Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow import keras
+import numpy as np
+from PIL import Image
+
+class_names = ['drive', 'legglance_flick', 'pullshot', 'sweep']
+
+
+# Load models
+def load_models():
+    try:
+        vgg16_model = keras.models.load_model('vgg16_finetuned.pth')
+        custom_cnn_model = keras.models.load_model('cricket_model.pth')
+        return vgg16_model, custom_cnn_model
+    except Exception as e:
+        print(f"Error loading models: {e}")
+        return None, None
+
+vgg16_model, custom_cnn_model = load_models()
+
+def preprocess_image(image, target_size=(224, 224)):
+    """Preprocess image for model prediction"""
+    if image is None:
+        return None
+    
+    # Convert to PIL Image if needed
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+    
+    # Resize image
+    image = image.resize(target_size)
+    
+    # Convert to array and normalize
+    img_array = np.array(image)
+    
+    # Handle grayscale images
+    if len(img_array.shape) == 2:
+        img_array = np.stack([img_array] * 3, axis=-1)
+    
+    # Add batch dimension
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    # Normalize to [0, 1]
+    img_array = img_array.astype('float32') / 255.0
+    
+    return img_array
+
+def predict(image):
+    """Make predictions with both models"""
+    if image is None:
+        return None, None
+    
+    if vgg16_model is None or custom_cnn_model is None:
+        return "Models not loaded properly", "Models not loaded properly"
+    
+    # Preprocess image
+    processed_img = preprocess_image(image)
+    
+    # Get predictions from both models
+    vgg16_pred = vgg16_model.predict(processed_img, verbose=0)[0]
+    custom_cnn_pred = custom_cnn_model.predict(processed_img, verbose=0)[0]
+    
+    # Create confidence dictionaries
+    vgg16_confidence = {CLASS_NAMES[i]: float(vgg16_pred[i]) for i in range(len(CLASS_NAMES))}
+    custom_cnn_confidence = {CLASS_NAMES[i]: float(custom_cnn_pred[i]) for i in range(len(CLASS_NAMES))}
+    
+    return vgg16_confidence, custom_cnn_confidence
+
+# Create Gradio interface
+with gr.Blocks(title="Dual Model Comparison") as demo:
+    gr.Markdown(
+        """
+        # 🔍 Dual Model Image Classification
+        
+        Compare predictions from two models trained on the same dataset:
+        - **VGG16 Fine-tuned**: Transfer learning model based on VGG16
+        - **Custom CNN**: CNN trained from scratch
+        
+        Upload an image to see predictions and confidence scores from both models.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Upload Image", type="numpy")
+            predict_btn = gr.Button("Predict", variant="primary")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### VGG16 Fine-tuned Model")
+            vgg16_output = gr.Label(label="Predictions", num_top_classes=4)
+        
+        with gr.Column():
+            gr.Markdown("### Custom CNN Model")
+            custom_cnn_output = gr.Label(label="Predictions", num_top_classes=4)
+    
+    # Examples section (optional - add your example images)
+    gr.Markdown("### Examples")
+    gr.Examples(
+        examples=[
+            # Add paths to example images here
+            # ["example1.jpg"],
+            # ["example2.jpg"],
+        ],
+        inputs=input_image,
+    )
+    
+    # Connect the prediction function
+    predict_btn.click(
+        fn=predict,
+        inputs=input_image,
+        outputs=[vgg16_output, custom_cnn_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()