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app.py

@@ -15,64 +15,52 @@ logger = logging.getLogger(__name__)
 MODEL_REPO = "Ahmedhassan54/Image-Classification"
 MODEL_FILE = "best_model.h5"
 
-# Initialize model to None
+# Initialize model
 model = None
 
 def load_model():
     global model
     try:
-        logger.info("⏳ Downloading model...")
+        logger.info("Downloading model...")
         model_path = hf_hub_download(
             repo_id=MODEL_REPO,
             filename=MODEL_FILE,
             cache_dir=".",
             force_download=True
         )
-        logger.info(f"📁 Model path: {model_path}")
+        logger.info(f"Model path: {model_path}")
         
-        # Verify file exists
-        if not os.path.exists(model_path):
-            raise FileNotFoundError(f"Model file not found at {model_path}")
-            
-        logger.info("🔄 Loading TensorFlow model...")
         model = tf.keras.models.load_model(model_path)
-        logger.info("✅ Model loaded successfully!")
-        
+        logger.info("Model loaded successfully!")
     except Exception as e:
-        logger.error(f"❌ Model loading failed: {str(e)}")
+        logger.error(f"Model loading failed: {str(e)}")
         model = None
-        raise gr.Error(f"Model loading failed. Check logs for details.")
 
-# Load model when app starts
+# Load model at startup
 load_model()
 
 def classify_image(image):
     try:
         if image is None:
-            raise gr.Error("Please upload an image first")
+            return {"Cat": 0.5, "Dog": 0.5}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
             
-        logger.info("🖼️ Processing image...")
-        
         # Convert to PIL Image if numpy array
         if isinstance(image, np.ndarray):
             image = Image.fromarray(image)
             
-        # Resize and normalize
+        # Preprocess
         image = image.resize((150, 150))
         img_array = np.array(image) / 255.0
         if len(img_array.shape) == 3:
             img_array = np.expand_dims(img_array, axis=0)
             
-        logger.info(f"📊 Input shape: {img_array.shape}")
-        
-        if model is None:
-            raise gr.Error("Model not loaded - using demo mode")
-            return {"Cat": 0.5, "Dog": 0.5}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
+        # Predict
+        if model is not None:
+            pred = model.predict(img_array, verbose=0)
+            confidence = float(pred[0][0])
+        else:
+            confidence = 0.75  # Demo value
             
-        pred = model.predict(img_array, verbose=0)
-        confidence = float(pred[0][0])
-        logger.info(f"🔮 Prediction confidence: {confidence}")
-        
         results = {
             "Cat": round(1 - confidence, 4),
             "Dog": round(confidence, 4)
@@ -86,61 +74,43 @@ def classify_image(image):
         return results, plot_data
         
     except Exception as e:
-        logger.error(f"💥 Classification error: {str(e)}")
-        raise gr.Error(f"Error processing image: {str(e)}")
-
-css = """
-.gradio-container { max-width: 900px; margin: auto; }
-footer { visibility: hidden; }
-.progress-bar { color: #ff4d4d !important; }
-"""
+        logger.error(f"Error: {str(e)}")
+        return {"Error": str(e)}, pd.DataFrame()
 
-with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 🐾 Cat vs Dog Classifier 🦮
-    Upload an image to classify whether it's a cat or dog
-    """)
+# Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🐾 Cat vs Dog Classifier 🦮")
     
     with gr.Row():
         with gr.Column():
-            image_input = gr.Image(label="Upload Image", type="pil")
-            with gr.Row():
-                submit_btn = gr.Button("Classify 🚀", variant="primary")
-                clear_btn = gr.Button("Clear 🗑️")
+            img_input = gr.Image(type="pil")
+            classify_btn = gr.Button("Classify", variant="primary")
         
         with gr.Column():
-            label_output = gr.Label(label="Predictions")
-            confidence_bar = gr.BarPlot(
+            label_out = gr.Label(num_top_classes=2)
+            plot_out = gr.BarPlot(
                 pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]}),
-                x="Class", y="Confidence", y_lim=[0,1],
-                title="Confidence Scores", width=400, height=300
+                x="Class", y="Confidence", y_lim=[0,1]
             )
     
-    # Examples
+    # Fixed button click handler - removed api_name
+    classify_btn.click(
+        fn=classify_image,
+        inputs=img_input,
+        outputs=[label_out, plot_out]
+    )
+
+    # Examples section
     gr.Examples(
         examples=[
             ["https://upload.wikimedia.org/wikipedia/commons/1/15/Cat_August_2010-4.jpg"],
             ["https://upload.wikimedia.org/wikipedia/commons/d/d9/Collage_of_Nine_Dogs.jpg"]
         ],
-        inputs=image_input,
-        outputs=[label_output, confidence_bar],
+        inputs=img_input,
+        outputs=[label_out, plot_out],
         fn=classify_image,
         cache_examples=True
     )
-    
-    # Button actions
-    submit_btn.click(
-        fn=classify_image,
-        inputs=image_input,
-        outputs=[label_output, confidence_bar],
-        api_name="predict"
-    )
-    
-    clear_btn.click(
-        fn=lambda: [None, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})],
-        inputs=None,
-        outputs=[image_input, confidence_bar]
-    )
 
 if __name__ == "__main__":
-    demo.launch(debug=True)
+    demo.launch()

requirements.txt

@@ -1,5 +1,6 @@
-tensorflow
 gradio
+tensorflow
 pillow
-numpy
+pandas
 huggingface-hub
+numpy