Upload app.py

app.py

@@ -5,6 +5,11 @@ from PIL import Image
 from huggingface_hub import hf_hub_download
 import os
 import pandas as pd
+import logging
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 # Configuration
 MODEL_REPO = "Ahmedhassan54/Image-Classification"
@@ -13,67 +18,77 @@ MODEL_FILE = "best_model.h5"
 # Download model from Hugging Face Hub
 def load_model_from_hf():
     try:
-        print("Attempting to load model...")
-        if not os.path.exists(MODEL_FILE):
-            print("Model file not found locally. Downloading...")
-            model_path = hf_hub_download(
-                repo_id=MODEL_REPO,
-                filename=MODEL_FILE,
-                cache_dir="."
-            )
-            print(f"Model downloaded to: {model_path}")
-            os.system(f"cp {model_path} {MODEL_FILE}")
-            print("Model copied to working directory")
+        logger.info("Attempting to load model...")
+        
+        # Check if model exists in cache
+        model_path = hf_hub_download(
+            repo_id=MODEL_REPO,
+            filename=MODEL_FILE,
+            cache_dir=".",
+            force_download=True  # Ensure fresh download
+        )
+        logger.info(f"Model downloaded to: {model_path}")
         
-        print("Loading model...")
-        model = tf.keras.models.load_model(MODEL_FILE)
-        print("Model loaded successfully!")
+        # Load model
+        logger.info("Loading model...")
+        model = tf.keras.models.load_model(model_path)
+        logger.info("Model loaded successfully!")
         return model
+        
     except Exception as e:
-        print(f"Model loading failed: {str(e)}")
-        raise gr.Error(f"Model loading failed: {str(e)}")
+        logger.error(f"Model loading failed: {str(e)}")
+        raise gr.Error(f"⚠️ Model loading failed: {str(e)}. Check the logs for details.")
 
 # Load model when the app starts
 try:
     model = load_model_from_hf()
-except:
+except Exception as e:
     model = None
-    print("Proceeding without model - for debugging purposes")
+    logger.error(f"Proceeding without model due to: {str(e)}")
 
 def classify_image(image):
     try:
-        print("\nClassification started...")
+        logger.info("\nClassification started...")
         
         # Debug: Check input type
-        print(f"Input type: {type(image)}")
+        logger.info(f"Input type: {type(image)}")
+        
+        if image is None:
+            raise ValueError("No image provided")
         
         # Convert image if needed
         if isinstance(image, np.ndarray):
-            print("Converting numpy array to PIL Image")
+            logger.info("Converting numpy array to PIL Image")
             image = Image.fromarray(image)
+        elif not isinstance(image, Image.Image):
+            raise ValueError(f"Unexpected image type: {type(image)}")
         
         # Preprocess image
-        print("Preprocessing image...")
+        logger.info("Preprocessing image...")
         image = image.resize((150, 150))
         image_array = np.array(image) / 255.0
-        image_array = np.expand_dims(image_array, axis=0)
+        
+        # Add batch dimension
+        if len(image_array.shape) == 3:
+            image_array = np.expand_dims(image_array, axis=0)
+        
+        logger.info(f"Image array shape: {image_array.shape}")
         
         # Make prediction
-        print("Making prediction...")
+        logger.info("Making prediction...")
         if model is None:
-            # For debugging when model fails to load
-            confidence = 0.75  # Mock value
-            print("Using mock prediction (model not loaded)")
-        else:
-            prediction = model.predict(image_array, verbose=0)
-            confidence = float(prediction[0][0])
+            raise gr.Error("Model failed to load. Cannot make predictions.")
         
-        print(f"Raw confidence: {confidence}")
+        prediction = model.predict(image_array, verbose=0)
+        logger.info(f"Raw prediction: {prediction}")
+        
+        confidence = float(prediction[0][0])
+        logger.info(f"Confidence score: {confidence}")
         
         # Format outputs
         label_output = {
-            "Cat": 1 - confidence,
-            "Dog": confidence
+            "Cat": round(1 - confidence, 4),
+            "Dog": round(confidence, 4)
         }
         
         # Create dataframe for bar plot
@@ -82,14 +97,14 @@ def classify_image(image):
             'Confidence': [1 - confidence, confidence]
         })
         
-        print("Classification successful!")
-        print(f"Results: {label_output}")
+        logger.info("Classification successful!")
+        logger.info(f"Results: {label_output}")
         
         return label_output, plot_data
         
     except Exception as e:
-        print(f"Error during classification: {str(e)}")
-        raise gr.Error(f"Classification error: {str(e)}")
+        logger.error(f"Error during classification: {str(e)}", exc_info=True)
+        raise gr.Error(f"🔴 Classification failed: {str(e)}")
 
 # Custom CSS
 css = """
@@ -99,13 +114,9 @@ css = """
 footer {
     visibility: hidden
 }
-.animate-pulse {
-    animation: pulse 2s infinite;
-}
-@keyframes pulse {
-    0% { opacity: 1; }
-    50% { opacity: 0.5; }
-    100% { opacity: 1; }
+.error-message {
+    color: red !important;
+    font-weight: bold !important;
 }
 """
 
@@ -158,12 +169,12 @@ with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
     )
     
     clear_btn.click(
-        fn=lambda: [None, None, None],
+        fn=lambda: [None, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})],
         inputs=None,
-        outputs=[image_input, label_output, confidence_bar],
+        outputs=[image_input, confidence_bar],
         show_progress=False
     )
 
-# For debugging in Hugging Face Spaces
+# Launch the app
 if __name__ == "__main__":
     demo.launch(debug=True)