
+            width: 100%;
+            height: 8px;
+            background: #e0e0e0;
+            border-radius: 10px;
+            overflow: hidden;
+            margin-top: 10px;
+        }
+
+        .progress-fill {
+            height: 100%;
+            background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+            transition: width 0.3s;
+        }
+
+        .btn {
+            padding: 12px 30px;
+            border: none;
+            border-radius: 8px;
+            cursor: pointer;
+            font-size: 1em;
+            font-weight: 600;
+            transition: all 0.3s;
+        }
+
+        .btn-primary {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
+        }
+
+        .btn-primary:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 7px 20px rgba(102, 126, 234, 0.6);
+        }
+
+        .btn-danger {
+            background: #f44336;
+            color: white;
+        }
+
+        .btn-danger:hover {
+            background: #d32f2f;
+        }
+
+        .btn-success {
+            background: #4caf50;
+            color: white;
+        }
+
+        .btn-success:hover {
+            background: #45a049;
+        }
+
+        .export-buttons {
+            display: flex;
+            gap: 10px;
+            margin-top: 20px;
+        }
+
+        .file-upload {
+            margin-bottom: 20px;
+        }
+
+        .file-upload input[type="file"] {
+            display: none;
+        }
+
+        .file-upload label {
+            display: inline-block;
+            padding: 12px 30px;
+            background: #667eea;
+            color: white;
+            border-radius: 8px;
+            cursor: pointer;
+            font-weight: 600;
+            transition: all 0.3s;
+        }
+
+        .file-upload label:hover {
+            background: #5568d3;
+        }
+
+        .loading {
+            text-align: center;
+            padding: 40px;
+            color: #666;
+        }
+
+        .loading::after {
+            content: '...';
+            animation: loading 1.5s infinite;
+        }
+
+        @keyframes loading {
+            0%, 20% { content: '.'; }
+            40% { content: '..'; }
+            60%, 100% { content: '...'; }
+        }
+
+        .model-selector {
+            margin-bottom: 20px;
+        }
+
+        .model-selector select {
+            width: 100%;
+            padding: 12px;
+            border: 2px solid #e0e0e0;
+            border-radius: 8px;
+            font-size: 1em;
+            background: white;
+            cursor: pointer;
+        }
+
+        .model-selector select:focus {
+            outline: none;
+            border-color: #667eea;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>📊 Dashboard - Land Classification System</h1>
+            <p>Tổng quan hệ thống phân loại đất từ xa</p>
+        </div>
+
+        <div class="nav-tabs">
+            <button class="nav-tab active" onclick="switchTab('overview')">📈 Tổng Quan</button>
+            <button class="nav-tab" onclick="switchTab('trends')">📊 Accuracy Trends</button>
+            <button class="nav-tab" onclick="switchTab('batch')">🔄 Batch Processing</button>
+        </div>
+
+        <!-- Tab: Tổng Quan -->
+        <div id="overview" class="tab-content active">
+            <div class="stats-grid">
+                <div class="stat-card">
+                    <div class="icon">🤖</div>
+                    <div class="value" id="totalModels">-</div>
+                    <div class="label">Models Trained</div>
+                </div>
+                <div class="stat-card">
+                    <div class="icon">🗺️</div>
+                    <div class="value" id="totalPredictions">-</div>
+                    <div class="label">Predictions Generated</div>
+                </div>
+                <div class="stat-card">
+                    <div class="icon">📄</div>
+                    <div class="value" id="totalReports">-</div>
+                    <div class="label">Reports Created</div>
+                </div>
+                <div class="stat-card">
+                    <div class="icon">✅</div>
+                    <div class="value" id="latestAccuracy">-</div>
+                    <div class="label">Latest Model Accuracy</div>
+                </div>
+            </div>
+
+            <div class="chart-container">
+                <h3>📊 Phân bố các lớp đất (Model mới nhất)</h3>
+                <div class="model-selector">
+                    <select id="modelSelect" onchange="loadClassDistribution()">
+                        <option value="">Chọn model...</option>
+                    </select>
+                </div>
+                <div class="chart-wrapper">
+                    <canvas id="classDistChart"></canvas>
+                </div>
+                <div class="export-buttons">
+                    <button class="btn btn-primary" onclick="exportChart('classDistChart', 'class-distribution.png')">
+                        💾 Export PNG
+                    </button>
+                    <button class="btn btn-success" onclick="exportChartPDF('classDistChart', 'class-distribution.pdf')">
+                        📄 Export PDF
+                    </button>
+                </div>
+            </div>
+        </div>
+
+        <!-- Tab: Accuracy Trends -->
+        <div id="trends" class="tab-content">
+            <div class="chart-container">
+                <h3>📈 Accuracy Trends Over Time</h3>
+                <div class="chart-wrapper">
+                    <canvas id="accuracyTrendChart"></canvas>
+                </div>
+                <div class="export-buttons">
+                    <button class="btn btn-primary" onclick="exportChart('accuracyTrendChart', 'accuracy-trends.png')">
+                        💾 Export PNG
+                    </button>
+                    <button class="btn btn-success" onclick="exportChartPDF('accuracyTrendChart', 'accuracy-trends.pdf')">
+                        📄 Export PDF
+                    </button>
+                </div>
+            </div>
+
+            <div class="chart-container">
+                <h3>📊 F1-Score Comparison</h3>
+                <div class="chart-wrapper">
+                    <canvas id="f1ScoreChart"></canvas>
+                </div>
+            </div>
+        </div>
+
+        <!-- Tab: Batch Processing -->
+        <div id="batch" class="tab-content">
+            <div class="batch-queue">
+                <h3>🔄 Batch Prediction Queue</h3>
+                
+                <div class="file-upload">
+                    <label for="csvFile">📁 Upload CSV File</label>
+                    <input type="file" id="csvFile" accept=".csv" onchange="handleCSVUpload(event)">
+                    <p style="margin-top: 10px; color: #666;">
+                        Format CSV: name,min_lon,min_lat,max_lon,max_lat
+                    </p>
+                </div>
+
+                <div class="model-selector">
+                    <select id="batchModelSelect">
+                        <option value="">Chọn model để predict...</option>
+                    </select>
+                </div>
+
+                <button class="btn btn-primary" onclick="startBatchPrediction()" style="margin-bottom: 30px;">
+                    🚀 Start Batch Prediction
+                </button>
+
+                <h4 style="margin: 20px 0;">Queue Status</h4>
+                <div class="stats-grid" style="margin-bottom: 30px;">
+                    <div class="stat-card">
+                        <div class="value" id="queuedJobs">0</div>
+                        <div class="label">⏳ Queued</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value" id="runningJobs">0</div>
+                        <div class="label">▶️ Running</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value" id="completedJobs">0</div>
+                        <div class="label">✅ Completed</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value" id="failedJobs">0</div>
+                        <div class="label">❌ Failed</div>
+                    </div>
+                </div>
+
+                <h4 style="margin: 20px 0;">Active Jobs</h4>
+                <div id="batchJobs">
+                    <p class="loading">Đang tải dữ liệu</p>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/jspdf/2.5.1/jspdf.umd.min.js"></script>
+
+    <script>
+        let charts = {};
+        let batchItems = [];
+        let refreshInterval = null;
+
+        // Tab switching
+        function switchTab(tabName) {
+            // Update tab buttons
+            document.querySelectorAll('.nav-tab').forEach(tab => {
+                tab.classList.remove('active');
+            });
+            event.target.classList.add('active');
+
+            // Update tab content
+            document.querySelectorAll('.tab-content').forEach(content => {
+                content.classList.remove('active');
+            });
+            document.getElementById(tabName).classList.add('active');
+
+            // Load data for the active tab
+            if (tabName === 'overview') {
+                loadDashboardStats();
+            } else if (tabName === 'trends') {
+                loadAccuracyTrends();
+            } else if (tabName === 'batch') {
+                loadBatchStatus();
+                startBatchRefresh();
+            } else {
+                stopBatchRefresh();
+            }
+        }
+
+        // Load dashboard statistics
+        async function loadDashboardStats() {
+            try {
+                const response = await fetch('/api/dashboard/statistics');
+                const data = await response.json();
+
+                document.getElementById('totalModels').textContent = data.models.total;
+                document.getElementById('totalPredictions').textContent = data.predictions.total;
+                document.getElementById('totalReports').textContent = data.reports.total;
+
+                if (data.models.latest && data.models.latest.metrics) {
+                    const accuracy = (data.models.latest.metrics.accuracy * 100).toFixed(2);
+                    document.getElementById('latestAccuracy').textContent = accuracy + '%';
+                }
+
+                // Load models for selector
+                await loadModelsList();
+            } catch (error) {
+                console.error('Error loading dashboard stats:', error);
+            }
+        }
+
+        // Load models list
+        async function loadModelsList() {
+            try {
+                const response = await fetch('/api/models/list');
+                const data = await response.json();
+
+                const modelSelect = document.getElementById('modelSelect');
+                const batchModelSelect = document.getElementById('batchModelSelect');
+
+                modelSelect.innerHTML = '<option value="">Chọn model...</option>';
+                batchModelSelect.innerHTML = '<option value="">Chọn model...</option>';
+
+                data.models.forEach(model => {
+                    const option = document.createElement('option');
+                    option.value = model.filename;
+                    option.textContent = `${model.filename} (${model.created})`;
+                    modelSelect.appendChild(option.cloneNode(true));
+                    batchModelSelect.appendChild(option);
+                });
+
+                // Auto-select latest model
+                if (data.models.length > 0) {
+                    modelSelect.value = data.models[0].filename;
+                    await loadClassDistribution();
+                }
+            } catch (error) {
+                console.error('Error loading models:', error);
+            }
+        }
+
+        // Load class distribution
+        async function loadClassDistribution() {
+            const modelFilename = document.getElementById('modelSelect').value;
+            if (!modelFilename) return;
+
+            try {
+                const response = await fetch(`/api/dashboard/class-distribution/${modelFilename}`);
+                const data = await response.json();
+
+                const labels = Object.keys(data.class_distribution);
+                const values = Object.values(data.class_distribution);
+
+                if (charts.classDistChart) {
+                    charts.classDistChart.destroy();
+                }
+
+                const ctx = document.getElementById('classDistChart').getContext('2d');
+                charts.classDistChart = new Chart(ctx, {
+                    type: 'bar',
+                    data: {
+                        labels: labels,
+                        datasets: [{
+                            label: 'Số lượng mẫu',
+                            data: values,
+                            backgroundColor: [
+                                'rgba(102, 126, 234, 0.7)',
+                                'rgba(118, 75, 162, 0.7)',
+                                'rgba(76, 175, 80, 0.7)',
+                                'rgba(244, 67, 54, 0.7)',
+                                'rgba(33, 150, 243, 0.7)',
+                                'rgba(255, 193, 7, 0.7)',
+                            ],
+                            borderColor: [
+                                'rgba(102, 126, 234, 1)',
+                                'rgba(118, 75, 162, 1)',
+                                'rgba(76, 175, 80, 1)',
+                                'rgba(244, 67, 54, 1)',
+                                'rgba(33, 150, 243, 1)',
+                                'rgba(255, 193, 7, 1)',
+                            ],
+                            borderWidth: 2
+                        }]
+                    },
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        plugins: {
+                            legend: {
+                                display: false
+                            },
+                            title: {
+                                display: true,
+                                text: `Tổng: ${data.total_samples} mẫu`
+                            }
+                        },
+                        scales: {
+                            y: {
+                                beginAtZero: true
+                            }
+                        }
+                    }
+                });
+            } catch (error) {
+                console.error('Error loading class distribution:', error);
+            }
+        }
+
+        // Load accuracy trends
+        async function loadAccuracyTrends() {
+            try {
+                const response = await fetch('/api/dashboard/accuracy-trends');
+                const data = await response.json();
+
+                if (data.trends.length === 0) {
+                    return;
+                }
+
+                // Prepare data
+                const labels = data.trends.map(d => new Date(d.date).toLocaleDateString('vi-VN'));
+                const accuracies = data.trends.map(d => d.accuracy * 100);
+                const f1Scores = data.trends.map(d => d.f1_score * 100);
+                const precisions = data.trends.map(d => d.precision * 100);
+                const recalls = data.trends.map(d => d.recall * 100);
+
+                // Accuracy Trend Chart
+                if (charts.accuracyTrendChart) {
+                    charts.accuracyTrendChart.destroy();
+                }
+
+                const ctx1 = document.getElementById('accuracyTrendChart').getContext('2d');
+                charts.accuracyTrendChart = new Chart(ctx1, {
+                    type: 'line',
+                    data: {
+                        labels: labels,
+                        datasets: [
+                            {
+                                label: 'Accuracy (%)',
+                                data: accuracies,
+                                borderColor: 'rgba(102, 126, 234, 1)',
+                                backgroundColor: 'rgba(102, 126, 234, 0.1)',
+                                fill: true,
+                                tension: 0.4
+                            },
+                            {
+                                label: 'Precision (%)',
+                                data: precisions,
+                                borderColor: 'rgba(76, 175, 80, 1)',
+                                backgroundColor: 'rgba(76, 175, 80, 0.1)',
+                                fill: false,
+                                tension: 0.4
+                            },
+                            {
+                                label: 'Recall (%)',
+                                data: recalls,
+                                borderColor: 'rgba(244, 67, 54, 1)',
+                                backgroundColor: 'rgba(244, 67, 54, 0.1)',
+                                fill: false,
+                                tension: 0.4
+                            }
+                        ]
+                    },
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        plugins: {
+                            legend: {
+                                display: true,
+                                position: 'top'
+                            }
+                        },
+                        scales: {
+                            y: {
+                                beginAtZero: true,
+                                max: 100,
+                                ticks: {
+                                    callback: function(value) {
+                                        return value + '%';
+                                    }
+                                }
+                            }
+                        }
+                    }
+                });
+
+                // F1-Score Chart
+                if (charts.f1ScoreChart) {
+                    charts.f1ScoreChart.destroy();
+                }
+
+                const ctx2 = document.getElementById('f1ScoreChart').getContext('2d');
+                charts.f1ScoreChart = new Chart(ctx2, {
+                    type: 'bar',
+                    data: {
+                        labels: labels,
+                        datasets: [{
+                            label: 'F1-Score (%)',
+                            data: f1Scores,
+                            backgroundColor: 'rgba(118, 75, 162, 0.7)',
+                            borderColor: 'rgba(118, 75, 162, 1)',
+                            borderWidth: 2
+                        }]
+                    },
+                    options: {
+                        responsive: true,
+                        maintainAspectRatio: false,
+                        scales: {
+                            y: {
+                                beginAtZero: true,
+                                max: 100,
+                                ticks: {
+                                    callback: function(value) {
+                                        return value + '%';
+                                    }
+                                }
+                            }
+                        }
+                    }
+                });
+            } catch (error) {
+                console.error('Error loading accuracy trends:', error);
+            }
+        }
+
+        // Export chart as PNG
+        function exportChart(chartId, filename) {
+            const canvas = document.getElementById(chartId);
+            const url = canvas.toDataURL('image/png');
+            const link = document.createElement('a');
+            link.download = filename;
+            link.href = url;
+            link.click();
+        }
+
+        // Export chart as PDF
+        function exportChartPDF(chartId, filename) {
+            const canvas = document.getElementById(chartId);
+            const imgData = canvas.toDataURL('image/png');
+            
+            const { jsPDF } = window.jspdf;
+            const pdf = new jsPDF({
+                orientation: 'landscape',
+                unit: 'px',
+                format: [canvas.width, canvas.height]
+            });
+            
+            pdf.addImage(imgData, 'PNG', 0, 0, canvas.width, canvas.height);
+            pdf.save(filename);
+        }
+
+        // Handle CSV upload
+        function handleCSVUpload(event) {
+            const file = event.target.files[0];
+            if (!file) return;
+
+            const reader = new FileReader();
+            reader.onload = function(e) {
+                const text = e.target.result;
+                parseCSV(text);
+            };
+            reader.readAsText(file);
+        }
+
+        // Parse CSV
+        function parseCSV(text) {
+            const lines = text.trim().split('\n');
+            batchItems = [];
+
+            // Skip header
+            for (let i = 1; i < lines.length; i++) {
+                const parts = lines[i].split(',');
+                if (parts.length >= 5) {
+                    batchItems.push({
+                        name: parts[0].trim(),
+                        min_lon: parseFloat(parts[1]),
+                        min_lat: parseFloat(parts[2]),
+                        max_lon: parseFloat(parts[3]),
+                        max_lat: parseFloat(parts[4]),
+                        start_date: parts[5]?.trim() || "2023-03-01",
+                        end_date: parts[6]?.trim() || "2023-05-31",
+                        max_scenes: parseInt(parts[7]) || 12,
+                        cloud_cover: parseInt(parts[8]) || 30,
+                        resolution: parseInt(parts[9]) || 20
+                    });
+                }
+            }
+
+            alert(`✅ Đã tải ${batchItems.length} khu vực từ CSV`);
+        }
+
+        // Start batch prediction
+        async function startBatchPrediction() {
+            const modelFilename = document.getElementById('batchModelSelect').value;
+            
+            if (!modelFilename) {
+                alert('❌ Vui lòng chọn model');
+                return;
+            }
+
+            if (batchItems.length === 0) {
+                alert('❌ Vui lòng upload file CSV trước');
+                return;
+            }
+
+            try {
+                const response = await fetch('/api/batch/start', {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: JSON.stringify({
+                        model_filename: modelFilename,
+                        items: batchItems,
+                        auto_retry: true,
+                        max_retries: 3
+                    })
+                });
+
+                const result = await response.json();
+                alert(`✅ ${result.message}`);
+                
+                // Refresh batch status
+                loadBatchStatus();
+            } catch (error) {
+                console.error('Error starting batch:', error);
+                alert('❌ Lỗi khi bắt đầu batch prediction');
+            }
+        }
+
+        // Load batch status
+        async function loadBatchStatus() {
+            try {
+                const response = await fetch('/api/batch/status');
+                const data = await response.json();
+
+                // Update counters
+                document.getElementById('queuedJobs').textContent = data.queue.queued;
+                document.getElementById('runningJobs').textContent = data.queue.running;
+                document.getElementById('completedJobs').textContent = data.queue.completed;
+                document.getElementById('failedJobs').textContent = data.queue.failed;
+
+                // Display jobs
+                const jobsContainer = document.getElementById('batchJobs');
+                jobsContainer.innerHTML = '';
+
+                // Combine all jobs
+                const allJobs = [
+                    ...data.jobs.running,
+                    ...data.jobs.queued,
+                    ...data.jobs.recent_completed,
+                    ...data.jobs.recent_failed
+                ];
+
+                if (allJobs.length === 0) {
+                    jobsContainer.innerHTML = '<p style="text-align: center; color: #666;">Chưa có job nào</p>';
+                    return;
+                }
+
+                allJobs.forEach(job => {
+                    const jobElement = document.createElement('div');
+                    jobElement.className = `batch-item ${job.status}`;
+                    
+                    const progress = job.progress || 0;
+                    const errorMsg = job.error ? `<p style="color: #f44336; margin-top: 10px;">⚠️ ${job.error}</p>` : '';
+                    
+                    jobElement.innerHTML = `
+                        <div class="batch-header">
+                            <div class="batch-name">${job.name}</div>
+                            <div class="batch-status ${job.status}">${job.status.toUpperCase()}</div>
+                        </div>
+                        <p style="color: #666; margin: 5px 0;">Job ID: ${job.job_id}</p>
+                        <p style="color: #666; margin: 5px 0;">
+                            📍 [${job.config.min_lon.toFixed(2)}, ${job.config.min_lat.toFixed(2)}] → 
+                            [${job.config.max_lon.toFixed(2)}, ${job.config.max_lat.toFixed(2)}]
+                        </p>
+                        ${job.retries > 0 ? `<p style="color: #ff9800; margin: 5px 0;">🔄 Retries: ${job.retries}/${job.max_retries}</p>` : ''}
+                        ${errorMsg}
+                        <div class="progress-bar">
+                            <div class="progress-fill" style="width: ${progress}%"></div>
+                        </div>
+                    `;
+                    
+                    jobsContainer.appendChild(jobElement);
+                });
+            } catch (error) {
+                console.error('Error loading batch status:', error);
+            }
+        }
+
+        // Auto-refresh batch status
+        function startBatchRefresh() {
+            if (refreshInterval) return;
+            refreshInterval = setInterval(loadBatchStatus, 3000);
+        }
+
+        function stopBatchRefresh() {
+            if (refreshInterval) {
+                clearInterval(refreshInterval);
+                refreshInterval = null;
+            }
+        }
+
+        // Initialize on page load
+        window.onload = function() {
+            loadDashboardStats();
+        };
+
+        // Cleanup on page unload
+        window.onbeforeunload = function() {
+            stopBatchRefresh();
+        };
+    </script>
+</body>
+</html>

document/giai_thich_quy_trinh_phan_loai_dat.md

@@ -0,0 +1,164 @@
+# 🌾 Giải Thích Quy Trình Phân Loại Đất Trồng Cây
+
+File notebook `02.predict_ODC.ipynb` sử dụng **Machine Learning** kết hợp với **dữ liệu vệ tinh** để phân loại các loại đất/cây trồng. Dưới đây là quy trình chi tiết:
+
+---
+
+## **Bước 1: Thu thập dữ liệu vệ tinh** (Cell 3-4)
+
+```python
+date_range = ('2022-09-01', '2023-10-01')
+longtitude_range = (105.86575, 105.94120)
+latitude_range = (9.65070, 9.69850)
+data = load_data(dc, date_range, longtitude_range, latitude_range)
+```
+
+- Lấy ảnh **Sentinel-2** (ảnh quang học) từ kho dữ liệu trong khoảng thời gian và vị trí cụ thể
+
+---
+
+## **Bước 2: Xử lý mây** (Cell 5)
+
+```python
+result = mask_clean(data)
+```
+
+- Loại bỏ các pixel bị mây che phủ để đảm bảo dữ liệu chính xác
+
+---
+
+## **Bước 3: Tính chỉ số NDVI** (Cell 6-10)
+
+```python
+ndvi = calculate_indices(result, index='NDVI', satellite_mission='s2')
+fill_nan_ndvi = fill_nan(ndvi, time_split)
+average_ndvi = fill_nan_ndvi.resample(time='1M').mean()
+```
+
+- **NDVI** (Normalized Difference Vegetation Index) = (NIR - Red) / (NIR + Red)
+- Giá trị từ **-1 đến 1**: cao = thực vật xanh tốt, thấp = đất trống/nước
+- Điền giá trị nan (mây) và tính trung bình theo tháng
+
+---
+
+## **Bước 4: Lấy dữ liệu Radar Sentinel-1** (Cell 11)
+
+```python
+dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)
+average_vv = calculate_average(dsvv, time_pattern='1M')
+average_vh = calculate_average(dsvh, time_pattern='1M')
+```
+
+- **VH, VV**: Dữ liệu radar (xuyên mây), cho biết cấu trúc bề mặt
+- Giúp phân biệt lúa ngập nước, cây trồng cạn, mặt nước...
+
+---
+
+## **Bước 5: Dự đoán bằng Model ML** (Cell 12) ⭐ **QUAN TRỌNG NHẤT**
+
+```python
+loaded_model = joblib.load("model_train/model_odc.joblib")
+data_array = predict(loaded_model, data.rio.crs, average_ndvi, average_vh, average_vv)
+```
+
+**Model đã được train trước** với dữ liệu mẫu (training data) gồm:
+- **Đầu vào (Features)**: NDVI theo tháng + VH + VV (chuỗi thời gian)
+- **Đầu ra (Labels)**: Loại đất đã được gắn nhãn thủ công
+
+### Cách model phân loại:
+
+| Đặc điểm | Loại đất |
+|----------|----------|
+| NDVI cao đều, VV thấp | Rừng |
+| NDVI biến đổi theo mùa vụ, VH cao (nước) | Lúa |
+| NDVI thấp, VV rất thấp | Sông/nước |
+| NDVI trung bình ổn định | Cây lâu năm (CLN) |
+
+---
+
+## **Bước 6: Hiển thị kết quả** (Cell 13-15)
+
+```python
+colors = ["#abcee9", "#ffef44", "#c4ff9e", "#ffd6a8", "#93ddda", "#1aeef7", "#ffa7f2", "#33ee33"]
+labels = ["Lúa tôm", "Lúa", "CHN", "CLN", "TS", "Sông", "Đất xây dựng", "Rừng"]
+```
+
+### 8 lớp phân loại:
+
+| Mã | Tên | Màu | Ý nghĩa |
+|----|-----|-----|---------|
+| 0 | Lúa tôm | 🔵 Xanh nhạt | Luân canh lúa-tôm |
+| 1 | Lúa | 🟡 Vàng | Đất trồng lúa |
+| 2 | CHN | 🟢 Xanh lá nhạt | Cây hàng năm |
+| 3 | CLN | 🟠 Cam nhạt | Cây lâu năm (cây ăn trái) |
+| 4 | TS | 🩵 Xanh ngọc | Thủy sản |
+| 5 | Sông | 🔷 Cyan | Mặt nước sông |
+| 6 | Đất XD | 💗 Hồng | Đất xây dựng |
+| 7 | Rừng | 💚 Xanh đậm | Rừng |
+
+---
+
+## **Bước 7: Lưu kết quả** (Cell 16)
+
+```python
+region_result.rio.to_raster("KetQuaPhanLoaiDatODC.tif")
+```
+
+- Xuất file GeoTIFF chứa mã phân loại (0-7) cho từng pixel
+
+---
+
+## 📊 **Tóm tắt quy trình:**
+
+```
+Ảnh vệ tinh (Sentinel-1 + Sentinel-2)
+        ↓
+Xử lý (loại mây, tính NDVI, VH, VV)
+        ↓
+Kết hợp features theo thời gian (13 tháng)
+        ↓
+Model ML (Random Forest/XGBoost) dự đoán
+        ↓
+Bản đồ phân loại 8 lớp đất
+        ↓
+File .tif (mỗi pixel = 1 mã loại đất)
+```
+
+---
+
+## 📁 Cấu trúc dữ liệu đầu vào cho Model
+
+### Features (Đặc trưng):
+- **NDVI theo 13 tháng**: 13 bands
+- **VH (radar) theo 13 tháng**: 13 bands  
+- **VV (radar) theo 13 tháng**: 13 bands
+- **Tổng cộng**: ~39 features cho mỗi pixel
+
+### Labels (Nhãn):
+- Được lấy từ shapefile training: `train/ST_training data_updated_1130points_new.shp`
+- 1130 điểm mẫu đã được gắn nhãn thủ công bởi chuyên gia
+
+---
+
+## 🔧 Các thư viện sử dụng
+
+| Thư viện | Mục đích |
+|----------|----------|
+| `datacube` | Truy vấn dữ liệu vệ tinh |
+| `xarray` | Xử lý dữ liệu đa chiều |
+| `rioxarray` | Đọc/ghi GeoTIFF |
+| `joblib` | Load/save model ML |
+| `sklearn` / `xgboost` | Training model |
+| `matplotlib` / `hvplot` | Trực quan hóa |
+
+---
+
+## 📝 Ghi chú
+
+- **Độ phân giải**: 10-20m (tùy cấu hình)
+- **Thời gian xử lý**: Phụ thuộc vào kích thước vùng và số scenes
+- **Yêu cầu**: Cần kết nối internet để tải dữ liệu vệ tinh từ Planetary Computer hoặc ODC
+
+---
+
+*Tài liệu được tạo ngày 14/12/2025*

index.html

@@ -0,0 +1,991 @@
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Land Classification System - Complete Platform</title>
+    
+    <!-- Leaflet CSS -->
+    <link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" />
+    <link rel="stylesheet" href="https://unpkg.com/leaflet-draw@1.0.4/dist/leaflet.draw.css" />
+    
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        
+        body {
+            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            min-height: 100vh;
+        }
+        
+        .main-container {
+            max-width: 1600px;
+            margin: 0 auto;
+            padding: 20px;
+        }
+        
+        .header {
+            background: white;
+            padding: 30px;
+            border-radius: 15px;
+            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.2);
+            margin-bottom: 20px;
+            text-align: center;
+        }
+        
+        .header h1 {
+            color: #667eea;
+            font-size: 2.8em;
+            margin-bottom: 10px;
+            font-weight: 700;
+        }
+        
+        .header p {
+            color: #666;
+            font-size: 1.2em;
+        }
+        
+        /* Navigation Tabs */
+        .nav-tabs {
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 5px 20px rgba(0, 0, 0, 0.15);
+            padding: 15px;
+            margin-bottom: 20px;
+            display: flex;
+            gap: 10px;
+            overflow-x: auto;
+        }
+        
+        .nav-tab {
+            flex: 1;
+            min-width: 150px;
+            padding: 15px 25px;
+            background: #f5f5f5;
+            border: none;
+            border-radius: 10px;
+            cursor: pointer;
+            font-size: 1.1em;
+            font-weight: 600;
+            transition: all 0.3s;
+            color: #666;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            gap: 8px;
+        }
+        
+        .nav-tab:hover {
+            background: #e0e0e0;
+            transform: translateY(-2px);
+        }
+        
+        .nav-tab.active {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
+        }
+        
+        /* Tab Content */
+        .tab-content {
+            display: none;
+            animation: fadeIn 0.3s;
+        }
+        
+        .tab-content.active {
+            display: block;
+        }
+        
+        @keyframes fadeIn {
+            from { opacity: 0; transform: translateY(10px); }
+            to { opacity: 1; transform: translateY(0); }
+        }
+        
+        /* Content Container */
+        .content-wrapper {
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 10px 30px rgba(0, 0, 0, 0.15);
+            padding: 30px;
+            min-height: 600px;
+        }
+        
+        /* Common Styles */
+        .section {
+            margin-bottom: 30px;
+        }
+        
+        .section h2 {
+            color: #667eea;
+            margin-bottom: 15px;
+            font-size: 1.8em;
+            border-bottom: 3px solid #667eea;
+            padding-bottom: 10px;
+        }
+        
+        .section h3 {
+            color: #333;
+            margin-bottom: 15px;
+            font-size: 1.3em;
+        }
+        
+        .form-group {
+            margin-bottom: 20px;
+        }
+        
+        .form-group label {
+            display: block;
+            margin-bottom: 8px;
+            color: #333;
+            font-weight: 600;
+        }
+        
+        .form-group input,
+        .form-group select {
+            width: 100%;
+            padding: 12px;
+            border: 2px solid #e0e0e0;
+            border-radius: 8px;
+            font-size: 1em;
+            transition: border-color 0.3s;
+        }
+        
+        .form-group input:focus,
+        .form-group select:focus {
+            outline: none;
+            border-color: #667eea;
+        }
+        
+        .btn {
+            padding: 12px 30px;
+            border: none;
+            border-radius: 8px;
+            cursor: pointer;
+            font-size: 1.1em;
+            font-weight: 600;
+            transition: all 0.3s;
+            display: inline-flex;
+            align-items: center;
+            gap: 8px;
+        }
+        
+        .btn-primary {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
+        }
+        
+        .btn-primary:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 7px 20px rgba(102, 126, 234, 0.6);
+        }
+        
+        .btn-success {
+            background: #4caf50;
+            color: white;
+        }
+        
+        .btn-success:hover {
+            background: #45a049;
+        }
+        
+        .btn-danger {
+            background: #f44336;
+            color: white;
+        }
+        
+        .btn-danger:hover {
+            background: #d32f2f;
+        }
+        
+        .btn-secondary {
+            background: #6c757d;
+            color: white;
+        }
+        
+        .btn-secondary:hover {
+            background: #5a6268;
+        }
+        
+        /* Grid layouts */
+        .grid-2 {
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 20px;
+        }
+        
+        .grid-3 {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
+            gap: 20px;
+        }
+        
+        @media (max-width: 768px) {
+            .grid-2 {
+                grid-template-columns: 1fr;
+            }
+        }
+        
+        /* Cards */
+        .card {
+            background: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+            border: 2px solid #e0e0e0;
+            transition: all 0.3s;
+        }
+        
+        .card:hover {
+            border-color: #667eea;
+            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.2);
+        }
+        
+        /* Stats cards */
+        .stat-card {
+            background: white;
+            padding: 25px;
+            border-radius: 15px;
+            box-shadow: 0 5px 15px rgba(0, 0, 0, 0.1);
+            text-align: center;
+            transition: transform 0.3s;
+        }
+        
+        .stat-card:hover {
+            transform: translateY(-5px);
+        }
+        
+        .stat-card .icon {
+            font-size: 3em;
+            margin-bottom: 15px;
+        }
+        
+        .stat-card .value {
+            font-size: 2.5em;
+            font-weight: bold;
+            color: #667eea;
+            margin-bottom: 5px;
+        }
+        
+        .stat-card .label {
+            color: #666;
+            font-size: 1.1em;
+        }
+        
+        /* Alert boxes */
+        .alert {
+            padding: 15px 20px;
+            border-radius: 8px;
+            margin-bottom: 20px;
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+        
+        .alert-info {
+            background: #e3f2fd;
+            border-left: 4px solid #2196f3;
+            color: #1565c0;
+        }
+        
+        .alert-success {
+            background: #e8f5e9;
+            border-left: 4px solid #4caf50;
+            color: #2e7d32;
+        }
+        
+        .alert-warning {
+            background: #fff3cd;
+            border-left: 4px solid #ffc107;
+            color: #856404;
+        }
+        
+        .alert-danger {
+            background: #ffebee;
+            border-left: 4px solid #f44336;
+            color: #c62828;
+        }
+        
+        /* Progress bar */
+        .progress {
+            width: 100%;
+            height: 30px;
+            background: #e0e0e0;
+            border-radius: 15px;
+            overflow: hidden;
+            margin: 20px 0;
+        }
+        
+        .progress-bar {
+            height: 100%;
+            background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+            transition: width 0.3s;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: 600;
+        }
+        
+        /* Map styles */
+        #trainMap, #predictMap {
+            height: 500px;
+            border-radius: 10px;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+        }
+        
+        /* Loading spinner */
+        .loading {
+            text-align: center;
+            padding: 40px;
+            color: #666;
+        }
+        
+        .spinner {
+            border: 4px solid #f3f3f3;
+            border-top: 4px solid #667eea;
+            border-radius: 50%;
+            width: 40px;
+            height: 40px;
+            animation: spin 1s linear infinite;
+            margin: 0 auto 20px;
+        }
+        
+        @keyframes spin {
+            0% { transform: rotate(0deg); }
+            100% { transform: rotate(360deg); }
+        }
+        
+        /* Status badge */
+        .status-badge {
+            display: inline-block;
+            padding: 6px 12px;
+            border-radius: 20px;
+            font-size: 0.9em;
+            font-weight: 600;
+        }
+        
+        .status-badge.running {
+            background: #d4edda;
+            color: #155724;
+        }
+        
+        .status-badge.completed {
+            background: #cce5ff;
+            color: #004085;
+        }
+        
+        .status-badge.error {
+            background: #f8d7da;
+            color: #721c24;
+        }
+        
+        /* Table */
+        table {
+            width: 100%;
+            border-collapse: collapse;
+            margin-top: 20px;
+        }
+        
+        th, td {
+            padding: 12px;
+            text-align: left;
+            border-bottom: 1px solid #e0e0e0;
+        }
+        
+        th {
+            background: #f5f5f5;
+            font-weight: 600;
+            color: #333;
+        }
+        
+        tr:hover {
+            background: #f9f9f9;
+        }
+        
+        /* Footer */
+        .footer {
+            background: white;
+            padding: 20px;
+            border-radius: 15px;
+            box-shadow: 0 5px 20px rgba(0, 0, 0, 0.15);
+            margin-top: 20px;
+            text-align: center;
+            color: #666;
+        }
+    </style>
+</head>
+<body>
+    <div class="main-container">
+        <!-- Header -->
+        <div class="header">
+            <h1>🛰️ Land Classification System</h1>
+            <p>Hệ thống phân loại đất từ xa sử dụng Sentinel-2 & Sentinel-1</p>
+        </div>
+        
+        <!-- Navigation Tabs -->
+        <div class="nav-tabs">
+            <button class="nav-tab active" onclick="switchTab('home')">
+                🏠 Trang Chủ
+            </button>
+            <button class="nav-tab" onclick="switchTab('train')">
+                🎓 Training
+            </button>
+            <button class="nav-tab" onclick="switchTab('predict')">
+                🗺️ Prediction
+            </button>
+            <button class="nav-tab" onclick="switchTab('dashboard')">
+                📊 Dashboard
+            </button>
+            <button class="nav-tab" onclick="switchTab('models')">
+                🤖 Models
+            </button>
+            <button class="nav-tab" onclick="switchTab('reports')">
+                📄 Reports
+            </button>
+            <button class="nav-tab" onclick="switchTab('batch')">
+                🔄 Batch Processing
+            </button>
+        </div>
+        
+        <!-- Tab Content: Home -->
+        <div id="home" class="tab-content active">
+            <div class="content-wrapper">
+                <div class="section">
+                    <h2>🎯 Chào mừng đến với Land Classification System</h2>
+                    <p style="font-size: 1.2em; color: #666; margin-bottom: 30px;">
+                        Nền tảng phân loại đất tự động sử dụng dữ liệu vệ tinh Sentinel và Machine Learning
+                    </p>
+                </div>
+                
+                <div class="grid-3">
+                    <div class="stat-card">
+                        <div class="icon">🎓</div>
+                        <div class="value" id="homeModelsCount">-</div>
+                        <div class="label">Models Trained</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="icon">🗺️</div>
+                        <div class="value" id="homePredictionsCount">-</div>
+                        <div class="label">Predictions Created</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="icon">📄</div>
+                        <div class="value" id="homeReportsCount">-</div>
+                        <div class="label">Reports Generated</div>
+                    </div>
+                </div>
+                
+                <div class="section" style="margin-top: 40px;">
+                    <h3>🚀 Bắt đầu nhanh</h3>
+                    <div class="grid-2">
+                        <div class="card">
+                            <h4 style="color: #667eea; margin-bottom: 10px;">1️⃣ Training Model</h4>
+                            <p style="color: #666; margin-bottom: 15px;">
+                                Train model mới với dữ liệu Sentinel-2/1 và shapefile training data
+                            </p>
+                            <button class="btn btn-primary" onclick="switchTab('train')">
+                                🎓 Bắt đầu Training
+                            </button>
+                        </div>
+                        <div class="card">
+                            <h4 style="color: #667eea; margin-bottom: 10px;">2️⃣ Prediction</h4>
+                            <p style="color: #666; margin-bottom: 15px;">
+                                Sử dụng model đã train để phân loại khu vực mới
+                            </p>
+                            <button class="btn btn-success" onclick="switchTab('predict')">
+                                🗺️ Bắt đầu Prediction
+                            </button>
+                        </div>
+                        <div class="card">
+                            <h4 style="color: #667eea; margin-bottom: 10px;">3️⃣ Dashboard</h4>
+                            <p style="color: #666; margin-bottom: 15px;">
+                                Xem thống kê, biểu đồ accuracy trends và so sánh models
+                            </p>
+                            <button class="btn btn-secondary" onclick="switchTab('dashboard')">
+                                📊 Mở Dashboard
+                            </button>
+                        </div>
+                        <div class="card">
+                            <h4 style="color: #667eea; margin-bottom: 10px;">4️⃣ Batch Processing</h4>
+                            <p style="color: #666; margin-bottom: 15px;">
+                                Predict nhiều khu vực cùng lúc với CSV file
+                            </p>
+                            <button class="btn btn-secondary" onclick="switchTab('batch')">
+                                🔄 Batch Processing
+                            </button>
+                        </div>
+                    </div>
+                </div>
+                
+                <div class="section" style="margin-top: 40px;">
+                    <h3>📚 Tài liệu & Hướng dẫn</h3>
+                    <div class="alert alert-info">
+                        <span style="font-size: 1.5em;">ℹ️</span>
+                        <div>
+                            <strong>API Documentation:</strong> 
+                            <a href="/docs" target="_blank" style="color: #1565c0; text-decoration: none; font-weight: 600;">
+                                /docs
+                            </a>
+                            <br>
+                            <strong>Features Guide:</strong> Xem file NEW_FEATURES.md để biết chi tiết
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+        
+        <!-- Tab Content: Training -->
+        <div id="train" class="tab-content">
+            <div class="content-wrapper">
+                <iframe src="/training" style="width: 100%; height: 800px; border: none; border-radius: 10px;"></iframe>
+            </div>
+        </div>
+        
+        <!-- Tab Content: Prediction -->
+        <div id="predict" class="tab-content">
+            <div class="content-wrapper">
+                <iframe src="/prediction" style="width: 100%; height: 800px; border: none; border-radius: 10px;"></iframe>
+            </div>
+        </div>
+        
+        <!-- Tab Content: Dashboard -->
+        <div id="dashboard" class="tab-content">
+            <div class="content-wrapper">
+                <iframe src="/dashboard" style="width: 100%; height: 800px; border: none; border-radius: 10px;"></iframe>
+            </div>
+        </div>
+        
+        <!-- Tab Content: Models -->
+        <div id="models" class="tab-content">
+            <div class="content-wrapper">
+                <div class="section">
+                    <h2>🤖 Model Management</h2>
+                    <p style="color: #666; margin-bottom: 20px;">Quản lý các models đã train</p>
+                </div>
+                
+                <div id="modelsLoading" class="loading">
+                    <div class="spinner"></div>
+                    <p>Đang tải danh sách models...</p>
+                </div>
+                
+                <div id="modelsList" style="display: none;">
+                    <table>
+                        <thead>
+                            <tr>
+                                <th>Tên File</th>
+                                <th>Model Type</th>
+                                <th>Accuracy</th>
+                                <th>Ngày Tạo</th>
+                                <th>Kích Thước</th>
+                                <th>Thao Tác</th>
+                            </tr>
+                        </thead>
+                        <tbody id="modelsTableBody"></tbody>
+                    </table>
+                </div>
+            </div>
+        </div>
+        
+        <!-- Tab Content: Reports -->
+        <div id="reports" class="tab-content">
+            <div class="content-wrapper">
+                <div class="section">
+                    <h2>📄 Reports Management</h2>
+                    <p style="color: #666; margin-bottom: 20px;">Quản lý các báo cáo đã tạo</p>
+                </div>
+                
+                <div id="reportsLoading" class="loading">
+                    <div class="spinner"></div>
+                    <p>Đang tải danh sách reports...</p>
+                </div>
+                
+                <div id="reportsList" style="display: none;">
+                    <table>
+                        <thead>
+                            <tr>
+                                <th>Tên File</th>
+                                <th>Loại</th>
+                                <th>Ngày Tạo</th>
+                                <th>Kích Thước</th>
+                                <th>Thao Tác</th>
+                            </tr>
+                        </thead>
+                        <tbody id="reportsTableBody"></tbody>
+                    </table>
+                </div>
+            </div>
+        </div>
+        
+        <!-- Tab Content: Batch Processing -->
+        <div id="batch" class="tab-content">
+            <div class="content-wrapper">
+                <div class="section">
+                    <h2>🔄 Batch Processing</h2>
+                    <p style="color: #666; margin-bottom: 20px;">Predict nhiều khu vực cùng lúc</p>
+                </div>
+                
+                <div class="alert alert-info">
+                    <span style="font-size: 1.5em;">ℹ️</span>
+                    <div>
+                        <strong>CSV Format:</strong> name,min_lon,min_lat,max_lon,max_lat,start_date,end_date,max_scenes,cloud_cover,resolution
+                        <br>
+                        <strong>File mẫu:</strong> batch_regions_example.csv
+                    </div>
+                </div>
+                
+                <div class="grid-2">
+                    <div class="section">
+                        <h3>📁 Upload CSV</h3>
+                        <div class="form-group">
+                            <label>Chọn file CSV:</label>
+                            <input type="file" id="batchCSVFile" accept=".csv" onchange="handleBatchCSV(event)">
+                        </div>
+                        
+                        <div class="form-group">
+                            <label>Chọn Model:</label>
+                            <select id="batchModelSelect">
+                                <option value="">Đang tải...</option>
+                            </select>
+                        </div>
+                        
+                        <button class="btn btn-primary" onclick="startBatch()">
+                            🚀 Start Batch Prediction
+                        </button>
+                    </div>
+                    
+                    <div class="section">
+                        <h3>📊 Queue Status</h3>
+                        <div class="grid-2">
+                            <div class="stat-card">
+                                <div class="value" id="batchQueued">0</div>
+                                <div class="label">⏳ Queued</div>
+                            </div>
+                            <div class="stat-card">
+                                <div class="value" id="batchRunning">0</div>
+                                <div class="label">▶️ Running</div>
+                            </div>
+                            <div class="stat-card">
+                                <div class="value" id="batchCompleted">0</div>
+                                <div class="label">✅ Completed</div>
+                            </div>
+                            <div class="stat-card">
+                                <div class="value" id="batchFailed">0</div>
+                                <div class="label">❌ Failed</div>
+                            </div>
+                        </div>
+                    </div>
+                </div>
+                
+                <div class="section" style="margin-top: 30px;">
+                    <h3>📋 Jobs List</h3>
+                    <div id="batchJobsList"></div>
+                </div>
+            </div>
+        </div>
+        
+        <!-- Footer -->
+        <div class="footer">
+            <p>🛰️ Land Classification System v2.0 | Powered by Sentinel-2/1 & Microsoft Planetary Computer</p>
+        </div>
+    </div>
+    
+    <!-- Scripts -->
+    <script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
+    <script src="https://unpkg.com/leaflet-draw@1.0.4/dist/leaflet.draw.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
+    
+    <script>
+        let batchCSVData = [];
+        let refreshInterval = null;
+        
+        // Tab switching
+        function switchTab(tabName) {
+            // Update tab buttons
+            document.querySelectorAll('.nav-tab').forEach(tab => {
+                tab.classList.remove('active');
+            });
+            event.target.classList.add('active');
+            
+            // Update tab content
+            document.querySelectorAll('.tab-content').forEach(content => {
+                content.classList.remove('active');
+            });
+            document.getElementById(tabName).classList.add('active');
+            
+            // Load data for specific tabs
+            if (tabName === 'home') {
+                loadHomeStats();
+            } else if (tabName === 'models') {
+                loadModelsList();
+            } else if (tabName === 'reports') {
+                loadReportsList();
+            } else if (tabName === 'batch') {
+                loadBatchModels();
+                loadBatchStatus();
+                startBatchRefresh();
+            } else {
+                stopBatchRefresh();
+            }
+        }
+        
+        // Load home statistics
+        async function loadHomeStats() {
+            try {
+                const response = await fetch('/api/dashboard/statistics');
+                const data = await response.json();
+                
+                document.getElementById('homeModelsCount').textContent = data.models.total;
+                document.getElementById('homePredictionsCount').textContent = data.predictions.total;
+                document.getElementById('homeReportsCount').textContent = data.reports.total;
+            } catch (error) {
+                console.error('Error loading home stats:', error);
+            }
+        }
+        
+        // Load models list
+        async function loadModelsList() {
+            const loading = document.getElementById('modelsLoading');
+            const list = document.getElementById('modelsList');
+            const tbody = document.getElementById('modelsTableBody');
+            
+            loading.style.display = 'block';
+            list.style.display = 'none';
+            
+            try {
+                const response = await fetch('/api/models/list');
+                const data = await response.json();
+                
+                tbody.innerHTML = '';
+                
+                data.models.forEach(model => {
+                    const row = document.createElement('tr');
+                    const accuracy = model.info.metrics?.accuracy 
+                        ? (model.info.metrics.accuracy * 100).toFixed(2) + '%' 
+                        : 'N/A';
+                    
+                    row.innerHTML = `
+                        <td><strong>${model.filename}</strong></td>
+                        <td>${model.info.model_type || 'N/A'}</td>
+                        <td><span style="color: #4caf50; font-weight: 600;">${accuracy}</span></td>
+                        <td>${new Date(model.created).toLocaleString('vi-VN')}</td>
+                        <td>${model.size_mb} MB</td>
+                        <td>
+                            <button class="btn btn-primary" style="padding: 8px 16px; font-size: 0.9em;" 
+                                    onclick="window.open('/api/reports/view/training_report_${model.filename.replace('.joblib', '')}.html', '_blank')">
+                                📄 Report
+                            </button>
+                        </td>
+                    `;
+                    tbody.appendChild(row);
+                });
+                
+                loading.style.display = 'none';
+                list.style.display = 'block';
+            } catch (error) {
+                console.error('Error loading models:', error);
+                loading.innerHTML = '<p style="color: #f44336;">❌ Lỗi khi tải danh sách models</p>';
+            }
+        }
+        
+        // Load reports list
+        async function loadReportsList() {
+            const loading = document.getElementById('reportsLoading');
+            const list = document.getElementById('reportsList');
+            const tbody = document.getElementById('reportsTableBody');
+            
+            loading.style.display = 'block';
+            list.style.display = 'none';
+            
+            try {
+                const response = await fetch('/api/reports/list');
+                const data = await response.json();
+                
+                tbody.innerHTML = '';
+                
+                data.reports.forEach(report => {
+                    const row = document.createElement('tr');
+                    const typeIcon = report.type === 'training' ? '🎓' : '🗺️';
+                    
+                    row.innerHTML = `
+                        <td><strong>${report.filename}</strong></td>
+                        <td>${typeIcon} ${report.type}</td>
+                        <td>${new Date(report.created).toLocaleString('vi-VN')}</td>
+                        <td>${report.size_kb} KB</td>
+                        <td>
+                            <button class="btn btn-primary" style="padding: 8px 16px; font-size: 0.9em;" 
+                                    onclick="window.open('${report.view_url}', '_blank')">
+                                👁️ Xem
+                            </button>
+                            <button class="btn btn-success" style="padding: 8px 16px; font-size: 0.9em; margin-left: 5px;" 
+                                    onclick="window.location.href='${report.download_url}'">
+                                💾 Download
+                            </button>
+                        </td>
+                    `;
+                    tbody.appendChild(row);
+                });
+                
+                loading.style.display = 'none';
+                list.style.display = 'block';
+            } catch (error) {
+                console.error('Error loading reports:', error);
+                loading.innerHTML = '<p style="color: #f44336;">❌ Lỗi khi tải danh sách reports</p>';
+            }
+        }
+        
+        // Batch processing functions
+        async function loadBatchModels() {
+            try {
+                const response = await fetch('/api/models/list');
+                const data = await response.json();
+                
+                const select = document.getElementById('batchModelSelect');
+                select.innerHTML = '<option value="">Chọn model...</option>';
+                
+                data.models.forEach(model => {
+                    const option = document.createElement('option');
+                    option.value = model.filename;
+                    option.textContent = `${model.filename} (${model.created})`;
+                    select.appendChild(option);
+                });
+            } catch (error) {
+                console.error('Error loading batch models:', error);
+            }
+        }
+        
+        function handleBatchCSV(event) {
+            const file = event.target.files[0];
+            if (!file) return;
+            
+            const reader = new FileReader();
+            reader.onload = function(e) {
+                const text = e.target.result;
+                const lines = text.trim().split('\n');
+                batchCSVData = [];
+                
+                for (let i = 1; i < lines.length; i++) {
+                    const parts = lines[i].split(',');
+                    if (parts.length >= 5) {
+                        batchCSVData.push({
+                            name: parts[0].trim(),
+                            min_lon: parseFloat(parts[1]),
+                            min_lat: parseFloat(parts[2]),
+                            max_lon: parseFloat(parts[3]),
+                            max_lat: parseFloat(parts[4]),
+                            start_date: parts[5]?.trim() || "2023-03-01",
+                            end_date: parts[6]?.trim() || "2023-05-31",
+                            max_scenes: parseInt(parts[7]) || 12,
+                            cloud_cover: parseInt(parts[8]) || 30,
+                            resolution: parseInt(parts[9]) || 20
+                        });
+                    }
+                }
+                
+                alert(`✅ Đã tải ${batchCSVData.length} khu vực từ CSV`);
+            };
+            reader.readAsText(file);
+        }
+        
+        async function startBatch() {
+            const modelFilename = document.getElementById('batchModelSelect').value;
+            
+            if (!modelFilename) {
+                alert('❌ Vui lòng chọn model');
+                return;
+            }
+            
+            if (batchCSVData.length === 0) {
+                alert('❌ Vui lòng upload file CSV trước');
+                return;
+            }
+            
+            try {
+                const response = await fetch('/api/batch/start', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify({
+                        model_filename: modelFilename,
+                        items: batchCSVData,
+                        auto_retry: true,
+                        max_retries: 3
+                    })
+                });
+                
+                const result = await response.json();
+                alert(`✅ ${result.message}`);
+                loadBatchStatus();
+            } catch (error) {
+                console.error('Error starting batch:', error);
+                alert('❌ Lỗi khi bắt đầu batch prediction');
+            }
+        }
+        
+        async function loadBatchStatus() {
+            try {
+                const response = await fetch('/api/batch/status');
+                const data = await response.json();
+                
+                document.getElementById('batchQueued').textContent = data.queue.queued;
+                document.getElementById('batchRunning').textContent = data.queue.running;
+                document.getElementById('batchCompleted').textContent = data.queue.completed;
+                document.getElementById('batchFailed').textContent = data.queue.failed;
+                
+                // Display jobs
+                const jobsList = document.getElementById('batchJobsList');
+                const allJobs = [
+                    ...data.jobs.running,
+                    ...data.jobs.queued,
+                    ...data.jobs.recent_completed.slice(0, 5)
+                ];
+                
+                if (allJobs.length === 0) {
+                    jobsList.innerHTML = '<p style="text-align: center; color: #666;">Chưa có job nào</p>';
+                    return;
+                }
+                
+                jobsList.innerHTML = allJobs.map(job => `
+                    <div class="card" style="margin-bottom: 15px;">
+                        <div style="display: flex; justify-content: space-between; align-items: center;">
+                            <strong>${job.name}</strong>
+                            <span class="status-badge ${job.status}">${job.status.toUpperCase()}</span>
+                        </div>
+                        <p style="color: #666; margin: 10px 0;">
+                            📍 [${job.config.min_lon.toFixed(2)}, ${job.config.min_lat.toFixed(2)}] → 
+                            [${job.config.max_lon.toFixed(2)}, ${job.config.max_lat.toFixed(2)}]
+                        </p>
+                        ${job.error ? `<p style="color: #f44336;">⚠️ ${job.error}</p>` : ''}
+                    </div>
+                `).join('');
+            } catch (error) {
+                console.error('Error loading batch status:', error);
+            }
+        }
+        
+        function startBatchRefresh() {
+            if (refreshInterval) return;
+            refreshInterval = setInterval(loadBatchStatus, 3000);
+        }
+        
+        function stopBatchRefresh() {
+            if (refreshInterval) {
+                clearInterval(refreshInterval);
+                refreshInterval = null;
+            }
+        }
+        
+        // Initialize on page load
+        window.onload = function() {
+            loadHomeStats();
+        };
+        
+        // Cleanup on page unload
+        window.onbeforeunload = function() {
+            stopBatchRefresh();
+        };
+    </script>
+</body>
+</html>

prediction_interface.html

@@ -0,0 +1,803 @@
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Prediction Interface - Land Classification</title>
+    
+    <!-- Leaflet CSS -->
+    <link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css" />
+    <link rel="stylesheet" href="https://unpkg.com/leaflet-draw@1.0.4/dist/leaflet.draw.css" />
+    
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            padding: 20px;
+            min-height: 100vh;
+        }
+        
+        .container {
+            max-width: 1400px;
+            margin: 0 auto;
+            background: white;
+            border-radius: 20px;
+            box-shadow: 0 20px 60px rgba(0,0,0,0.3);
+            overflow: hidden;
+        }
+        
+        .header {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            padding: 30px;
+            text-align: center;
+        }
+        
+        .header h1 {
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }
+        
+        .header p {
+            opacity: 0.9;
+            font-size: 1.1em;
+        }
+        
+        .content {
+            padding: 30px;
+            display: grid;
+            grid-template-columns: 1fr 1fr;
+            gap: 30px;
+        }
+        
+        #predictMap {
+            height: 500px;
+            border-radius: 10px;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+        }
+        
+        .map-container {
+            grid-column: 1 / -1;
+        }
+        
+        .map-instructions {
+            background: #e3f2fd;
+            padding: 15px;
+            border-radius: 10px;
+            margin-bottom: 15px;
+            border-left: 4px solid #2196f3;
+        }
+        
+        .map-instructions h3 {
+            color: #1976d2;
+            margin-bottom: 8px;
+        }
+        
+        .map-instructions p {
+            color: #555;
+            margin: 5px 0;
+        }
+        
+        .section {
+            margin-bottom: 30px;
+            padding: 20px;
+            background: #f8f9fa;
+            border-radius: 10px;
+        }
+        
+        .section h2 {
+            color: #667eea;
+            margin-bottom: 15px;
+            font-size: 1.5em;
+        }
+        
+        .form-group {
+            margin-bottom: 15px;
+        }
+        
+        .form-group label {
+            display: block;
+            margin-bottom: 5px;
+            color: #333;
+            font-weight: 600;
+        }
+        
+        .form-group input, .form-group select {
+            width: 100%;
+            padding: 10px;
+            border: 2px solid #e0e0e0;
+            border-radius: 5px;
+            font-size: 1em;
+            transition: border-color 0.3s;
+        }
+        
+        .form-group input:focus, .form-group select:focus {
+            outline: none;
+            border-color: #667eea;
+        }
+        
+        .form-row {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 15px;
+        }
+        
+        .btn {
+            padding: 12px 30px;
+            border: none;
+            border-radius: 5px;
+            font-size: 1em;
+            font-weight: 600;
+            cursor: pointer;
+            transition: all 0.3s;
+            margin-right: 10px;
+        }
+        
+        .btn-primary {
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+        }
+        
+        .btn-primary:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
+        }
+        
+        .btn-success {
+            background: #28a745;
+            color: white;
+        }
+        
+        .btn-success:hover {
+            background: #218838;
+        }
+        
+        .btn-secondary {
+            background: #6c757d;
+            color: white;
+        }
+        
+        .btn:disabled {
+            opacity: 0.5;
+            cursor: not-allowed;
+        }
+        
+        .status-box {
+            padding: 20px;
+            background: white;
+            border-radius: 10px;
+            border-left: 5px solid #667eea;
+            margin-bottom: 20px;
+        }
+        
+        .status-box.success {
+            border-left-color: #28a745;
+            background: #d4edda;
+        }
+        
+        .status-box.error {
+            border-left-color: #dc3545;
+            background: #f8d7da;
+        }
+        
+        .status-box.predicting {
+            border-left-color: #ffc107;
+            background: #fff3cd;
+        }
+        
+        .progress {
+            height: 30px;
+            background: #e0e0e0;
+            border-radius: 15px;
+            overflow: hidden;
+            margin: 10px 0;
+        }
+        
+        .progress-bar {
+            height: 100%;
+            background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+            width: 0%;
+            transition: width 0.3s;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            color: white;
+            font-weight: 600;
+        }
+        
+        .metric-card {
+            background: white;
+            padding: 15px;
+            border-radius: 10px;
+            box-shadow: 0 2px 5px rgba(0,0,0,0.1);
+            text-align: center;
+        }
+        
+        .metric-card h4 {
+            color: #667eea;
+            margin-bottom: 10px;
+        }
+        
+        .metric-card .value {
+            font-size: 2em;
+            font-weight: bold;
+            color: #333;
+        }
+        
+        .alert {
+            padding: 15px;
+            border-radius: 5px;
+            margin-bottom: 20px;
+        }
+        
+        .alert-info {
+            background: #d1ecf1;
+            border-left: 4px solid #0c5460;
+            color: #0c5460;
+        }
+        
+        .alert-success {
+            background: #d4edda;
+            border-left: 4px solid #155724;
+            color: #155724;
+        }
+        
+        .alert-danger {
+            background: #f8d7da;
+            border-left: 4px solid #721c24;
+            color: #721c24;
+        }
+        
+        .predictions-list {
+            max-height: 400px;
+            overflow-y: auto;
+        }
+        
+        .prediction-item {
+            background: white;
+            padding: 15px;
+            border-radius: 8px;
+            margin-bottom: 10px;
+            border-left: 4px solid #667eea;
+            display: flex;
+            justify-content: space-between;
+            align-items: center;
+        }
+        
+        .prediction-item:hover {
+            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        }
+        
+        @media (max-width: 768px) {
+            .content {
+                grid-template-columns: 1fr;
+            }
+            
+            .form-row {
+                grid-template-columns: 1fr;
+            }
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🗺️ Prediction Interface</h1>
+            <p>Phân loại đất cho khu vực mới sử dụng model đã train</p>
+        </div>
+        
+        <div class="content">
+            <!-- Map Section -->
+            <div class="map-container">
+                <div class="map-instructions">
+                    <h3>📍 Chọn khu vực để predict</h3>
+                    <p>✏️ Click vào nút hình vuông bên phải để vẽ bbox</p>
+                    <p>🖱️ Kéo và thả để tạo vùng muốn phân loại</p>
+                    <p>🔄 Có thể chỉnh sửa sau khi vẽ</p>
+                </div>
+                <div id="predictMap"></div>
+            </div>
+            
+            <!-- Model Selection -->
+            <div class="section">
+                <h2>🤖 Chọn Model</h2>
+                <div class="form-group">
+                    <label for="modelSelect">Model đã train:</label>
+                    <select id="modelSelect">
+                        <option value="">Đang tải...</option>
+                    </select>
+                </div>
+                <!-- Cache selection dropdown -->
+                <div class="form-group" style="margin-top:15px;">
+                    <label for="cacheSelect">Chọn cache dữ liệu đầu vào:</label>
+                    <select id="cacheSelect">
+                        <option value="">-- Không dùng cache --</option>
+                    </select>
+                </div>
+                <div id="modelInfo" style="display: none; background: #e8f5e9; padding: 15px; border-radius: 8px; margin-top: 15px;">
+                    <h4 style="color: #2e7d32; margin-bottom: 10px;">📊 Thông tin Model</h4>
+                    <p><strong>Type:</strong> <span id="modelType">-</span></p>
+                    <p><strong>Accuracy:</strong> <span id="modelAccuracy">-</span></p>
+                    <p><strong>Training Date:</strong> <span id="modelDate">-</span></p>
+                </div>
+            </div>
+            
+            <!-- Time & Data Configuration -->
+            <div class="section">
+                <h2>⏰ Thời gian & Dữ liệu</h2>
+                
+                <div class="form-row">
+                    <div class="form-group">
+                        <label for="predStartDate">Từ ngày:</label>
+                        <input type="date" id="predStartDate" value="2023-03-01">
+                    </div>
+                    <div class="form-group">
+                        <label for="predEndDate">Đến ngày:</label>
+                        <input type="date" id="predEndDate" value="2023-05-31">
+                    </div>
+                </div>
+                
+                <div class="form-row">
+                    <div class="form-group">
+                        <label for="predMaxScenes">Max Scenes:</label>
+                        <input type="number" id="predMaxScenes" value="12" min="1" max="100">
+                    </div>
+                    <div class="form-group">
+                        <label for="predCloudCover">Cloud Cover (%):</label>
+                        <input type="number" id="predCloudCover" value="30" min="0" max="100">
+                    </div>
+                </div>
+                
+                <div class="form-group">
+                    <label for="predResolution">Resolution:</label>
+                    <select id="predResolution">
+                        <option value="10">10m (Chi tiết cao - Chậm)</option>
+                        <option value="20" selected>20m (Cân bằng)</option>
+                    </select>
+                </div>
+                
+                <button class="btn btn-primary" onclick="startPrediction()" id="predictBtn">
+                    🚀 Start Prediction
+                </button>
+            </div>
+            
+            <!-- Status Section -->
+            <div class="section" style="grid-column: 1 / -1;">
+                <h2>📊 Trạng thái Prediction</h2>
+                
+                <div id="predictionStatus" class="status-box" style="display: none;">
+                    <h3>⏳ Đang xử lý...</h3>
+                    <p id="predictionProgress">Đang khởi tạo...</p>
+                    <div class="progress">
+                        <div class="progress-bar" id="predictionProgressBar">0%</div>
+                    </div>
+                </div>
+                
+                <div id="predictionResult" style="display: none;">
+                    <div class="alert alert-success">
+                        <h3>✅ Prediction hoàn thành!</h3>
+                        <p><strong>Output file:</strong> <span id="resultFile"></span></p>
+                        <p><strong>Shape:</strong> <span id="resultShape"></span></p>
+                        <p><strong>Unique classes:</strong> <span id="resultClasses"></span></p>
+                        
+                        <!-- PNG Preview -->
+                        <div id="pngPreviewContainer" style="display: none; margin: 20px 0;">
+                            <h4 style="margin-bottom: 10px;">🖼️ Preview:</h4>
+                            <img id="pngPreview" style="max-width: 100%; border-radius: 8px; box-shadow: 0 4px 15px rgba(0,0,0,0.2);" />
+                        </div>
+                        
+                        <div style="margin-top: 15px;">
+                            <button class="btn btn-success" onclick="downloadPrediction()">
+                                💾 Download GeoTIFF
+                            </button>
+                            <button class="btn btn-secondary" onclick="viewReport()">
+                                📄 View Report
+                            </button>
+                        </div>
+                    </div>
+                </div>
+                
+                <div id="predictionError" class="alert alert-danger" style="display: none;">
+                    <h3>❌ Lỗi</h3>
+                    <p id="errorMessage"></p>
+                </div>
+            </div>
+            
+            <!-- Previous Predictions -->
+            <div class="section" style="grid-column: 1 / -1;">
+                <h2>📋 Predictions đã tạo</h2>
+                <div id="predictionsList" class="predictions-list">
+                    <p style="text-align: center; color: #666;">Đang tải...</p>
+                </div>
+            </div>
+        </div>
+    </div>
+    
+    <!-- Scripts -->
+    <script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
+    <script src="https://unpkg.com/leaflet-draw@1.0.4/dist/leaflet.draw.js"></script>
+    
+    <script>
+        // Map setup
+        let map, drawnItems, drawControl;
+        let selectedBbox = null;
+        let currentPredictionFile = null;
+        let currentReportFile = null;
+        let statusCheckInterval = null;
+        
+        // Initialize map
+        function initMap() {
+            map = L.map('predictMap').setView([9.5, 105.9], 9);
+            
+            L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
+                attribution: '© OpenStreetMap contributors'
+            }).addTo(map);
+            
+            // Initialize drawing
+            drawnItems = new L.FeatureGroup();
+            map.addLayer(drawnItems);
+            
+            drawControl = new L.Control.Draw({
+                draw: {
+                    rectangle: true,
+                    polygon: false,
+                    circle: false,
+                    marker: false,
+                    polyline: false,
+                    circlemarker: false
+                },
+                edit: {
+                    featureGroup: drawnItems,
+                    remove: true
+                }
+            });
+            map.addControl(drawControl);
+
+            // Handle drawing
+            map.on(L.Draw.Event.CREATED, function(event) {
+                drawnItems.clearLayers();
+                const layer = event.layer;
+                drawnItems.addLayer(layer);
+
+                const bounds = layer.getBounds();
+                let bbox = {
+                    min_lon: bounds.getWest(),
+                    min_lat: bounds.getSouth(),
+                    max_lon: bounds.getEast(),
+                    max_lat: bounds.getNorth()
+                };
+                
+                // Validate bbox (must be within valid geographic coordinates)
+                if (bbox.min_lon < -180 || bbox.max_lon > 180 || bbox.min_lat < -90 || bbox.max_lat > 90) {
+                    alert('❌ Bbox không hợp lệ! Vui lòng vẽ trong phạm vi bản đồ hợp lệ.\nKinh độ: -180 đến 180, Vĩ độ: -90 đến 90');
+                    drawnItems.clearLayers();
+                    return;
+                }
+                
+                selectedBbox = bbox;
+                // Cache bbox to localStorage
+                localStorage.setItem('prediction_bbox', JSON.stringify(selectedBbox));
+                console.log('Selected bbox:', selectedBbox);
+            });
+
+            // On load, restore bbox from cache if exists
+            const cachedBbox = localStorage.getItem('prediction_bbox');
+            if (cachedBbox) {
+                try {
+                    const bbox = JSON.parse(cachedBbox);
+                    
+                    // Validate bbox before restoring
+                    if (bbox.min_lon < -180 || bbox.max_lon > 180 || 
+                        bbox.min_lat < -90 || bbox.max_lat > 90) {
+                        console.warn('Cache bbox không hợp lệ, đã xóa:', bbox);
+                        localStorage.removeItem('prediction_bbox');
+                    } else {
+                        // Draw rectangle on map
+                        const bounds = [
+                            [bbox.min_lat, bbox.min_lon],
+                            [bbox.max_lat, bbox.max_lon]
+                        ];
+                        const rectangle = L.rectangle(bounds, {
+                            color: '#667eea',
+                            weight: 3,
+                            fillOpacity: 0.2
+                        });
+                        drawnItems.addLayer(rectangle);
+                        map.fitBounds(bounds);
+                        selectedBbox = bbox;
+                    }
+                } catch (e) {
+                    console.warn('Không thể khôi phục bbox từ cache:', e);
+                    localStorage.removeItem('prediction_bbox');
+                }
+            }
+        }
+        
+        // Load models list
+        async function loadModels() {
+            try {
+                const response = await fetch('/api/models/list');
+                const data = await response.json();
+                
+                const select = document.getElementById('modelSelect');
+                select.innerHTML = '<option value="">Chọn model...</option>';
+                
+                                // Chỉ lấy các file model thực sự (.joblib), loại bỏ các file có chứa '_info.joblib'
+                                data.models
+                                    .filter(m => m.filename.endsWith('.joblib') && !m.filename.includes('_info.joblib'))
+                                    .forEach(model => {
+                                        const option = document.createElement('option');
+                                        option.value = model.filename;
+                                        option.textContent = `${model.filename} - ${model.created}`;
+                                        option.dataset.info = JSON.stringify(model.info);
+                                        select.appendChild(option);
+                                    });
+
+                                // Auto-select first model đúng
+                                const firstJoblib = data.models.find(m => m.filename.endsWith('.joblib') && !m.filename.includes('_info.joblib'));
+                                if (firstJoblib) {
+                                        select.value = firstJoblib.filename;
+                                        updateModelInfo();
+                                }
+            } catch (error) {
+                console.error('Error loading models:', error);
+            }
+        }
+        
+        // Update model info display
+        function updateModelInfo() {
+            const select = document.getElementById('modelSelect');
+            const option = select.options[select.selectedIndex];
+            
+            if (option.dataset.info) {
+                const info = JSON.parse(option.dataset.info);
+                const infoDiv = document.getElementById('modelInfo');
+                
+                document.getElementById('modelType').textContent = info.model_type || 'N/A';
+                document.getElementById('modelAccuracy').textContent = info.metrics?.accuracy 
+                    ? (info.metrics.accuracy * 100).toFixed(2) + '%' 
+                    : 'N/A';
+                document.getElementById('modelDate').textContent = info.training_date || 'N/A';
+                
+                infoDiv.style.display = 'block';
+            } else {
+                document.getElementById('modelInfo').style.display = 'none';
+            }
+        }
+        
+        // Start prediction
+        async function startPrediction() {
+            if (!selectedBbox) {
+                alert('❌ Vui lòng vẽ bbox trên bản đồ trước!');
+                return;
+            }
+            
+            // Validate bbox before sending
+            if (selectedBbox.min_lon < -180 || selectedBbox.max_lon > 180 || 
+                selectedBbox.min_lat < -90 || selectedBbox.max_lat > 90) {
+                alert('❌ Bbox không hợp lệ! Vui lòng vẽ lại trong phạm vi bản đồ hợp lệ.');
+                drawnItems.clearLayers();
+                selectedBbox = null;
+                localStorage.removeItem('prediction_bbox');
+                return;
+            }
+            
+            const modelFilename = document.getElementById('modelSelect').value;
+            if (!modelFilename) {
+                alert('❌ Vui lòng chọn model!');
+                return;
+            }
+            
+            const config = {
+                model_filename: modelFilename,
+                min_lon: selectedBbox.min_lon,
+                min_lat: selectedBbox.min_lat,
+                max_lon: selectedBbox.max_lon,
+                max_lat: selectedBbox.max_lat,
+                start_date: document.getElementById('predStartDate').value,
+                end_date: document.getElementById('predEndDate').value,
+                max_scenes: parseInt(document.getElementById('predMaxScenes').value),
+                cloud_cover: parseInt(document.getElementById('predCloudCover').value),
+                resolution: parseInt(document.getElementById('predResolution').value)
+            };
+            
+            try {
+                document.getElementById('predictBtn').disabled = true;
+                document.getElementById('predictionStatus').style.display = 'block';
+                document.getElementById('predictionResult').style.display = 'none';
+                document.getElementById('predictionError').style.display = 'none';
+                
+                const response = await fetch('/api/prediction/start', {
+                    method: 'POST',
+                    headers: { 'Content-Type': 'application/json' },
+                    body: JSON.stringify(config)
+                });
+                
+                const result = await response.json();
+                
+                if (response.ok) {
+                    // Start monitoring status
+                    startStatusCheck();
+                } else {
+                    throw new Error(result.detail || 'Lỗi khi bắt đầu prediction');
+                }
+            } catch (error) {
+                console.error('Error starting prediction:', error);
+                document.getElementById('predictionError').style.display = 'block';
+                document.getElementById('errorMessage').textContent = error.message;
+                document.getElementById('predictBtn').disabled = false;
+            }
+        }
+        
+        // Check prediction status
+        async function checkStatus() {
+            try {
+                const response = await fetch('/api/prediction/status');
+                const status = await response.json();
+                
+                document.getElementById('predictionProgress').textContent = status.progress;
+                
+                // Update progress bar (estimate based on message)
+                let progress = 0;
+                if (status.progress.includes('khởi')) progress = 10;
+                else if (status.progress.includes('Sentinel-2')) progress = 30;
+                else if (status.progress.includes('NDVI')) progress = 50;
+                else if (status.progress.includes('Sentinel-1')) progress = 60;
+                else if (status.progress.includes('features')) progress = 70;
+                else if (status.progress.includes('dự đoán')) progress = 80;
+                else if (status.progress.includes('lưu')) progress = 90;
+                else if (status.progress.includes('Hoàn thành')) progress = 100;
+                
+                document.getElementById('predictionProgressBar').style.width = progress + '%';
+                document.getElementById('predictionProgressBar').textContent = progress + '%';
+                
+                if (!status.is_predicting) {
+                    stopStatusCheck();
+                    document.getElementById('predictBtn').disabled = false;
+                    
+                    if (status.error) {
+                        document.getElementById('predictionStatus').style.display = 'none';
+                        document.getElementById('predictionError').style.display = 'block';
+                        document.getElementById('errorMessage').textContent = status.error;
+                    } else if (status.result) {
+                        document.getElementById('predictionStatus').style.display = 'none';
+                        document.getElementById('predictionResult').style.display = 'block';
+                        
+                        currentPredictionFile = status.result.output_file;
+                        currentReportFile = status.result.report_filename;
+                        
+                        document.getElementById('resultFile').textContent = status.result.output_file;
+                        document.getElementById('resultShape').textContent = status.result.shape.join(' x ');
+                        document.getElementById('resultClasses').textContent = status.result.unique_classes.join(', ');
+                        
+                        // Show PNG preview if available
+                        if (status.result.png_file) {
+                            const pngFilename = status.result.png_file.split('/').pop();
+                            const previewImg = document.getElementById('pngPreview');
+                            const previewContainer = document.getElementById('pngPreviewContainer');
+                            
+                            previewImg.src = `/api/predictions/preview/${pngFilename}`;
+                            previewContainer.style.display = 'block';
+                        }
+                        
+                        // Reload predictions list
+                        loadPredictionsList();
+                    }
+                }
+            } catch (error) {
+                console.error('Error checking status:', error);
+            }
+        }
+        
+        // Start/stop status monitoring
+        function startStatusCheck() {
+            if (statusCheckInterval) clearInterval(statusCheckInterval);
+            statusCheckInterval = setInterval(checkStatus, 2000);
+        }
+        
+        function stopStatusCheck() {
+            if (statusCheckInterval) {
+                clearInterval(statusCheckInterval);
+                statusCheckInterval = null;
+            }
+        }
+        
+        // Download prediction
+        function downloadPrediction() {
+            if (currentPredictionFile) {
+                const filename = currentPredictionFile.split('/').pop();
+                window.location.href = `/api/predictions/download/${filename}`;
+            }
+        }
+        
+        // View report
+        function viewReport() {
+            if (currentReportFile) {
+                window.open(`/api/reports/view/${currentReportFile}`, '_blank');
+            }
+        }
+        
+        // Load predictions list
+        async function loadPredictionsList() {
+            try {
+                const response = await fetch('/api/predictions/list');
+                const data = await response.json();
+                
+                const listDiv = document.getElementById('predictionsList');
+                
+                if (data.predictions.length === 0) {
+                    listDiv.innerHTML = '<p style="text-align: center; color: #666;">Chưa có prediction nào</p>';
+                    return;
+                }
+                
+                listDiv.innerHTML = data.predictions.map(pred => `
+                    <div class="prediction-item">
+                        <div>
+                            <strong>${pred.filename}</strong>
+                            <br>
+                            <small style="color: #666;">
+                                ${new Date(pred.created).toLocaleString('vi-VN')} - ${pred.size_mb} MB
+                            </small>
+                        </div>
+                        <div>
+                            <button class="btn btn-success" style="padding: 8px 16px; font-size: 0.9em;"
+                                    onclick="window.location.href='${pred.download_url}'">
+                                💾 Download
+                            </button>
+                        </div>
+                    </div>
+                `).join('');
+            } catch (error) {
+                console.error('Error loading predictions:', error);
+            }
+        }
+        
+        // Load cache list
+        async function loadCacheList() {
+            try {
+                const response = await fetch('/api/cache/info');
+                const data = await response.json();
+                const select = document.getElementById('cacheSelect');
+                select.innerHTML = '<option value="">-- Không dùng cache --</option>';
+                if (data.files && data.files.length > 0) {
+                    data.files.forEach((file, idx) => {
+                        if (file.filename.startsWith('prediction_input_')) {
+                            let label = `#${idx+1} | ${file.filename}`;
+                            if (file.metadata && file.metadata.bbox) {
+                                label += ` | BBox: [${file.metadata.bbox.join(', ')}]`;
+                            }
+                            if (file.metadata && file.metadata.time_range) {
+                                label += ` | Time: ${file.metadata.time_range}`;
+                            }
+                            select.innerHTML += `<option value="${file.filename}">${label}</option>`;
+                        }
+                    });
+                }
+            } catch (e) {
+                console.warn('Không thể tải danh sách cache:', e);
+            }
+        }
+
+        // Initialize on page load
+        window.onload = function() {
+            initMap();
+            loadModels();
+            loadPredictionsList();
+            loadCacheList();
+            // Add event listener for model selection
+            document.getElementById('modelSelect').addEventListener('change', updateModelInfo);
+        };
+        
+        // Cleanup on page unload
+        window.onbeforeunload = function() {
+            stopStatusCheck();
+        };
+    </script>
+</body>
+</html>

report_generator.py

@@ -0,0 +1,786 @@
+"""
+Auto Report Generator for Land Classification
+Tự động tạo báo cáo HTML chi tiết sau training/prediction
+"""
+
+import json
+from datetime import datetime
+from pathlib import Path
+import base64
+import io
+
+# Optional: for generating charts
+try:
+    import matplotlib
+    matplotlib.use('Agg')  # Non-interactive backend
+    import matplotlib.pyplot as plt
+    import numpy as np
+    MATPLOTLIB_AVAILABLE = True
+except ImportError:
+    MATPLOTLIB_AVAILABLE = False
+
+
+def generate_confusion_matrix_image(conf_matrix, class_names):
+    """Tạo hình ảnh confusion matrix dạng base64"""
+    if not MATPLOTLIB_AVAILABLE:
+        return None
+    
+    try:
+        fig, ax = plt.subplots(figsize=(10, 8))
+        conf_matrix = np.array(conf_matrix)
+        
+        im = ax.imshow(conf_matrix, interpolation='nearest', cmap=plt.cm.Blues)
+        ax.figure.colorbar(im, ax=ax)
+        
+        ax.set(xticks=np.arange(len(class_names)),
+               yticks=np.arange(len(class_names)),
+               xticklabels=class_names, yticklabels=class_names,
+               title='Confusion Matrix',
+               ylabel='Thực tế (True)',
+               xlabel='Dự đoán (Predicted)')
+        
+        plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
+        
+        # Add text annotations
+        thresh = conf_matrix.max() / 2.
+        for i in range(len(class_names)):
+            for j in range(len(class_names)):
+                ax.text(j, i, format(conf_matrix[i, j], 'd'),
+                       ha="center", va="center",
+                       color="white" if conf_matrix[i, j] > thresh else "black")
+        
+        fig.tight_layout()
+        
+        # Convert to base64
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
+        buf.seek(0)
+        img_base64 = base64.b64encode(buf.read()).decode('utf-8')
+        plt.close(fig)
+        
+        return img_base64
+    except Exception as e:
+        print(f"Error generating confusion matrix image: {e}")
+        return None
+
+
+def generate_class_distribution_chart(class_names, classification_report):
+    """Tạo biểu đồ phân bố các class dạng base64"""
+    if not MATPLOTLIB_AVAILABLE:
+        return None
+    
+    try:
+        # Extract support (number of samples) for each class
+        supports = []
+        for cls in class_names:
+            if cls in classification_report:
+                supports.append(classification_report[cls].get('support', 0))
+            else:
+                supports.append(0)
+        
+        fig, ax = plt.subplots(figsize=(10, 6))
+        colors = plt.cm.Set3(np.linspace(0, 1, len(class_names)))
+        
+        bars = ax.bar(class_names, supports, color=colors)
+        ax.set_xlabel('Loại đất')
+        ax.set_ylabel('Số mẫu')
+        ax.set_title('Phân bố số mẫu theo loại đất')
+        plt.xticks(rotation=45, ha='right')
+        
+        # Add value labels on bars
+        for bar, val in zip(bars, supports):
+            ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5,
+                   str(int(val)), ha='center', va='bottom', fontsize=9)
+        
+        fig.tight_layout()
+        
+        # Convert to base64
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
+        buf.seek(0)
+        img_base64 = base64.b64encode(buf.read()).decode('utf-8')
+        plt.close(fig)
+        
+        return img_base64
+    except Exception as e:
+        print(f"Error generating class distribution chart: {e}")
+        return None
+
+
+def generate_metrics_chart(class_names, classification_report):
+    """Tạo biểu đồ precision/recall/f1 cho từng class"""
+    if not MATPLOTLIB_AVAILABLE:
+        return None
+    
+    try:
+        precisions = []
+        recalls = []
+        f1_scores = []
+        
+        for cls in class_names:
+            if cls in classification_report:
+                precisions.append(classification_report[cls].get('precision', 0))
+                recalls.append(classification_report[cls].get('recall', 0))
+                f1_scores.append(classification_report[cls].get('f1-score', 0))
+            else:
+                precisions.append(0)
+                recalls.append(0)
+                f1_scores.append(0)
+        
+        x = np.arange(len(class_names))
+        width = 0.25
+        
+        fig, ax = plt.subplots(figsize=(12, 6))
+        
+        bars1 = ax.bar(x - width, precisions, width, label='Precision', color='#3498db')
+        bars2 = ax.bar(x, recalls, width, label='Recall', color='#2ecc71')
+        bars3 = ax.bar(x + width, f1_scores, width, label='F1-Score', color='#e74c3c')
+        
+        ax.set_xlabel('Loại đất')
+        ax.set_ylabel('Score')
+        ax.set_title('Precision / Recall / F1-Score theo loại đất')
+        ax.set_xticks(x)
+        ax.set_xticklabels(class_names, rotation=45, ha='right')
+        ax.legend()
+        ax.set_ylim(0, 1.1)
+        
+        # Add grid
+        ax.yaxis.grid(True, linestyle='--', alpha=0.7)
+        
+        fig.tight_layout()
+        
+        # Convert to base64
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=100, bbox_inches='tight')
+        buf.seek(0)
+        img_base64 = base64.b64encode(buf.read()).decode('utf-8')
+        plt.close(fig)
+        
+        return img_base64
+    except Exception as e:
+        print(f"Error generating metrics chart: {e}")
+        return None
+
+
+def generate_training_report(training_result, config=None):
+    """
+    Tạo báo cáo HTML cho kết quả training
+    
+    Args:
+        training_result: Dict chứa kết quả từ train_model()
+        config: Dict chứa cấu hình training (optional)
+    
+    Returns:
+        Tuple (report_path, report_html)
+    """
+    
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    
+    # Extract data from result
+    train_acc = training_result.get('train_accuracy', 0) * 100
+    test_acc = training_result.get('test_accuracy', 0) * 100
+    train_samples = training_result.get('training_samples', 0)
+    test_samples = training_result.get('testing_samples', 0)
+    test_size = training_result.get('test_size', 0.2)
+    classes = training_result.get('classes', [])
+    cls_report = training_result.get('classification_report', {})
+    conf_matrix = training_result.get('confusion_matrix', [])
+    model_type = training_result.get('model_type', 'unknown')
+    model_path = training_result.get('model_path', '')
+    bbox = training_result.get('bbox', [])
+    time_range = training_result.get('time_range', '')
+    resolution = training_result.get('resolution', 20)
+    
+    # Generate charts
+    conf_matrix_img = generate_confusion_matrix_image(conf_matrix, classes) if conf_matrix else None
+    class_dist_img = generate_class_distribution_chart(classes, cls_report) if cls_report else None
+    metrics_img = generate_metrics_chart(classes, cls_report) if cls_report else None
+    
+    # Build classification report table
+    cls_report_rows = ""
+    for cls in classes:
+        if cls in cls_report:
+            metrics = cls_report[cls]
+            cls_report_rows += f"""
+            <tr>
+                <td><strong>{cls}</strong></td>
+                <td>{metrics.get('precision', 0):.3f}</td>
+                <td>{metrics.get('recall', 0):.3f}</td>
+                <td>{metrics.get('f1-score', 0):.3f}</td>
+                <td>{int(metrics.get('support', 0))}</td>
+            </tr>
+            """
+    
+    # Add averages
+    for avg_type in ['macro avg', 'weighted avg']:
+        if avg_type in cls_report:
+            metrics = cls_report[avg_type]
+            cls_report_rows += f"""
+            <tr style="background-color: #f0f0f0; font-weight: bold;">
+                <td>{avg_type}</td>
+                <td>{metrics.get('precision', 0):.3f}</td>
+                <td>{metrics.get('recall', 0):.3f}</td>
+                <td>{metrics.get('f1-score', 0):.3f}</td>
+                <td>{int(metrics.get('support', 0))}</td>
+            </tr>
+            """
+    
+    # Build confusion matrix table (fallback if no image)
+    conf_matrix_table = ""
+    if conf_matrix:
+        conf_matrix_table = "<table class='conf-matrix'><tr><th></th>"
+        for cls in classes:
+            conf_matrix_table += f"<th>{cls}</th>"
+        conf_matrix_table += "</tr>"
+        for i, row in enumerate(conf_matrix):
+            conf_matrix_table += f"<tr><th>{classes[i]}</th>"
+            for val in row:
+                conf_matrix_table += f"<td>{val}</td>"
+            conf_matrix_table += "</tr>"
+        conf_matrix_table += "</table>"
+    
+    # HTML Template
+    html = f"""
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Training Report - {timestamp}</title>
+    <style>
+        * {{
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }}
+        body {{
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: #f5f5f5;
+            padding: 20px;
+            line-height: 1.6;
+        }}
+        .container {{
+            max-width: 1200px;
+            margin: 0 auto;
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 10px 40px rgba(0,0,0,0.1);
+            overflow: hidden;
+        }}
+        .header {{
+            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }}
+        .header h1 {{
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }}
+        .header .subtitle {{
+            opacity: 0.9;
+            font-size: 1.1em;
+        }}
+        .content {{
+            padding: 40px;
+        }}
+        .section {{
+            margin-bottom: 40px;
+        }}
+        .section h2 {{
+            color: #667eea;
+            border-bottom: 3px solid #667eea;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+            font-size: 1.5em;
+        }}
+        .stats-grid {{
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+            margin-bottom: 30px;
+        }}
+        .stat-card {{
+            background: linear-gradient(135deg, #667eea15 0%, #764ba215 100%);
+            padding: 25px;
+            border-radius: 10px;
+            text-align: center;
+            border: 1px solid #667eea30;
+        }}
+        .stat-card .value {{
+            font-size: 2.5em;
+            font-weight: bold;
+            color: #667eea;
+        }}
+        .stat-card .label {{
+            color: #666;
+            margin-top: 5px;
+        }}
+        .stat-card.success .value {{
+            color: #28a745;
+        }}
+        .stat-card.warning .value {{
+            color: #ffc107;
+        }}
+        table {{
+            width: 100%;
+            border-collapse: collapse;
+            margin: 20px 0;
+        }}
+        th, td {{
+            padding: 12px 15px;
+            text-align: left;
+            border-bottom: 1px solid #ddd;
+        }}
+        th {{
+            background: #667eea;
+            color: white;
+        }}
+        tr:hover {{
+            background-color: #f5f5f5;
+        }}
+        .conf-matrix {{
+            font-size: 14px;
+        }}
+        .conf-matrix th, .conf-matrix td {{
+            text-align: center;
+            padding: 8px;
+        }}
+        .chart-container {{
+            text-align: center;
+            margin: 20px 0;
+        }}
+        .chart-container img {{
+            max-width: 100%;
+            border-radius: 10px;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+        }}
+        .info-box {{
+            background: #e3f2fd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 5px solid #2196f3;
+            margin: 20px 0;
+        }}
+        .info-row {{
+            display: flex;
+            margin: 10px 0;
+        }}
+        .info-label {{
+            font-weight: bold;
+            width: 200px;
+            color: #555;
+        }}
+        .info-value {{
+            color: #333;
+        }}
+        .footer {{
+            background: #f8f9fa;
+            padding: 20px;
+            text-align: center;
+            color: #666;
+            font-size: 14px;
+        }}
+        @media print {{
+            body {{
+                background: white;
+                padding: 0;
+            }}
+            .container {{
+                box-shadow: none;
+            }}
+        }}
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>📊 Báo Cáo Training Model</h1>
+            <p class="subtitle">Land Classification - {datetime.now().strftime("%d/%m/%Y %H:%M:%S")}</p>
+        </div>
+        
+        <div class="content">
+            <!-- Summary Stats -->
+            <div class="section">
+                <h2>📈 Tóm Tắt Kết Quả</h2>
+                <div class="stats-grid">
+                    <div class="stat-card success">
+                        <div class="value">{train_acc:.1f}%</div>
+                        <div class="label">Train Accuracy</div>
+                    </div>
+                    <div class="stat-card {'success' if test_acc >= 80 else 'warning'}">
+                        <div class="value">{test_acc:.1f}%</div>
+                        <div class="label">Test Accuracy</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">{train_samples}</div>
+                        <div class="label">Training Samples</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">{test_samples}</div>
+                        <div class="label">Testing Samples</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">{len(classes)}</div>
+                        <div class="label">Số Classes</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">{test_size*100:.0f}%</div>
+                        <div class="label">Test Size</div>
+                    </div>
+                </div>
+            </div>
+            
+            <!-- Configuration Info -->
+            <div class="section">
+                <h2>⚙️ Cấu Hình Training</h2>
+                <div class="info-box">
+                    <div class="info-row">
+                        <span class="info-label">🤖 Model Type:</span>
+                        <span class="info-value">{model_type.upper()}</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📍 Khu vực (bbox):</span>
+                        <span class="info-value">{bbox}</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📅 Thời gian:</span>
+                        <span class="info-value">{time_range}</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📐 Độ phân giải:</span>
+                        <span class="info-value">{resolution}m</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">💾 Model Path:</span>
+                        <span class="info-value">{model_path}</span>
+                    </div>
+                </div>
+            </div>
+            
+            <!-- Classification Report -->
+            <div class="section">
+                <h2>📋 Classification Report</h2>
+                <table>
+                    <thead>
+                        <tr>
+                            <th>Loại đất</th>
+                            <th>Precision</th>
+                            <th>Recall</th>
+                            <th>F1-Score</th>
+                            <th>Support</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        {cls_report_rows}
+                    </tbody>
+                </table>
+            </div>
+            
+            <!-- Metrics Chart -->
+            {'<div class="section"><h2>📊 Biểu Đồ Metrics</h2><div class="chart-container"><img src="data:image/png;base64,' + metrics_img + '" alt="Metrics Chart"></div></div>' if metrics_img else ''}
+            
+            <!-- Class Distribution -->
+            {'<div class="section"><h2>📊 Phân Bố Số Mẫu</h2><div class="chart-container"><img src="data:image/png;base64,' + class_dist_img + '" alt="Class Distribution"></div></div>' if class_dist_img else ''}
+            
+            <!-- Confusion Matrix -->
+            <div class="section">
+                <h2>🔢 Confusion Matrix</h2>
+                {'<div class="chart-container"><img src="data:image/png;base64,' + conf_matrix_img + '" alt="Confusion Matrix"></div>' if conf_matrix_img else conf_matrix_table}
+            </div>
+            
+            <!-- Classes List -->
+            <div class="section">
+                <h2>🏷️ Danh Sách Các Loại Đất</h2>
+                <div class="info-box">
+                    <ul style="list-style: none; display: flex; flex-wrap: wrap; gap: 10px;">
+                        {''.join([f'<li style="background: #667eea; color: white; padding: 8px 15px; border-radius: 20px;">{cls}</li>' for cls in classes])}
+                    </ul>
+                </div>
+            </div>
+        </div>
+        
+        <div class="footer">
+            <p>🌍 Land Classification Training System | Generated: {datetime.now().strftime("%d/%m/%Y %H:%M:%S")}</p>
+            <p>Data Source: Microsoft Planetary Computer (Sentinel-2 L2A, Sentinel-1 RTC)</p>
+        </div>
+    </div>
+</body>
+</html>
+"""
+    
+    # Save report
+    reports_dir = Path("reports")
+    reports_dir.mkdir(exist_ok=True)
+    
+    report_filename = f"training_report_{timestamp}.html"
+    report_path = reports_dir / report_filename
+    
+    with open(report_path, 'w', encoding='utf-8') as f:
+        f.write(html)
+    
+    return str(report_path), html
+
+
+def generate_prediction_report(prediction_result, config=None):
+    """
+    Tạo báo cáo HTML cho kết quả prediction
+    
+    Args:
+        prediction_result: Dict chứa kết quả prediction
+        config: Dict chứa cấu hình prediction (optional)
+    
+    Returns:
+        Tuple (report_path, report_html)
+    """
+    
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    
+    # Extract data
+    output_file = prediction_result.get('output_file', '')
+    shape = prediction_result.get('shape', [0, 0])
+    unique_classes = prediction_result.get('unique_classes', [])
+    bbox = prediction_result.get('bbox', [])
+    time_range = prediction_result.get('time_range', '')
+    n_features = prediction_result.get('n_features', 0)
+    used_radar = prediction_result.get('used_radar', False)
+    model_used = prediction_result.get('model_used', '')
+    
+    # Calculate area (approximate)
+    if len(bbox) == 4:
+        # Approximate calculation (1 degree ≈ 111km at equator)
+        width_km = (bbox[2] - bbox[0]) * 111 * 0.85  # cos adjustment for Vietnam
+        height_km = (bbox[3] - bbox[1]) * 111
+        area_km2 = width_km * height_km
+    else:
+        area_km2 = 0
+    
+    total_pixels = shape[0] * shape[1] if len(shape) == 2 else 0
+    
+    # HTML Template
+    html = f"""
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Prediction Report - {timestamp}</title>
+    <style>
+        * {{
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }}
+        body {{
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: #f5f5f5;
+            padding: 20px;
+            line-height: 1.6;
+        }}
+        .container {{
+            max-width: 1200px;
+            margin: 0 auto;
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 10px 40px rgba(0,0,0,0.1);
+            overflow: hidden;
+        }}
+        .header {{
+            background: linear-gradient(135deg, #ff6b6b 0%, #ee5a6f 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }}
+        .header h1 {{
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }}
+        .content {{
+            padding: 40px;
+        }}
+        .section {{
+            margin-bottom: 40px;
+        }}
+        .section h2 {{
+            color: #ff6b6b;
+            border-bottom: 3px solid #ff6b6b;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }}
+        .stats-grid {{
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+        }}
+        .stat-card {{
+            background: linear-gradient(135deg, #ff6b6b15 0%, #ee5a6f15 100%);
+            padding: 25px;
+            border-radius: 10px;
+            text-align: center;
+            border: 1px solid #ff6b6b30;
+        }}
+        .stat-card .value {{
+            font-size: 2em;
+            font-weight: bold;
+            color: #ff6b6b;
+        }}
+        .stat-card .label {{
+            color: #666;
+            margin-top: 5px;
+        }}
+        .info-box {{
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 5px solid #ff6b6b;
+            margin: 20px 0;
+        }}
+        .info-row {{
+            display: flex;
+            margin: 10px 0;
+        }}
+        .info-label {{
+            font-weight: bold;
+            width: 200px;
+            color: #555;
+        }}
+        .class-badge {{
+            display: inline-block;
+            background: #ff6b6b;
+            color: white;
+            padding: 8px 15px;
+            border-radius: 20px;
+            margin: 5px;
+        }}
+        .footer {{
+            background: #f8f9fa;
+            padding: 20px;
+            text-align: center;
+            color: #666;
+        }}
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🗺️ Báo Cáo Dự Đoán</h1>
+            <p>Land Classification Prediction - {datetime.now().strftime("%d/%m/%Y %H:%M:%S")}</p>
+        </div>
+        
+        <div class="content">
+            <div class="section">
+                <h2>📈 Tóm Tắt Kết Quả</h2>
+                <div class="stats-grid">
+                    <div class="stat-card">
+                        <div class="value">{total_pixels:,}</div>
+                        <div class="label">Tổng số Pixels</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">{shape[0]}x{shape[1]}</div>
+                        <div class="label">Kích thước (px)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">{area_km2:.1f}</div>
+                        <div class="label">Diện tích (km²)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">{len(unique_classes)}</div>
+                        <div class="label">Số Classes</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">{n_features}</div>
+                        <div class="label">Số Features</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">{'✅' if used_radar else '❌'}</div>
+                        <div class="label">Sử dụng Radar</div>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>⚙️ Thông Tin Chi Tiết</h2>
+                <div class="info-box">
+                    <div class="info-row">
+                        <span class="info-label">🤖 Model sử dụng:</span>
+                        <span>{model_used}</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📍 Khu vực (bbox):</span>
+                        <span>{bbox}</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📅 Thời gian:</span>
+                        <span>{time_range}</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">💾 Output file:</span>
+                        <span>{output_file}</span>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>🏷️ Các Classes Phát Hiện</h2>
+                <div>
+                    {''.join([f'<span class="class-badge">{cls}</span>' for cls in unique_classes])}
+                </div>
+            </div>
+        </div>
+        
+        <div class="footer">
+            <p>🌍 Land Classification System | Generated: {datetime.now().strftime("%d/%m/%Y %H:%M:%S")}</p>
+        </div>
+    </div>
+</body>
+</html>
+"""
+    
+    # Save report
+    reports_dir = Path("reports")
+    reports_dir.mkdir(exist_ok=True)
+    
+    report_filename = f"prediction_report_{timestamp}.html"
+    report_path = reports_dir / report_filename
+    
+    with open(report_path, 'w', encoding='utf-8') as f:
+        f.write(html)
+    
+    return str(report_path), html
+
+
+if __name__ == "__main__":
+    # Test report generation
+    test_result = {
+        "success": True,
+        "train_accuracy": 0.95,
+        "test_accuracy": 0.87,
+        "training_samples": 800,
+        "testing_samples": 200,
+        "test_size": 0.2,
+        "classes": ["Lua", "Rung", "Nuoc", "Dan_cu", "Cay_lau_nam"],
+        "model_type": "xgboost",
+        "model_path": "model_train/model_xgboost_20251221.joblib",
+        "bbox": [105.6, 9.3, 106.2, 9.8],
+        "time_range": "2023-03-01/2023-05-31",
+        "resolution": 20,
+        "classification_report": {
+            "Lua": {"precision": 0.92, "recall": 0.89, "f1-score": 0.90, "support": 50},
+            "Rung": {"precision": 0.88, "recall": 0.91, "f1-score": 0.89, "support": 45},
+            "Nuoc": {"precision": 0.95, "recall": 0.93, "f1-score": 0.94, "support": 40},
+            "Dan_cu": {"precision": 0.85, "recall": 0.82, "f1-score": 0.83, "support": 35},
+            "Cay_lau_nam": {"precision": 0.80, "recall": 0.85, "f1-score": 0.82, "support": 30},
+            "macro avg": {"precision": 0.88, "recall": 0.88, "f1-score": 0.88, "support": 200},
+            "weighted avg": {"precision": 0.88, "recall": 0.87, "f1-score": 0.87, "support": 200}
+        },
+        "confusion_matrix": [
+            [45, 2, 1, 1, 1],
+            [3, 41, 0, 1, 0],
+            [1, 0, 37, 1, 1],
+            [2, 1, 1, 29, 2],
+            [1, 1, 1, 2, 26]
+        ]
+    }
+    
+    path, html = generate_training_report(test_result)
+    print(f"Report generated: {path}")

reports/prediction_report_20251221_122210.html

@@ -0,0 +1,176 @@
+
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Prediction Report - 20251221_122210</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: #f5f5f5;
+            padding: 20px;
+            line-height: 1.6;
+        }
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 10px 40px rgba(0,0,0,0.1);
+            overflow: hidden;
+        }
+        .header {
+            background: linear-gradient(135deg, #ff6b6b 0%, #ee5a6f 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }
+        .content {
+            padding: 40px;
+        }
+        .section {
+            margin-bottom: 40px;
+        }
+        .section h2 {
+            color: #ff6b6b;
+            border-bottom: 3px solid #ff6b6b;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+        }
+        .stat-card {
+            background: linear-gradient(135deg, #ff6b6b15 0%, #ee5a6f15 100%);
+            padding: 25px;
+            border-radius: 10px;
+            text-align: center;
+            border: 1px solid #ff6b6b30;
+        }
+        .stat-card .value {
+            font-size: 2em;
+            font-weight: bold;
+            color: #ff6b6b;
+        }
+        .stat-card .label {
+            color: #666;
+            margin-top: 5px;
+        }
+        .info-box {
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 5px solid #ff6b6b;
+            margin: 20px 0;
+        }
+        .info-row {
+            display: flex;
+            margin: 10px 0;
+        }
+        .info-label {
+            font-weight: bold;
+            width: 200px;
+            color: #555;
+        }
+        .class-badge {
+            display: inline-block;
+            background: #ff6b6b;
+            color: white;
+            padding: 8px 15px;
+            border-radius: 20px;
+            margin: 5px;
+        }
+        .footer {
+            background: #f8f9fa;
+            padding: 20px;
+            text-align: center;
+            color: #666;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🗺️ Báo Cáo Dự Đoán</h1>
+            <p>Land Classification Prediction - 21/12/2025 12:22:10</p>
+        </div>
+        
+        <div class="content">
+            <div class="section">
+                <h2>📈 Tóm Tắt Kết Quả</h2>
+                <div class="stats-grid">
+                    <div class="stat-card">
+                        <div class="value">9,156,974</div>
+                        <div class="label">Tổng số Pixels</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">2774x3301</div>
+                        <div class="label">Kích thước (px)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">3141.9</div>
+                        <div class="label">Diện tích (km²)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">3</div>
+                        <div class="label">Số Classes</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">3</div>
+                        <div class="label">Số Features</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">✅</div>
+                        <div class="label">Sử dụng Radar</div>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>⚙️ Thông Tin Chi Tiết</h2>
+                <div class="info-box">
+                    <div class="info-row">
+                        <span class="info-label">🤖 Model sử dụng:</span>
+                        <span>model_xgboost_20251221_122105.joblib</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📍 Khu vực (bbox):</span>
+                        <span>[105.6, 9.3, 106.2, 9.8]</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📅 Thời gian:</span>
+                        <span>2023-03-01/2023-05-31</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">💾 Output file:</span>
+                        <span>predictions/prediction_20251221_122209.tif</span>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>🏷️ Các Classes Phát Hiện</h2>
+                <div>
+                    <span class="class-badge">3</span><span class="class-badge">5</span><span class="class-badge">6</span>
+                </div>
+            </div>
+        </div>
+        
+        <div class="footer">
+            <p>🌍 Land Classification System | Generated: 21/12/2025 12:22:10</p>
+        </div>
+    </div>
+</body>
+</html>

reports/prediction_report_20251221_171732.html

@@ -0,0 +1,176 @@
+
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Prediction Report - 20251221_171732</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: #f5f5f5;
+            padding: 20px;
+            line-height: 1.6;
+        }
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 10px 40px rgba(0,0,0,0.1);
+            overflow: hidden;
+        }
+        .header {
+            background: linear-gradient(135deg, #ff6b6b 0%, #ee5a6f 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }
+        .content {
+            padding: 40px;
+        }
+        .section {
+            margin-bottom: 40px;
+        }
+        .section h2 {
+            color: #ff6b6b;
+            border-bottom: 3px solid #ff6b6b;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+        }
+        .stat-card {
+            background: linear-gradient(135deg, #ff6b6b15 0%, #ee5a6f15 100%);
+            padding: 25px;
+            border-radius: 10px;
+            text-align: center;
+            border: 1px solid #ff6b6b30;
+        }
+        .stat-card .value {
+            font-size: 2em;
+            font-weight: bold;
+            color: #ff6b6b;
+        }
+        .stat-card .label {
+            color: #666;
+            margin-top: 5px;
+        }
+        .info-box {
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 5px solid #ff6b6b;
+            margin: 20px 0;
+        }
+        .info-row {
+            display: flex;
+            margin: 10px 0;
+        }
+        .info-label {
+            font-weight: bold;
+            width: 200px;
+            color: #555;
+        }
+        .class-badge {
+            display: inline-block;
+            background: #ff6b6b;
+            color: white;
+            padding: 8px 15px;
+            border-radius: 20px;
+            margin: 5px;
+        }
+        .footer {
+            background: #f8f9fa;
+            padding: 20px;
+            text-align: center;
+            color: #666;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🗺️ Báo Cáo Dự Đoán</h1>
+            <p>Land Classification Prediction - 21/12/2025 17:17:32</p>
+        </div>
+        
+        <div class="content">
+            <div class="section">
+                <h2>📈 Tóm Tắt Kết Quả</h2>
+                <div class="stats-grid">
+                    <div class="stat-card">
+                        <div class="value">420</div>
+                        <div class="label">Tổng số Pixels</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">20x21</div>
+                        <div class="label">Kích thước (px)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">0.1</div>
+                        <div class="label">Diện tích (km²)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">1</div>
+                        <div class="label">Số Classes</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">3</div>
+                        <div class="label">Số Features</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">✅</div>
+                        <div class="label">Sử dụng Radar</div>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>⚙️ Thông Tin Chi Tiết</h2>
+                <div class="info-box">
+                    <div class="info-row">
+                        <span class="info-label">🤖 Model sử dụng:</span>
+                        <span>model_cnn_20251221_163841.joblib</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📍 Khu vực (bbox):</span>
+                        <span>[105.16372919082643, 9.182049314243548, 105.16746282577516, 9.185480898286633]</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📅 Thời gian:</span>
+                        <span>2023-03-01/2023-05-31</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">💾 Output file:</span>
+                        <span>predictions/prediction_20251221_171732.tif</span>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>🏷️ Các Classes Phát Hiện</h2>
+                <div>
+                    <span class="class-badge">6</span>
+                </div>
+            </div>
+        </div>
+        
+        <div class="footer">
+            <p>🌍 Land Classification System | Generated: 21/12/2025 17:17:32</p>
+        </div>
+    </div>
+</body>
+</html>

reports/prediction_report_20251221_172119.html

@@ -0,0 +1,176 @@
+
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Prediction Report - 20251221_172119</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: #f5f5f5;
+            padding: 20px;
+            line-height: 1.6;
+        }
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 10px 40px rgba(0,0,0,0.1);
+            overflow: hidden;
+        }
+        .header {
+            background: linear-gradient(135deg, #ff6b6b 0%, #ee5a6f 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }
+        .content {
+            padding: 40px;
+        }
+        .section {
+            margin-bottom: 40px;
+        }
+        .section h2 {
+            color: #ff6b6b;
+            border-bottom: 3px solid #ff6b6b;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+        }
+        .stat-card {
+            background: linear-gradient(135deg, #ff6b6b15 0%, #ee5a6f15 100%);
+            padding: 25px;
+            border-radius: 10px;
+            text-align: center;
+            border: 1px solid #ff6b6b30;
+        }
+        .stat-card .value {
+            font-size: 2em;
+            font-weight: bold;
+            color: #ff6b6b;
+        }
+        .stat-card .label {
+            color: #666;
+            margin-top: 5px;
+        }
+        .info-box {
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 5px solid #ff6b6b;
+            margin: 20px 0;
+        }
+        .info-row {
+            display: flex;
+            margin: 10px 0;
+        }
+        .info-label {
+            font-weight: bold;
+            width: 200px;
+            color: #555;
+        }
+        .class-badge {
+            display: inline-block;
+            background: #ff6b6b;
+            color: white;
+            padding: 8px 15px;
+            border-radius: 20px;
+            margin: 5px;
+        }
+        .footer {
+            background: #f8f9fa;
+            padding: 20px;
+            text-align: center;
+            color: #666;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🗺️ Báo Cáo Dự Đoán</h1>
+            <p>Land Classification Prediction - 21/12/2025 17:21:19</p>
+        </div>
+        
+        <div class="content">
+            <div class="section">
+                <h2>📈 Tóm Tắt Kết Quả</h2>
+                <div class="stats-grid">
+                    <div class="stat-card">
+                        <div class="value">420</div>
+                        <div class="label">Tổng số Pixels</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">20x21</div>
+                        <div class="label">Kích thước (px)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">0.1</div>
+                        <div class="label">Diện tích (km²)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">1</div>
+                        <div class="label">Số Classes</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">3</div>
+                        <div class="label">Số Features</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">✅</div>
+                        <div class="label">Sử dụng Radar</div>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>⚙️ Thông Tin Chi Tiết</h2>
+                <div class="info-box">
+                    <div class="info-row">
+                        <span class="info-label">🤖 Model sử dụng:</span>
+                        <span>model_cnn_20251221_163841.joblib</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📍 Khu vực (bbox):</span>
+                        <span>[105.16372919082643, 9.182049314243548, 105.16746282577516, 9.185480898286633]</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📅 Thời gian:</span>
+                        <span>2023-03-01/2023-05-31</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">💾 Output file:</span>
+                        <span>predictions/prediction_20251221_172118.tif</span>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>🏷️ Các Classes Phát Hiện</h2>
+                <div>
+                    <span class="class-badge">6</span>
+                </div>
+            </div>
+        </div>
+        
+        <div class="footer">
+            <p>🌍 Land Classification System | Generated: 21/12/2025 17:21:19</p>
+        </div>
+    </div>
+</body>
+</html>

reports/prediction_report_20251221_172815.html

@@ -0,0 +1,176 @@
+
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Prediction Report - 20251221_172815</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: #f5f5f5;
+            padding: 20px;
+            line-height: 1.6;
+        }
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 10px 40px rgba(0,0,0,0.1);
+            overflow: hidden;
+        }
+        .header {
+            background: linear-gradient(135deg, #ff6b6b 0%, #ee5a6f 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }
+        .content {
+            padding: 40px;
+        }
+        .section {
+            margin-bottom: 40px;
+        }
+        .section h2 {
+            color: #ff6b6b;
+            border-bottom: 3px solid #ff6b6b;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+        }
+        .stat-card {
+            background: linear-gradient(135deg, #ff6b6b15 0%, #ee5a6f15 100%);
+            padding: 25px;
+            border-radius: 10px;
+            text-align: center;
+            border: 1px solid #ff6b6b30;
+        }
+        .stat-card .value {
+            font-size: 2em;
+            font-weight: bold;
+            color: #ff6b6b;
+        }
+        .stat-card .label {
+            color: #666;
+            margin-top: 5px;
+        }
+        .info-box {
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 5px solid #ff6b6b;
+            margin: 20px 0;
+        }
+        .info-row {
+            display: flex;
+            margin: 10px 0;
+        }
+        .info-label {
+            font-weight: bold;
+            width: 200px;
+            color: #555;
+        }
+        .class-badge {
+            display: inline-block;
+            background: #ff6b6b;
+            color: white;
+            padding: 8px 15px;
+            border-radius: 20px;
+            margin: 5px;
+        }
+        .footer {
+            background: #f8f9fa;
+            padding: 20px;
+            text-align: center;
+            color: #666;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🗺️ Báo Cáo Dự Đoán</h1>
+            <p>Land Classification Prediction - 21/12/2025 17:28:15</p>
+        </div>
+        
+        <div class="content">
+            <div class="section">
+                <h2>📈 Tóm Tắt Kết Quả</h2>
+                <div class="stats-grid">
+                    <div class="stat-card">
+                        <div class="value">420</div>
+                        <div class="label">Tổng số Pixels</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">20x21</div>
+                        <div class="label">Kích thước (px)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">0.1</div>
+                        <div class="label">Diện tích (km²)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">2</div>
+                        <div class="label">Số Classes</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">3</div>
+                        <div class="label">Số Features</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">✅</div>
+                        <div class="label">Sử dụng Radar</div>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>⚙️ Thông Tin Chi Tiết</h2>
+                <div class="info-box">
+                    <div class="info-row">
+                        <span class="info-label">🤖 Model sử dụng:</span>
+                        <span>model_xgboost_20251221_172351.joblib</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📍 Khu vực (bbox):</span>
+                        <span>[105.16372919082643, 9.182049314243548, 105.16746282577516, 9.185480898286633]</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📅 Thời gian:</span>
+                        <span>2023-03-01/2023-05-31</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">💾 Output file:</span>
+                        <span>predictions/prediction_20251221_172814.tif</span>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>🏷️ Các Classes Phát Hiện</h2>
+                <div>
+                    <span class="class-badge">3</span><span class="class-badge">6</span>
+                </div>
+            </div>
+        </div>
+        
+        <div class="footer">
+            <p>🌍 Land Classification System | Generated: 21/12/2025 17:28:15</p>
+        </div>
+    </div>
+</body>
+</html>

reports/prediction_report_20251221_172829.html

@@ -0,0 +1,176 @@
+
+<!DOCTYPE html>
+<html lang="vi">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Prediction Report - 20251221_172829</title>
+    <style>
+        * {
+            margin: 0;
+            padding: 0;
+            box-sizing: border-box;
+        }
+        body {
+            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+            background: #f5f5f5;
+            padding: 20px;
+            line-height: 1.6;
+        }
+        .container {
+            max-width: 1200px;
+            margin: 0 auto;
+            background: white;
+            border-radius: 15px;
+            box-shadow: 0 10px 40px rgba(0,0,0,0.1);
+            overflow: hidden;
+        }
+        .header {
+            background: linear-gradient(135deg, #ff6b6b 0%, #ee5a6f 100%);
+            color: white;
+            padding: 40px;
+            text-align: center;
+        }
+        .header h1 {
+            font-size: 2.5em;
+            margin-bottom: 10px;
+        }
+        .content {
+            padding: 40px;
+        }
+        .section {
+            margin-bottom: 40px;
+        }
+        .section h2 {
+            color: #ff6b6b;
+            border-bottom: 3px solid #ff6b6b;
+            padding-bottom: 10px;
+            margin-bottom: 20px;
+        }
+        .stats-grid {
+            display: grid;
+            grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
+            gap: 20px;
+        }
+        .stat-card {
+            background: linear-gradient(135deg, #ff6b6b15 0%, #ee5a6f15 100%);
+            padding: 25px;
+            border-radius: 10px;
+            text-align: center;
+            border: 1px solid #ff6b6b30;
+        }
+        .stat-card .value {
+            font-size: 2em;
+            font-weight: bold;
+            color: #ff6b6b;
+        }
+        .stat-card .label {
+            color: #666;
+            margin-top: 5px;
+        }
+        .info-box {
+            background: #fff3cd;
+            padding: 20px;
+            border-radius: 10px;
+            border-left: 5px solid #ff6b6b;
+            margin: 20px 0;
+        }
+        .info-row {
+            display: flex;
+            margin: 10px 0;
+        }
+        .info-label {
+            font-weight: bold;
+            width: 200px;
+            color: #555;
+        }
+        .class-badge {
+            display: inline-block;
+            background: #ff6b6b;
+            color: white;
+            padding: 8px 15px;
+            border-radius: 20px;
+            margin: 5px;
+        }
+        .footer {
+            background: #f8f9fa;
+            padding: 20px;
+            text-align: center;
+            color: #666;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">
+            <h1>🗺️ Báo Cáo Dự Đoán</h1>
+            <p>Land Classification Prediction - 21/12/2025 17:28:29</p>
+        </div>
+        
+        <div class="content">
+            <div class="section">
+                <h2>📈 Tóm Tắt Kết Quả</h2>
+                <div class="stats-grid">
+                    <div class="stat-card">
+                        <div class="value">420</div>
+                        <div class="label">Tổng số Pixels</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">20x21</div>
+                        <div class="label">Kích thước (px)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">0.1</div>
+                        <div class="label">Diện tích (km²)</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">2</div>
+                        <div class="label">Số Classes</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">3</div>
+                        <div class="label">Số Features</div>
+                    </div>
+                    <div class="stat-card">
+                        <div class="value">✅</div>
+                        <div class="label">Sử dụng Radar</div>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>⚙️ Thông Tin Chi Tiết</h2>
+                <div class="info-box">
+                    <div class="info-row">
+                        <span class="info-label">🤖 Model sử dụng:</span>
+                        <span>model_xgboost_20251221_172351.joblib</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📍 Khu vực (bbox):</span>
+                        <span>[105.16372919082643, 9.182049314243548, 105.16746282577516, 9.185480898286633]</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">📅 Thời gian:</span>
+                        <span>2023-03-01/2023-05-31</span>
+                    </div>
+                    <div class="info-row">
+                        <span class="info-label">💾 Output file:</span>
+                        <span>predictions/prediction_20251221_172828.tif</span>
+                    </div>
+                </div>
+            </div>
+            
+            <div class="section">
+                <h2>🏷️ Các Classes Phát Hiện</h2>
+                <div>
+                    <span class="class-badge">3</span><span class="class-badge">6</span>
+                </div>
+            </div>
+        </div>
+        
+        <div class="footer">
+            <p>🌍 Land Classification System | Generated: 21/12/2025 17:28:29</p>
+        </div>
+    </div>
+</body>
+</html>

requirement.txt

@@ -0,0 +1,309 @@
+affine @ file:///home/conda/feedstock_root/build_artifacts/affine_1733762038348/work
+aiobotocore==2.25.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.15
+aioitertools==0.12.0
+aiosignal==1.4.0
+alembic==1.16.5
+annotated-doc==0.0.4
+annotated-types==0.7.0
+antimeridian @ file:///home/conda/feedstock_root/build_artifacts/antimeridian_1753706324394/work
+anyio @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_anyio_1758634638/work
+argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1749017159514/work
+argon2-cffi-bindings @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi-bindings_1649500328244/work
+arrow @ file:///home/conda/feedstock_root/build_artifacts/arrow_1733584251875/work
+asciitree==0.3.3
+asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1733250440834/work
+async-lru @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_async-lru_1742153708/work
+async-timeout==3.0.1
+attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1741918516150/work
+babel @ file:///home/conda/feedstock_root/build_artifacts/babel_1738490167835/work
+beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1759146011391/work
+bleach @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_bleach_1737382993/work
+blinker==1.9.0
+bokeh==3.7.3
+boto3==1.40.18
+botocore==1.40.49
+Bottleneck @ file:///croot/bottleneck_1731058641041/work
+branca @ file:///croot/branca_1675157607453/work
+Brotli @ file:///croot/brotli-split_1736182456865/work
+brotlicffi @ file:///croot/brotlicffi_1736182461069/work
+cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work
+cachetools==6.2.0
+Cartopy==0.25.0
+certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1759648874697/work/certifi
+cffi @ file:///croot/cffi_1736182485317/work
+cftime @ file:///home/conda/feedstock_root/build_artifacts/cftime_1649636873066/work
+chardet @ file:///home/conda/feedstock_root/build_artifacts/chardet_1649184137891/work
+charset-normalizer @ file:///croot/charset-normalizer_1721748349566/work
+ciso8601==2.3.3
+click @ file:///home/conda/feedstock_root/build_artifacts/click_1747811314515/work
+click-plugins @ file:///home/conda/feedstock_root/build_artifacts/click-plugins_1750848229740/work
+cligj @ file:///home/conda/feedstock_root/build_artifacts/cligj_1733749956636/work
+cloudpickle @ file:///home/conda/feedstock_root/build_artifacts/cloudpickle_1736947526808/work
+colorama==0.4.6
+colorcet==3.1.0
+comm @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_comm_1753453984/work
+contourpy @ file:///croot/contourpy_1732540045555/work
+cycler @ file:///tmp/build/80754af9/cycler_1637851556182/work
+cytoolz==0.11.2
+dask @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_dask-core_1760473436/work
+dask-gateway @ file:///Users/runner/miniforge3/conda-bld/bld/rattler-build_dask-gateway_1744370153/work/dask-gateway
+dask-glm @ file:///home/conda/feedstock_root/build_artifacts/dask-glm_1701346265909/work
+dask-image==2024.5.3
+dask-ml @ file:///home/conda/feedstock_root/build_artifacts/dask-ml_1679705292494/work
+datacube==1.8.15
+datacube_ows==1.9.4
+datashader==0.18.2
+dea-tools==0.3.0
+debugpy @ file:///home/task_175706711740264/conda-bld/debugpy_1757067131873/work
+decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1740384970518/work
+deepdiff==8.6.1
+defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work
+deprecat @ file:///home/conda/feedstock_root/build_artifacts/deprecat_1734684036993/work
+distributed @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_distributed_1760476147/work
+eo-tides==0.8.2
+exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1746947292760/work
+executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1756729339227/work
+fastapi==0.124.3
+fasteners @ file:///home/conda/feedstock_root/build_artifacts/fasteners_1734943108928/work
+fastjsonschema @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_python-fastjsonschema_1755304154/work/dist
+filelock==3.19.1
+fiona==1.10.1
+Flask==3.1.2
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+flatbuffers==25.2.10
+folium==0.20.0
+fonttools @ file:///croot/fonttools_1737039080035/work
+fqdn @ file:///home/conda/feedstock_root/build_artifacts/fqdn_1733327382592/work/dist
+frozenlist==1.7.0
+fsspec @ file:///home/conda/feedstock_root/build_artifacts/fsspec_1756908513222/work
+GDAL @ file:///croot/gdal-split_1734448174900/work/build/swig/python
+GeoAlchemy2 @ file:///home/conda/feedstock_root/build_artifacts/geoalchemy2_1753372953474/work
+geographiclib==2.1
+geojson==3.2.0
+geomad==1.0.0
+geopandas @ file:///croot/geopandas-split_1755761494241/work
+geopy==2.4.1
+greenlet @ file:///home/conda/feedstock_root/build_artifacts/greenlet_1648882383677/work
+h11 @ file:///home/conda/feedstock_root/build_artifacts/h11_1745526374115/work
+h2 @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_h2_1756364871/work
+h3==4.3.1
+hdstats==0.2.1
+holoviews==1.21.0
+hpack @ file:///home/conda/feedstock_root/build_artifacts/hpack_1737618293087/work
+httpcore @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_httpcore_1745602916/work
+httpx @ file:///home/conda/feedstock_root/build_artifacts/httpx_1733663348460/work
+hvplot==0.12.1
+hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1737618333194/work
+idna==3.10
+imagecodecs==2025.3.30
+imageio==2.37.0
+importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_importlib-metadata_1747934053/work
+ipykernel @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_ipykernel_1760459840/work
+ipyleaflet==0.20.0
+ipython @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_ipython_1748711175/work
+ipywidgets==8.1.7
+iso8601==2.1.0
+isoduration @ file:///home/conda/feedstock_root/build_artifacts/isoduration_1733493628631/work/dist
+itsdangerous==2.2.0
+jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1733300866624/work
+Jinja2 @ file:///croot/jinja2_1741710844255/work
+jmespath @ file:///home/conda/feedstock_root/build_artifacts/jmespath_1733229141657/work
+joblib @ file:///home/conda/feedstock_root/build_artifacts/joblib_1756321760188/work
+json5 @ file:///home/conda/feedstock_root/build_artifacts/json5_1755034879854/work
+jsonpointer @ file:///home/conda/feedstock_root/build_artifacts/jsonpointer_1756754132747/work
+jsonschema @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jsonschema_1755595646/work
+jsonschema-specifications==2025.4.1
+jupyter-events @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jupyter_events_1738765986/work
+jupyter-leaflet==0.20.0
+jupyter-lsp @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jupyter-lsp_1756388269/work/jupyter-lsp
+jupyter-ui-poll==1.0.0
+jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1733440914442/work
+jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1748333051527/work
+jupyter_server @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_jupyter_server_1755870522/work
+jupyter_server_terminals @ file:///home/conda/feedstock_root/build_artifacts/jupyter_server_terminals_1733427956852/work
+jupyterlab @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_1758913905644/work
+jupyterlab_pygments @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_pygments_1733328101776/work
+jupyterlab_server @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_server_1733599573484/work
+jupyterlab_widgets==3.0.15
+kiwisolver @ file:///croot/kiwisolver_1737039087198/work
+lark==1.2.2
+lark-parser==0.12.0
+lazy_loader==0.4
+linkify-it-py==2.0.3
+llvmlite @ file:///croot/llvmlite_1741209858218/work
+locket @ file:///home/conda/feedstock_root/build_artifacts/locket_1650660393415/work
+lxml==5.4.0
+lz4 @ file:///croot/lz4_1736366683208/work
+Mako @ file:///home/conda/feedstock_root/build_artifacts/mako_1744317760971/work
+mapclassify @ file:///croot/mapclassify_1675157730177/work
+Markdown==3.9
+markdown-it-py==4.0.0
+MarkupSafe @ file:///croot/markupsafe_1738584038848/work
+matplotlib==3.10.5
+matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1733416936468/work
+mdit-py-plugins==0.5.0
+mdurl==0.1.2
+mistune @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_mistune_1756495311/work
+mpmath==1.3.0
+msgpack @ file:///home/conda/feedstock_root/build_artifacts/msgpack-python_1648745999384/work
+multidict @ file:///home/conda/feedstock_root/build_artifacts/multidict_1648882415384/work
+multipledispatch @ file:///home/conda/feedstock_root/build_artifacts/multipledispatch_1721907546485/work
+narwhals==2.3.0
+nbclient @ file:///home/conda/feedstock_root/build_artifacts/nbclient_1734628800805/work
+nbconvert @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_nbconvert-core_1738067871/work
+nbformat @ file:///home/conda/feedstock_root/build_artifacts/nbformat_1733402752141/work
+nest_asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1733325553580/work
+netCDF4 @ file:///croot/netcdf4_1743512888672/work
+networkx @ file:///croot/networkx_1737039604450/work
+notebook @ file:///home/conda/feedstock_root/build_artifacts/notebook_1759152069573/work
+notebook_shim @ file:///home/conda/feedstock_root/build_artifacts/notebook-shim_1733408315203/work
+numba @ file:///croot/numba_1750798165355/work
+numcodecs @ file:///croot/numcodecs_1707513121886/work
+numexpr @ file:///croot/numexpr_1755766469354/work
+numpy @ file:///croot/numpy_and_numpy_base_1755590845055/work/dist/numpy-1.26.4-cp310-cp310-linux_x86_64.whl#sha256=1096d33ad9a9757a1b4b46634d809e894263fc8b78780bff36801684b6e8cc88
+nvidia-cublas-cu12==12.8.4.1
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+nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-runtime-cu12==12.8.90
+nvidia-cudnn-cu12==9.10.2.21
+nvidia-cufft-cu12==11.3.3.83
+nvidia-cufile-cu12==1.13.1.3
+nvidia-curand-cu12==10.3.9.90
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+nvidia-nccl-cu12==2.27.3
+nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvtx-cu12==12.8.90
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+odc-loader @ file:///home/conda/feedstock_root/build_artifacts/odc-loader_1743656085024/work
+odc-stac @ file:///home/conda/feedstock_root/build_artifacts/odc-stac_1746136311934/work
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+OWSLib==0.34.1
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+pandas @ file:///home/task_175982153789305/conda-bld/pandas_1759822248912/work/dist/pandas-2.3.3-cp310-cp310-linux_x86_64.whl#sha256=0de7c83109c411cc2a74419a396c92f65e3d1e457fb4d835e5f100cfb04393a7
+pandocfilters @ file:///home/conda/feedstock_root/build_artifacts/pandocfilters_1631603243851/work
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+platformdirs @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_platformdirs_1756227402/work
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+pure_eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1733569405015/work
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+pystac @ file:///home/conda/feedstock_root/build_artifacts/pystac_1758218055393/work
+pystac-client==0.9.0
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+python-json-logger @ file:///home/conda/feedstock_root/build_artifacts/python-json-logger_1677079630776/work
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+rfc3339_validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3339-validator_1733599910982/work
+rfc3986-validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3986-validator_1598024191506/work
+rfc3987==1.3.8
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+rioxarray @ file:///home/conda/feedstock_root/build_artifacts/rioxarray_1737140588464/work
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+ruamel.yaml @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml_1649033201098/work
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+Send2Trash @ file:///home/conda/feedstock_root/build_artifacts/send2trash_1733322040660/work
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+tifffile==2025.5.10
+timescale==0.0.9
+timezonefinder==8.0.0
+tinycss2 @ file:///home/conda/feedstock_root/build_artifacts/tinycss2_1729802851396/work
+tomli @ file:///croot/tomli_1753774587605/work
+toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1733736030883/work
+torch==2.8.0
+tornado @ file:///croot/tornado_1748956929273/work
+tqdm==4.67.1
+traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1733367359838/work
+traittypes==0.2.1
+triton==3.4.0
+types-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/types-python-dateutil_1759899809376/work
+typing-inspection==0.4.1
+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_typing_extensions_1756220668/work
+typing_utils @ file:///home/conda/feedstock_root/build_artifacts/typing_utils_1733331286120/work
+tzdata @ file:///croot/python-tzdata_1746123641790/work
+uc-micro-py==1.0.3
+unicodedata2 @ file:///croot/unicodedata2_1736541023050/work
+uri-template @ file:///home/conda/feedstock_root/build_artifacts/uri-template_1733323593477/work/dist
+urllib3 @ file:///croot/urllib3_1750775463400/work
+uvicorn==0.38.0
+wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1733231326287/work
+webcolors @ file:///home/conda/feedstock_root/build_artifacts/webcolors_1733359735138/work
+webencodings @ file:///home/conda/feedstock_root/build_artifacts/webencodings_1733236011802/work
+websocket-client @ file:///home/conda/feedstock_root/build_artifacts/websocket-client_1759928050786/work
+Werkzeug==3.1.3
+widgetsnbextension==4.0.14
+wrapt @ file:///home/conda/feedstock_root/build_artifacts/wrapt_1651495243689/work
+xarray @ file:///home/conda/feedstock_root/build_artifacts/xarray_1749743207754/work
+xgboost==3.1.2
+xyzservices @ file:///croot/xyzservices_1675159059961/work
+yarl==1.20.1
+zarr @ file:///home/conda/feedstock_root/build_artifacts/zarr_1733237197728/work
+zict @ file:///home/conda/feedstock_root/build_artifacts/zict_1733261551178/work
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1749421620841/work

requirements_api.txt

@@ -0,0 +1,3 @@
+fastapi
+uvicorn
+pydantic

start.sh

@@ -0,0 +1 @@
+uvicorn api_server:app --reload --host 0.0.0.0 --port 8000

test_new_features.py

@@ -0,0 +1,191 @@
+#!/usr/bin/env python3
+"""
+Demo script để test các chức năng mới của API
+"""
+
+import requests
+import json
+import time
+from pathlib import Path
+
+BASE_URL = "http://localhost:8000"
+
+def print_section(title):
+    print("\n" + "=" * 70)
+    print(f"  {title}")
+    print("=" * 70)
+
+def test_dashboard_statistics():
+    print_section("📊 Test Dashboard Statistics")
+    try:
+        response = requests.get(f"{BASE_URL}/api/dashboard/statistics")
+        if response.status_code == 200:
+            data = response.json()
+            print(f"✅ Success!")
+            print(f"   Models: {data['models']['total']}")
+            print(f"   Predictions: {data['predictions']['total']}")
+            print(f"   Reports: {data['reports']['total']}")
+        else:
+            print(f"❌ Error: {response.status_code}")
+    except Exception as e:
+        print(f"❌ Exception: {e}")
+
+def test_accuracy_trends():
+    print_section("📈 Test Accuracy Trends")
+    try:
+        response = requests.get(f"{BASE_URL}/api/dashboard/accuracy-trends")
+        if response.status_code == 200:
+            data = response.json()
+            print(f"✅ Success!")
+            print(f"   Trends: {len(data['trends'])} records")
+            print(f"   Models: {data['models']}")
+        else:
+            print(f"❌ Error: {response.status_code}")
+    except Exception as e:
+        print(f"❌ Exception: {e}")
+
+def test_class_distribution():
+    print_section("📊 Test Class Distribution")
+    try:
+        # First, get list of models
+        response = requests.get(f"{BASE_URL}/api/models/list")
+        if response.status_code == 200:
+            models = response.json()['models']
+            if models:
+                model_filename = models[0]['filename']
+                print(f"   Using model: {model_filename}")
+                
+                # Get class distribution
+                response = requests.get(f"{BASE_URL}/api/dashboard/class-distribution/{model_filename}")
+                if response.status_code == 200:
+                    data = response.json()
+                    print(f"✅ Success!")
+                    print(f"   Total samples: {data['total_samples']}")
+                    print(f"   Classes: {list(data['class_distribution'].keys())}")
+                else:
+                    print(f"❌ Error: {response.status_code}")
+            else:
+                print("⚠️  No models found")
+        else:
+            print(f"❌ Error getting models: {response.status_code}")
+    except Exception as e:
+        print(f"❌ Exception: {e}")
+
+def test_batch_status():
+    print_section("🔄 Test Batch Status")
+    try:
+        response = requests.get(f"{BASE_URL}/api/batch/status")
+        if response.status_code == 200:
+            data = response.json()
+            print(f"✅ Success!")
+            print(f"   Queued: {data['queue']['queued']}")
+            print(f"   Running: {data['queue']['running']}")
+            print(f"   Completed: {data['queue']['completed']}")
+            print(f"   Failed: {data['queue']['failed']}")
+        else:
+            print(f"❌ Error: {response.status_code}")
+    except Exception as e:
+        print(f"❌ Exception: {e}")
+
+def test_batch_prediction_demo():
+    print_section("🚀 Test Batch Prediction (Demo)")
+    try:
+        # Get a model
+        response = requests.get(f"{BASE_URL}/api/models/list")
+        if response.status_code != 200:
+            print("❌ Cannot get models list")
+            return
+        
+        models = response.json()['models']
+        if not models:
+            print("⚠️  No models available for testing")
+            return
+        
+        model_filename = models[0]['filename']
+        print(f"   Using model: {model_filename}")
+        
+        # Create test batch
+        batch_config = {
+            "model_filename": model_filename,
+            "items": [
+                {
+                    "name": "Test_Region_1",
+                    "min_lon": 105.6,
+                    "min_lat": 9.3,
+                    "max_lon": 105.7,
+                    "max_lat": 9.4,
+                    "start_date": "2023-03-01",
+                    "end_date": "2023-03-31",
+                    "max_scenes": 5,
+                    "cloud_cover": 30,
+                    "resolution": 20
+                }
+            ],
+            "auto_retry": True,
+            "max_retries": 2
+        }
+        
+        print("   Creating batch job...")
+        response = requests.post(
+            f"{BASE_URL}/api/batch/start",
+            json=batch_config
+        )
+        
+        if response.status_code == 200:
+            data = response.json()
+            print(f"✅ Success!")
+            print(f"   {data['message']}")
+            print(f"   Batch ID: {data['batch_id']}")
+            
+            # Check status after a moment
+            time.sleep(2)
+            response = requests.get(f"{BASE_URL}/api/batch/status")
+            if response.status_code == 200:
+                status = response.json()
+                print(f"   Current queue: {status['queue']}")
+        else:
+            print(f"❌ Error: {response.status_code} - {response.text}")
+    except Exception as e:
+        print(f"❌ Exception: {e}")
+
+def test_reports_list():
+    print_section("📄 Test Reports List")
+    try:
+        response = requests.get(f"{BASE_URL}/api/reports/list")
+        if response.status_code == 200:
+            data = response.json()
+            print(f"✅ Success!")
+            print(f"   Total reports: {data['count']}")
+            if data['reports']:
+                print(f"   Latest report: {data['reports'][0]['filename']}")
+        else:
+            print(f"❌ Error: {response.status_code}")
+    except Exception as e:
+        print(f"❌ Exception: {e}")
+
+def main():
+    print("=" * 70)
+    print("  🧪 API Testing Suite - New Features")
+    print("=" * 70)
+    print(f"\n  Base URL: {BASE_URL}")
+    print(f"  Đảm bảo server đang chạy: python api_server.py")
+    
+    input("\n  Press ENTER to start testing...")
+    
+    # Run all tests
+    test_dashboard_statistics()
+    test_accuracy_trends()
+    test_class_distribution()
+    test_reports_list()
+    test_batch_status()
+    test_batch_prediction_demo()
+    
+    print("\n" + "=" * 70)
+    print("  ✅ Testing completed!")
+    print("=" * 70)
+    print(f"\n  Dashboard: {BASE_URL}/dashboard")
+    print(f"  API Docs: {BASE_URL}/docs")
+    print("=" * 70 + "\n")
+
+if __name__ == "__main__":
+    main()

train_module.py

@@ -0,0 +1,558 @@
+"""
+Training module for land classification using Sentinel-2 and Sentinel-1 data
+from Microsoft Planetary Computer STAC API
+"""
+
+import numpy as np
+import xarray as xr
+import geopandas as gpd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import classification_report, confusion_matrix
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.svm import SVC
+from xgboost import XGBClassifier
+import joblib
+from datetime import datetime
+import json
+import os
+import warnings
+import hashlib
+from pathlib import Path
+warnings.filterwarnings('ignore')
+
+# PyTorch for CNN
+try:
+    import torch
+    import torch.nn as nn
+    import torch.nn.functional as F
+    import torch.optim as optim
+    from torch.utils.data import TensorDataset, DataLoader
+    PYTORCH_AVAILABLE = True
+except ImportError:
+    PYTORCH_AVAILABLE = False
+    print("Warning: PyTorch not available. CNN model will not work.")
+
+# Define CNN model class for PyTorch
+class CNNClassifier(nn.Module):
+    def __init__(self, n_features, n_classes):
+        super(CNNClassifier, self).__init__()
+        self.n_features = n_features
+        self.n_classes = n_classes
+        
+        # For small feature sets (like 3 features), use simpler architecture
+        if n_features < 8:
+            # Simple fully connected network for small features
+            self.use_conv = False
+            self.fc1 = nn.Linear(n_features, 64)
+            self.dropout1 = nn.Dropout(0.3)
+            self.fc2 = nn.Linear(64, 128)
+            self.dropout2 = nn.Dropout(0.5)
+            self.fc3 = nn.Linear(128, n_classes)
+        else:
+            # CNN architecture for larger feature sets
+            self.use_conv = True
+            self.conv1 = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, padding=1)
+            self.pool1 = nn.MaxPool1d(kernel_size=2)
+            self.conv2 = nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
+            self.pool2 = nn.MaxPool1d(kernel_size=2)
+            
+            # Calculate size after convolutions
+            conv_output_size = (n_features // 2 // 2) * 64
+            
+            # Fully connected layers
+            self.fc1 = nn.Linear(conv_output_size, 128)
+            self.dropout = nn.Dropout(0.5)
+            self.fc2 = nn.Linear(128, n_classes)
+    
+    def forward(self, x):
+        # x shape: (batch, n_features) or (batch, 1, n_features)
+        if self.use_conv:
+            # CNN path for larger feature sets
+            if len(x.shape) == 2:
+                x = x.unsqueeze(1)  # Add channel dimension
+            x = F.relu(self.conv1(x))
+            x = self.pool1(x)
+            x = F.relu(self.conv2(x))
+            x = self.pool2(x)
+            x = x.view(x.size(0), -1)  # Flatten
+            x = F.relu(self.fc1(x))
+            x = self.dropout(x)
+            x = self.fc2(x)
+        else:
+            # Fully connected path for small feature sets
+            if len(x.shape) == 3:
+                x = x.squeeze(1)  # Remove channel dimension if present
+            x = F.relu(self.fc1(x))
+            x = self.dropout1(x)
+            x = F.relu(self.fc2(x))
+            x = self.dropout2(x)
+            x = self.fc3(x)
+        return x
+    
+    def predict(self, X):
+        """Scikit-learn style predict method"""
+        self.eval()
+        with torch.no_grad():
+            if isinstance(X, np.ndarray):
+                X = torch.FloatTensor(X)
+            # Handle both 2D and 3D inputs
+            if not self.use_conv and len(X.shape) == 3:
+                X = X.squeeze(1)
+            elif self.use_conv and len(X.shape) == 2:
+                X = X.unsqueeze(1)
+            outputs = self(X)
+            _, predicted = torch.max(outputs, 1)
+            return predicted.cpu().numpy()
+    
+    def score(self, X, y):
+        """Scikit-learn style score method"""
+        predictions = self.predict(X)
+        if isinstance(y, torch.Tensor):
+            y = y.cpu().numpy()
+        return np.mean(predictions == y)
+
+# Microsoft Planetary Computer imports
+import planetary_computer
+from pystac_client import Client
+from odc.stac import load as stac_load
+
+
+def train_model(
+    bbox=[105.6, 9.3, 106.2, 9.8],
+    time_range='2023-03-01/2023-05-31',
+    max_scenes=12,
+    cloud_cover=30,
+    resolution=20,
+    training_shapefile='train/ST_training data_updated_1130points_new.shp',
+    model_type='xgboost',
+    n_estimators=100,
+    max_depth=20,
+    learning_rate=0.1,
+    use_gpu=True,
+    use_cache=True,
+    test_size=0.2,
+    output_model_path=None,
+    status_callback=None,
+    cancel_check=None
+):
+    """
+    Train a land classification model using Sentinel-2 and Sentinel-1 data
+    
+    Args:
+        bbox: [min_lon, min_lat, max_lon, max_lat]
+        time_range: "YYYY-MM-DD/YYYY-MM-DD"
+        max_scenes: maximum number of scenes to load
+        cloud_cover: maximum cloud cover percentage
+        resolution: resolution in meters (e.g., 20)
+        training_shapefile: path to training shapefile
+        n_estimators: number of trees for XGBoost
+        max_depth: maximum tree depth
+        learning_rate: learning rate for XGBoost
+        use_gpu: whether to use GPU for training
+        output_model_path: path to save trained model (auto-generated if None)
+        status_callback: Optional callback function to report progress
+        cancel_check: Optional function that returns True if training should be cancelled
+        test_size: Fraction of data to use for test set (0-1)
+    
+    Returns:
+        Dictionary containing training results
+    """
+    
+    def update_status(message, progress=None):
+        """Helper to update status"""
+        if status_callback:
+            # Try calling with both arguments, fallback to just message
+            try:
+                status_callback(message, progress)
+            except TypeError:
+                status_callback(message)
+        print(message)
+    
+    def check_cancellation():
+        """Check if training should be cancelled"""
+        if cancel_check and cancel_check():
+            raise InterruptedError("Training cancelled by user")
+    
+    try:
+        # Auto-generate output path if not provided
+        if output_model_path is None:
+            timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+            output_model_path = f'model_train/model_{model_type}_{timestamp}.joblib'
+        
+        # ============ CACHE SYSTEM ============
+        # Create cache directory
+        cache_dir = Path("dataset_cache")
+        cache_dir.mkdir(exist_ok=True)
+        
+        # Generate cache key from parameters
+        cache_params = f"{bbox}_{time_range}_{max_scenes}_{cloud_cover}_{resolution}"
+        cache_key = hashlib.md5(cache_params.encode()).hexdigest()
+        cache_file = cache_dir / f"training_data_{cache_key}.joblib"
+        
+        features = None
+        labels = None
+        
+        # Try to load from cache
+        if use_cache and cache_file.exists():
+            update_status(f"📦 Loading cached dataset from {cache_file.name}...", 5)
+            try:
+                cached_data = joblib.load(cache_file)
+                features = cached_data['features']
+                labels = cached_data['labels']
+                update_status(f"✅ Loaded {len(features)} samples from cache (skipped satellite download!)", 50)
+            except Exception as e:
+                update_status(f"⚠️ Cache load failed: {str(e)}, downloading fresh data...", 10)
+                features = None
+        
+        # If no cache or cache failed, download data
+        if features is None:
+            update_status("📡 Cache not found or disabled, downloading satellite data...", 10)
+            
+            # Connect to Microsoft Planetary Computer
+            update_status("Connecting to Microsoft Planetary Computer...", 12)
+            catalog = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1")
+            check_cancellation()
+            
+            # Search for Sentinel-2 scenes
+            update_status("Searching for Sentinel-2 scenes...", 10)
+            query_s2 = catalog.search(
+                collections=["sentinel-2-l2a"],
+                bbox=bbox,
+                datetime=time_range,
+                query={"eo:cloud_cover": {"lt": cloud_cover}}
+            )
+            items_s2 = list(query_s2.item_collection())
+            
+            check_cancellation()
+            
+            # Limit scenes
+            if len(items_s2) > max_scenes:
+                step = len(items_s2) // max_scenes
+                items_s2 = items_s2[::step][:max_scenes]
+            
+            update_status(f"Found {len(items_s2)} Sentinel-2 scenes", 20)
+            
+            # Sign and load Sentinel-2 data
+            update_status("Loading Sentinel-2 data...", 25)
+            items_s2 = [planetary_computer.sign(item) for item in items_s2]
+            ds_s2 = stac_load(
+                items_s2,
+                bands=["B04", "B08", "SCL"],
+                crs="EPSG:32648",
+                resolution=resolution,
+                bbox=bbox,
+                patch_url=planetary_computer.sign,
+                fail_on_error=False,
+            )
+            ds_s2 = ds_s2.rename({"B04": "red", "B08": "nir", "SCL": "scl"})
+            
+            check_cancellation()
+            
+            # Search for Sentinel-1 scenes
+            update_status("Searching for Sentinel-1 scenes...", 35)
+            query_s1 = catalog.search(
+                collections=["sentinel-1-rtc"],
+                bbox=bbox,
+                datetime=time_range,
+            )
+            items_s1 = list(query_s1.item_collection())
+            
+            # Limit scenes
+            if len(items_s1) > max_scenes:
+                step = len(items_s1) // max_scenes
+                items_s1 = items_s1[::step][:max_scenes]
+            
+            update_status(f"Found {len(items_s1)} Sentinel-1 scenes", 40)
+            
+            # Sign and load Sentinel-1 data
+            update_status("Loading Sentinel-1 data...", 45)
+            items_s1 = [planetary_computer.sign(item) for item in items_s1]
+            ds_s1 = stac_load(
+                items_s1,
+                bands=["vv", "vh"],
+                crs="EPSG:32648",
+                resolution=resolution,
+                bbox=bbox,
+                patch_url=planetary_computer.sign,
+                fail_on_error=False,
+            )
+            
+            # Convert to dB
+            ds_s1['vv_db'] = 10 * np.log10(ds_s1['vv'].where(ds_s1['vv'] > 0))
+            ds_s1['vh_db'] = 10 * np.log10(ds_s1['vh'].where(ds_s1['vh'] > 0))
+            
+            check_cancellation()
+            
+            # Calculate NDVI
+            update_status("Calculating NDVI...", 50)
+            ndvi = (ds_s2['nir'] - ds_s2['red']) / (ds_s2['nir'] + ds_s2['red'] + 1e-8)
+            
+            # Apply cloud mask
+            cloud_mask = ds_s2['scl'].isin([1, 3, 8, 9, 10])
+            ndvi_masked = ndvi.where(~cloud_mask)
+            ndvi_mean = ndvi_masked.mean(dim='time')
+            
+            # Load training data
+            update_status("Loading training data...", 55)
+            train_gdf = gpd.read_file(training_shapefile)
+            
+            if train_gdf.crs != 'EPSG:32648':
+                train_gdf = train_gdf.to_crs('EPSG:32648')
+            
+            # Auto-detect label column
+            label_column = None
+            for col in ['HT_code', 'Ma_LU', 'LU2022', 'Hientrang', 'class', 'Class', 'CLASS']:
+                if col in train_gdf.columns:
+                    label_column = col
+                    break
+            
+            if label_column is None:
+                raise ValueError(f"Cannot find label column in shapefile. Available: {list(train_gdf.columns)}")
+            
+            # Extract features
+            update_status("Extracting features from training points...", 60)
+            features = []
+            labels = []
+            
+            for idx, row in train_gdf.iterrows():
+                point = row.geometry
+                x_coord = point.x
+                y_coord = point.y
+                label = row[label_column]
+                
+                try:
+                    ndvi_val = ndvi_mean.sel(x=x_coord, y=y_coord, method='nearest').values
+                    vh_val = ds_s1['vh_db'].sel(x=x_coord, y=y_coord, method='nearest').mean(dim='time').values
+                    vv_val = ds_s1['vv_db'].sel(x=x_coord, y=y_coord, method='nearest').mean(dim='time').values
+                    
+                    feature_vec = [ndvi_val, vh_val, vv_val]
+                    
+                    if not np.isnan(feature_vec).any():
+                        features.append(feature_vec)
+                        labels.append(label)
+                except:
+                    continue
+            
+            features = np.array(features)
+            labels = np.array(labels)
+            
+            check_cancellation()
+            
+            update_status(f"Extracted {len(features)} valid training samples", 70)
+            
+            # ============ SAVE TO CACHE ============
+            if use_cache:
+                update_status(f"💾 Saving dataset to cache for future use...", 72)
+                try:
+                    cache_data = {
+                        'features': features,
+                        'labels': labels,
+                        'bbox': bbox,
+                        'time_range': time_range,
+                        'resolution': resolution,
+                        'timestamp': datetime.now().isoformat()
+                    }
+                    joblib.dump(cache_data, cache_file)
+                    update_status(f"✅ Cached to {cache_file.name}", 75)
+                except Exception as e:
+                    update_status(f"⚠️ Cache save failed: {str(e)}", 75)
+        
+        # Encode labels
+        label_encoder = LabelEncoder()
+        labels_encoded = label_encoder.fit_transform(labels)
+        
+        # Split data
+        X_train, X_test, y_train, y_test = train_test_split(
+            features, labels_encoded, test_size=test_size, random_state=42, stratify=labels_encoded
+        )
+        
+        # Train model based on selected type
+        update_status(f"Training {model_type.upper()} model...", 75)
+        
+        device = 'cuda:0' if use_gpu else 'cpu'
+        
+        if model_type == 'xgboost':
+            model = XGBClassifier(
+                n_estimators=n_estimators,
+                max_depth=max_depth,
+                learning_rate=learning_rate,
+                device=device if use_gpu else 'cpu',
+                tree_method='hist',
+                random_state=42,
+                eval_metric='mlogloss',
+                verbosity=0
+            )
+        elif model_type == 'random_forest':
+            model = RandomForestClassifier(
+                n_estimators=n_estimators,
+                max_depth=max_depth,
+                random_state=42,
+                n_jobs=-1,  # Use all cores
+                verbose=0
+            )
+        elif model_type == 'decision_tree':
+            model = DecisionTreeClassifier(
+                max_depth=max_depth,
+                random_state=42
+            )
+        elif model_type == 'svm':
+            model = SVC(
+                kernel='rbf',
+                random_state=42,
+                verbose=False
+            )
+        elif model_type == 'cnn':
+            if not PYTORCH_AVAILABLE:
+                raise ImportError("PyTorch is required for CNN. Install: pip install torch")
+            
+            # CNN requires reshaping data
+            n_features = X_train.shape[1]
+            n_classes = len(np.unique(y_train))
+            
+            # Build PyTorch CNN model
+            device = torch.device('cuda' if torch.cuda.is_available() and use_gpu else 'cpu')
+            update_status(f"Building CNN model on {device}...", 75)
+            
+            model = CNNClassifier(n_features, n_classes).to(device)
+            
+            # Convert to PyTorch tensors
+            X_train_tensor = torch.FloatTensor(X_train).unsqueeze(1)  # Add channel dim: (N, 1, features)
+            y_train_tensor = torch.LongTensor(y_train)
+            X_test_tensor = torch.FloatTensor(X_test).unsqueeze(1)
+            y_test_tensor = torch.LongTensor(y_test)
+            
+            # Create data loaders
+            train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
+            train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+            
+            # Loss and optimizer
+            criterion = nn.CrossEntropyLoss()
+            optimizer = optim.Adam(model.parameters(), lr=0.001)
+            
+            # Train CNN
+            update_status("Training CNN model with PyTorch...", 80)
+            epochs = min(50, n_estimators // 2)  # Use n_estimators as epochs
+            
+            model.train()
+            for epoch in range(epochs):
+                epoch_loss = 0.0
+                for batch_X, batch_y in train_loader:
+                    batch_X, batch_y = batch_X.to(device), batch_y.to(device)
+                    
+                    optimizer.zero_grad()
+                    outputs = model(batch_X)
+                    loss = criterion(outputs, batch_y)
+                    loss.backward()
+                    optimizer.step()
+                    
+                    epoch_loss += loss.item()
+                
+                if (epoch + 1) % 10 == 0:
+                    avg_loss = epoch_loss / len(train_loader)
+                    update_status(f"CNN Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}", 80 + (epoch / epochs) * 10)
+            
+            # Move model to CPU for saving (compatible with non-GPU systems)
+            model = model.cpu()
+            model.device_used = str(device)
+        else:
+            raise ValueError(f"Unknown model type: {model_type}. Choose: xgboost, random_forest, decision_tree, svm, cnn")
+        
+        # Fit non-CNN models
+        if model_type != 'cnn':
+            model.fit(X_train, y_train)
+        
+        # Evaluate
+        update_status("Evaluating model...", 90)
+        if model_type == 'cnn':
+            # PyTorch CNN evaluation
+            train_score = model.score(X_train, y_train)
+            test_score = model.score(X_test, y_test)
+            y_pred = model.predict(X_test)
+        else:
+            train_score = model.score(X_train, y_train)
+            test_score = model.score(X_test, y_test)
+            y_pred = model.predict(X_test)
+        
+        # Generate classification report and confusion matrix
+        update_status("Generating classification report...", 92)
+        class_names = label_encoder.classes_.tolist()
+        
+        # Classification report as dict
+        from sklearn.metrics import classification_report, confusion_matrix
+        cls_report = classification_report(y_test, y_pred, target_names=class_names, output_dict=True, zero_division=0)
+        
+        # Confusion matrix
+        conf_matrix = confusion_matrix(y_test, y_pred).tolist()
+        
+        # Save model
+        update_status("Saving model...", 95)
+        os.makedirs(os.path.dirname(output_model_path), exist_ok=True)
+        joblib.dump({'model': model, 'label_encoder': label_encoder}, output_model_path)
+        
+        # Save model info
+        info = {
+            "timestamp": datetime.now().isoformat(),
+            "data_source": "Microsoft Planetary Computer STAC",
+            "collections": ["sentinel-2-l2a", "sentinel-1-rtc"],
+            "features": ["NDVI_mean", "VH_dB_mean", "VV_dB_mean"],
+            "training_samples": len(X_train),
+            "testing_samples": len(X_test),
+            "test_size": test_size,
+            "train_accuracy": float(train_score),
+            "test_accuracy": float(test_score),
+            "model_type": model_type,
+            "device": device if model_type == 'xgboost' else 'cpu',
+            "n_estimators": n_estimators if model_type in ['xgboost', 'random_forest', 'cnn'] else None,
+            "max_depth": max_depth if model_type != 'cnn' else None,
+            "learning_rate": learning_rate if model_type == 'xgboost' else None,
+            "cnn_epochs": min(50, n_estimators // 2) if model_type == 'cnn' else None,
+            "n_features": X_train.shape[1],
+            "n_classes": len(np.unique(y_train)),
+            "class_names": class_names,
+            "classification_report": cls_report,
+            "confusion_matrix": conf_matrix,
+            "bbox": bbox,
+            "time_range": time_range,
+            "resolution": resolution
+        }
+        
+        info_path = output_model_path.replace('.joblib', '_info.json')
+        with open(info_path, 'w') as f:
+            json.dump(info, f, indent=2)
+        
+        update_status("Training complete!", 100)
+        
+        return {
+            "success": True,
+            "model_path": output_model_path,
+            "info_path": info_path,
+            "train_accuracy": train_score,
+            "test_accuracy": test_score,
+            "training_samples": len(X_train),
+            "testing_samples": len(X_test),
+            "test_size": test_size,
+            "classes": class_names,
+            "classification_report": cls_report,
+            "confusion_matrix": conf_matrix,
+            "model_type": model_type,
+            "bbox": bbox,
+            "time_range": time_range,
+            "resolution": resolution
+        }
+        
+    except InterruptedError as e:
+        update_status(f"Cancelled: {str(e)}", -1)
+        return {
+            "success": False,
+            "error": str(e),
+            "cancelled": True
+        }
+        
+    except Exception as e:
+        update_status(f"Error: {str(e)}", -1)
+        return {
+            "success": False,
+            "error": str(e)
+        }