Cập nhật mã nguồn và file Colab Cache

api_server.py

@@ -507,6 +507,57 @@ async def get_cloud_removal_methods():
     }
 
 
+@app.get("/api/ndvi-forecast/models")
+async def list_ndvi_forecast_models():
+    """Liệt kê các NDVI forecast models đã train"""
+    model_dir = Path("ndvi_forecast_model")
+    if not model_dir.exists():
+        return {"models": [], "count": 0}
+    
+    models = []
+    # Search for all models
+    for model_file in list(model_dir.rglob("*.pth")) + list(model_dir.rglob("*.joblib")):
+        try:
+            import json
+            
+            # Try to load metadata from .json sidecar file first
+            metadata_file = model_file.with_name(model_file.stem + "_info.json")
+            if metadata_file.exists():
+                try:
+                    with open(metadata_file, 'r') as f:
+                        metadata = json.load(f)
+                    
+                    models.append({
+                        "filename": model_file.name,
+                        "path": str(model_file),
+                        "model_type": metadata.get('model_type', 'Unknown'),
+                        "target": metadata.get('target', 'NDVI'),
+                        "rmse": metadata.get('rmse', 0),
+                        "mae": metadata.get('mae', 0),
+                        "epoch": metadata.get('epoch', 0),
+                        "created": model_file.stat().st_mtime,
+                        "size_mb": model_file.stat().st_size / (1024 * 1024),
+                    })
+                    continue
+                except Exception as e:
+                    print(f"Error reading JSON {metadata_file}: {e}")
+                    
+            # Fallback for models without metadata
+            models.append({
+                "filename": model_file.name,
+                "path": str(model_file),
+                "model_type": "Unknown",
+                "created": model_file.stat().st_mtime,
+                "size_mb": model_file.stat().st_size / (1024 * 1024)
+            })
+        except Exception as e:
+            print(f"Error loading model info for {model_file}: {e}")
+            
+    # Sort by creation time (newest first)
+    models.sort(key=lambda x: x['created'], reverse=True)
+    return {"models": models, "count": len(models)}
+
+
 @app.get("/api/cloud-removal/models")
 async def list_cloud_removal_models():
     """Liệt kê các cloud removal models đã train"""