Update app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from tensorflow.keras.preprocessing import image
+
+# Load the pre-trained MobileNetV2 model for feature extraction
+model = MobileNetV2(weights='imagenet', include_top=False, pooling='avg')
+
+def preprocess_image(img):
+    """Preprocess the image to fit MobileNetV2 requirements"""
+    img = img.resize((224, 224))  # Resize the image to 224x224
+    img_array = image.img_to_array(img)  # Convert to numpy array
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    return preprocess_input(img_array)  # Preprocess the image
+
+def compare_images(image1, image2):
+    """Compare two images using cosine similarity of their features"""
+    # Extract features for both images
+    features1 = model.predict(preprocess_image(image1))
+    features2 = model.predict(preprocess_image(image2))
+
+    # Compute cosine similarity between features
+    similarity = np.dot(features1, features2.T)
+    similarity = similarity / (np.linalg.norm(features1) * np.linalg.norm(features2))
+    return {'Similarity': float(similarity)}
+
+# Setup Gradio interface
+iface = gr.Interface(
+    fn=compare_images,
+    inputs=[gr.inputs.Image(shape=None), gr.inputs.Image(shape=None)],
+    outputs=[gr.outputs.Label(num_top_classes=1)],
+    title="Image Similarity Checker",
+    description="Upload two images to compare their similarity based on extracted features using MobileNetV2."
+)
+
+if __name__ == "__main__":
+    iface.launch()