Update app.py

app.py

@@ -1,121 +1,121 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-from huggingface_hub import hf_hub_download
-import os
-import pandas as pd
-import logging
-
-# Disable GPU if not available (for Hugging Face Spaces)
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-
-# Setup logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Configuration
-MODEL_REPO = "Ahmedhassan54/Image-Classification"
-MODEL_FILE = "best_model.h5"
-
-# Initialize model
-model = None
-
-def load_model():
-    global model
-    try:
-        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}")
-        
-        # Explicitly disable GPU
-        with tf.device('/CPU:0'):
-            model = tf.keras.models.load_model(model_path)
-        logger.info("Model loaded successfully!")
-    except Exception as e:
-        logger.error(f"Model loading failed: {str(e)}")
-        model = None
-
-# Load model at startup
-load_model()
-
-def classify_image(image):
-    try:
-        if image is None:
-            return {"Cat": 0.5, "Dog": 0.5}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
-            
-        # Convert to PIL Image if numpy array
-        if isinstance(image, np.ndarray):
-            image = Image.fromarray(image.astype('uint8'))
-            
-        # 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)
-            
-        # Predict
-        if model is not None:
-            with tf.device('/CPU:0'):
-                pred = model.predict(img_array, verbose=0)
-            confidence = float(pred[0][0])
-        else:
-            confidence = 0.75  # Demo value
-            
-        results = {
-            "Cat": round(1 - confidence, 4),
-            "Dog": round(confidence, 4)
-        }
-        
-        plot_data = pd.DataFrame({
-            'Class': ['Cat', 'Dog'],
-            'Confidence': [1 - confidence, confidence]
-        })
-        
-        return results, plot_data
-        
-    except Exception as e:
-        logger.error(f"Error: {str(e)}")
-        return {"Error": str(e)}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
-
-# Interface
-with gr.Blocks() as demo:
-    gr.Markdown("# 🐾 Cat vs Dog Classifier 🦮")
-    
-    with gr.Row():
-        with gr.Column():
-            img_input = gr.Image(type="pil")
-            classify_btn = gr.Button("Classify", variant="primary")
-        
-        with gr.Column():
-            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]
-            )
-    
-    classify_btn.click(
-        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=img_input,
-        outputs=[label_out, plot_out],
-        fn=classify_image,
-        cache_examples=True
-    )
-
-if __name__ == "__main__":
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import os
+import pandas as pd
+import logging
+
+# Disable GPU if not available (for Hugging Face Spaces)
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Configuration
+MODEL_REPO = "Ahmedhassan54/Image-classifier"
+MODEL_FILE = "final_model.h5"
+
+# Initialize model
+model = None
+
+def load_model():
+    global model
+    try:
+        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}")
+        
+        # Explicitly disable GPU
+        with tf.device('/CPU:0'):
+            model = tf.keras.models.load_model(model_path)
+        logger.info("Model loaded successfully!")
+    except Exception as e:
+        logger.error(f"Model loading failed: {str(e)}")
+        model = None
+
+# Load model at startup
+load_model()
+
+def classify_image(image):
+    try:
+        if image is None:
+            return {"Cat": 0.5, "Dog": 0.5}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
+            
+        # Convert to PIL Image if numpy array
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image.astype('uint8'))
+            
+        # 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)
+            
+        # Predict
+        if model is not None:
+            with tf.device('/CPU:0'):
+                pred = model.predict(img_array, verbose=0)
+            confidence = float(pred[0][0])
+        else:
+            confidence = 0.75  # Demo value
+            
+        results = {
+            "Cat": round(1 - confidence, 4),
+            "Dog": round(confidence, 4)
+        }
+        
+        plot_data = pd.DataFrame({
+            'Class': ['Cat', 'Dog'],
+            'Confidence': [1 - confidence, confidence]
+        })
+        
+        return results, plot_data
+        
+    except Exception as e:
+        logger.error(f"Error: {str(e)}")
+        return {"Error": str(e)}, pd.DataFrame({'Class': ['Cat', 'Dog'], 'Confidence': [0.5, 0.5]})
+
+# Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🐾 Cat vs Dog Classifier 🦮")
+    
+    with gr.Row():
+        with gr.Column():
+            img_input = gr.Image(type="pil")
+            classify_btn = gr.Button("Classify", variant="primary")
+        
+        with gr.Column():
+            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]
+            )
+    
+    classify_btn.click(
+        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=img_input,
+        outputs=[label_out, plot_out],
+        fn=classify_image,
+        cache_examples=True
+    )
+
+if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)