Upload app.py

app.py

@@ -7,41 +7,68 @@ import os
 import pandas as pd
 
 # Configuration
-MODEL_REPO = "Ahmedhassan54/Image-Classification"  # Changed to your actual repo
+MODEL_REPO = "Ahmedhassan54/Image-Classification"
 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("Downloading model from Hugging Face Hub...")
+            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")
         
-        return tf.keras.models.load_model(MODEL_FILE)
+        print("Loading model...")
+        model = tf.keras.models.load_model(MODEL_FILE)
+        print("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)}")
 
-model = load_model_from_hf()
+# Load model when the app starts
+try:
+    model = load_model_from_hf()
+except:
+    model = None
+    print("Proceeding without model - for debugging purposes")
 
 def classify_image(image):
     try:
+        print("\nClassification started...")
+        
+        # Debug: Check input type
+        print(f"Input type: {type(image)}")
+        
         # Convert image if needed
         if isinstance(image, np.ndarray):
+            print("Converting numpy array to PIL Image")
             image = Image.fromarray(image)
         
         # Preprocess image
+        print("Preprocessing image...")
         image = image.resize((150, 150))
         image_array = np.array(image) / 255.0
         image_array = np.expand_dims(image_array, axis=0)
         
         # Make prediction
-        prediction = model.predict(image_array, verbose=0)
-        confidence = float(prediction[0][0])
+        print("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])
+        
+        print(f"Raw confidence: {confidence}")
         
         # Format outputs
         label_output = {
@@ -55,10 +82,13 @@ def classify_image(image):
             'Confidence': [1 - confidence, confidence]
         })
         
+        print("Classification successful!")
+        print(f"Results: {label_output}")
+        
         return label_output, plot_data
         
     except Exception as e:
-        print(f"Error: {str(e)}")  # Debug print
+        print(f"Error during classification: {str(e)}")
         raise gr.Error(f"Classification error: {str(e)}")
 
 # Custom CSS
@@ -69,17 +99,29 @@ css = """
 footer {
     visibility: hidden
 }
+.animate-pulse {
+    animation: pulse 2s infinite;
+}
+@keyframes pulse {
+    0% { opacity: 1; }
+    50% { opacity: 0.5; }
+    100% { opacity: 1; }
+}
 """
 
 # Build the interface
 with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🐾 Cat vs Dog Classifier 🦮")
-    gr.Markdown("Upload an image to classify whether it's a cat or dog")
+    gr.Markdown("""
+    # 🐾 Cat vs Dog Classifier 🦮
+    Upload an image to classify whether it's a cat or dog
+    """)
     
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(label="Upload Image", type="pil")
-            submit_btn = gr.Button("Classify", variant="primary")
+            with gr.Row():
+                submit_btn = gr.Button("Classify", variant="primary")
+                clear_btn = gr.Button("Clear")
         
         with gr.Column():
             label_output = gr.Label(label="Predictions", num_top_classes=2)
@@ -103,16 +145,25 @@ with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
         inputs=image_input,
         outputs=[label_output, confidence_bar],
         fn=classify_image,
-        cache_examples=True
+        cache_examples=True,
+        label="Try these examples:"
     )
     
-    # Button action
+    # Button actions
     submit_btn.click(
         fn=classify_image,
         inputs=image_input,
         outputs=[label_output, confidence_bar],
         api_name="classify"
     )
+    
+    clear_btn.click(
+        fn=lambda: [None, None, None],
+        inputs=None,
+        outputs=[image_input, label_output, confidence_bar],
+        show_progress=False
+    )
 
+# For debugging in Hugging Face Spaces
 if __name__ == "__main__":
     demo.launch(debug=True)