"""
Tạo metadata cho model_odc.joblib (legacy model)
"""

import json
from pathlib import Path

# Metadata cho model_odc.joblib  
# Model này là GridSearchCV Pipeline với 39 features (temporal mode)
# Features: NDVI time series + NDWI time series + NDBI time series + radar features

# Calculate feature names for temporal mode with 12 timesteps
# (12 NDVI + 12 NDWI + 12 NDBI + 3 radar = 39 features)
n_timesteps = 12
feature_names = []

# NDVI time series
for t in range(n_timesteps):
    feature_names.append(f"NDVI_t{t+1}")

# NDWI time series
for t in range(n_timesteps):
    feature_names.append(f"NDWI_t{t+1}")

# NDBI time series
for t in range(n_timesteps):
    feature_names.append(f"NDBI_t{t+1}")

# Radar features
feature_names.extend(["VH_db_mean", "VV_db_mean", "VH_VV_ratio"])

metadata = {
    "timestamp": "2025-12-20T10:00:00",
    "data_source": "Unknown (Legacy model)",
    "collections": ["sentinel-2-l2a", "sentinel-1-rtc"],
    "features": feature_names,
    "feature_mode": "temporal",  # IMPORTANT: temporal mode with 39 features
    "training_samples": None,
    "testing_samples": None,
    "test_size": 0.2,
    "train_accuracy": None,
    "test_accuracy": None,
    "model_type": "random_forest",  # GridSearchCV with RandomForest
    "device": "cpu",
    "n_estimators": 100,
    "max_depth": None,
    "learning_rate": None,
    "cnn_epochs": None,
    "n_features": 39,  # GridSearchCV expects 39 features!
    "n_classes": 8,
    "class_names": [
        "Lua tom",  # 0
        "Lua",      # 1
        "CHN",      # 2
        "CLN",      # 3
        "TS",       # 4
        "Song",     # 5
        "Dat xay dung",  # 6
        "Rung"      # 7
    ],
    "classification_report": None,
    "confusion_matrix": None,
    "bbox": None,
    "time_range": None,
    "resolution": 10,
    "notes": "Legacy GridSearchCV Pipeline model with 39 temporal features (12 timesteps each for NDVI/NDWI/NDBI + 3 radar features). Requires temporal mode feature extraction."
}

# Save metadata
model_train_dir = Path("model_train")
metadata_file = model_train_dir / "model_odc_info.json"

print("Creating metadata for model_odc.joblib...")
print(f"Saving to: {metadata_file}")

with open(metadata_file, 'w') as f:
    json.dump(metadata, f, indent=2)

print("✅ Metadata created successfully!")
print("\nMetadata content:")
print(json.dumps(metadata, indent=2))