Update app.py

app.py

@@ -23,45 +23,55 @@ except Exception as e:
 
 # --- Prediction Function ---
 def predict_image(input_img_pil):
-    """
-    Predicts the class (Cat or Dog) given a PIL Image object.
     
-    Args:
-        input_img_pil: A PIL Image object received from Gradio's Image input.
-        
-    Returns:
-        A dictionary of class labels and their probabilities (for Gradio's Label output).
-    """
-    if model is None:
-        # Placeholder behavior if model loading failed
-        return {"Error ********": 1.0}
 
-    # 1. Preprocessing: Resize and convert to NumPy array
-    img_resized = input_img_pil.resize(IMAGE_SIZE)
-    print("image resized")
-    img_array = keras.preprocessing.image.img_to_array(img_resized)
-    print(" image converted to array")
+     # WRAP ENTIRE LOGIC IN TRY/EXCEPT FOR MAXIMUM ERROR CAPTURE
+    try:
+        # 0. Crucial check: ensure an image was actually uploaded
+        if input_img_pil is None:
+            # Return a simple dictionary indicating missing input
+            return {"Please upload an image first.": 1.0}
+
+        if model is None:
+            # Model loading failed during initialization
+            return {"MODEL NOT FOUND": 1.0, "Please check if cat-vs-dog.keras exists.": 0.0}
+
+        # 1. Preprocessing: Resize and convert to NumPy array
+        print(f"Original image size: {input_img_pil.size}")
+        img_resized = input_img_pil.resize(IMAGE_SIZE)
+        img_array = keras.preprocessing.image.img_to_array(img_resized)
         
-    # 2. Rescaling and Batch dimension:
-    # Keras models usually expect input shapes like (Batch_Size, Height, Width, Channels)
-    # and often expect pixel values to be normalized (e.g., 0-1 range).
-    # Please adjust the normalization based on how your model was trained!
-    img_array = img_array / 255.0  # Common normalization step
-    img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
+        # 2. Rescaling and Batch dimension:
+        img_array = img_array / 255.0  # Common normalization step
+        img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
 
-    # 3. Prediction
-    predictions = model.predict(img_array)[0] # Get the single prediction result
+        # 3. Prediction
+        print(f"Array shape for model input: {img_array.shape}")
+        predictions = model.predict(img_array)[0] # Get the single prediction result
+        print(f"Raw model predictions: {predictions}")
 
-    # 4. Format the output for Gradio's Label component
-    # The output is expected to be a dictionary: {'label': probability, ...}
-    
-    # Assuming predictions is a 2-element array: [prob_cat, prob_dog]
-    output_dict = {
-        CLASS_LABELS[0]: float(predictions[0]),
-        CLASS_LABELS[1]: float(predictions[1])
-    }
+        # 4. Format the output for Gradio's Label component
+        # Assuming predictions is a 2-element array: [prob_cat, prob_dog]
+        output_dict = {
+            CLASS_LABELS[0]: float(predictions[0]),
+            CLASS_LABELS[1]: float(predictions[1])
+        }
+        
+        return output_dict
     
-    return output_dict
+    except Exception as e:
+        # Catch any error, log it, and return it to the user in a visible format
+        error_message = f"CRITICAL RUNTIME ERROR: {str(e)}"
+        detailed_trace = traceback.format_exc()
+        
+        print("\n--- DETAILED RUNTIME ERROR LOG ---")
+        print(error_message)
+        print(detailed_trace)
+        print("------------------------------------\n")
+        
+        # This format should force Gradio to display the specific error message
+        return {f"💥 {error_message}": 1.0}
+
 
 
 # --- Gradio Interface Setup ---