Update app.py

app.py

@@ -35,21 +35,27 @@ model = None
 # IMPORTANT: Your class order from training (alphabetical from image_dataset_from_directory)
 class_labels = ["glass", "metal", "organic", "paper", "plastic"]
 
+
 def load_model():
-    """Load the trained TensorFlow/Keras model"""
+    """Load the trained TensorFlow/Keras model from model/ directory"""
     try:
-        # Try loading different formats in order of preference
+        # Try loading different formats from model/ directory
         model_files = [
             'model/waste_model.keras',  # Keras format (recommended)
             'model/waste_model.h5',     # H5 format
-            'model/best_model.keras'    # Checkpoint from training
+            'model/best_model.keras',   # Checkpoint from training
+            'waste_model.keras',        # Fallback to root (original)
+            'waste_model.h5'            # Fallback to root
         ]
         
         model = None
+        loaded_from = None
+        
         for model_file in model_files:
             if os.path.exists(model_file):
                 try:
                     model = tf.keras.models.load_model(model_file)
+                    loaded_from = model_file
                     logger.info(f"Model loaded successfully from {model_file}")
                     break
                 except Exception as e:
@@ -57,7 +63,7 @@ def load_model():
                     continue
         
         if model is None:
-            logger.error("No model file found. Creating dummy model for testing.")
+            logger.error("No model file found in any location. Creating dummy model for testing.")
             # Create dummy model with same architecture for testing
             model = tf.keras.Sequential([
                 tf.keras.layers.Rescaling(1./255, input_shape=(224, 224, 3)),
@@ -68,6 +74,8 @@ def load_model():
                 tf.keras.layers.Dense(5, activation='softmax')
             ])
             logger.warning("Using dummy model - predictions will be random!")
+        else:
+            logger.info(f"Successfully loaded model from: {loaded_from}")
             
         return model
         
@@ -142,15 +150,19 @@ async def health_check():
         # Quick model test
         dummy_input = np.random.random((1, 224, 224, 3)).astype(np.float32)
         prediction = model.predict(dummy_input, verbose=0)
-        model_working = prediction is not None
+        
+        # Check if we're using the dummy model (random predictions)
+        is_dummy_model = np.allclose(prediction.sum(), 1.0)  # Should sum to ~1
+        model_status = "real_model" if not is_dummy_model else "dummy_model"
         
         return {
             "status": "healthy",
+            "model_status": model_status,
             "model_loaded": model is not None,
-            "model_working": model_working,
             "classes": class_labels,
             "input_shape": "(224, 224, 3)",
-            "model_type": "TensorFlow/Keras MobileNetV2"
+            "model_type": "TensorFlow/Keras MobileNetV2",
+            "prediction_sample": prediction[0].tolist()  # Show first prediction
         }
     except Exception as e:
         return {
@@ -159,7 +171,33 @@ async def health_check():
             "model_loaded": model is not None,
             "classes": class_labels
         }
-
+@app.on_event("startup")
+async def startup_event():
+    """Load model on startup"""
+    global model
+    try:
+        # Log available files for debugging
+        logger.info("Available files in root:")
+        for file in os.listdir('.'):
+            logger.info(f"  {file}")
+        
+        if os.path.exists('model'):
+            logger.info("Available files in model/ directory:")
+            for file in os.listdir('model'):
+                logger.info(f"  model/{file}")
+        
+        model = load_model()
+        logger.info("API startup complete")
+        
+        # Test model with dummy input
+        dummy_input = np.random.random((1, 224, 224, 3)).astype(np.float32)
+        _ = model.predict(dummy_input, verbose=0)
+        logger.info("Model test prediction successful")
+        
+    except Exception as e:
+        logger.error(f"Startup failed: {e}")
+        raise
+        
 @app.post("/classify")
 async def classify_image(file: UploadFile = File(...)):
     """