Create app.py

app.py

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+import cv2
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+
+# Load the trained model
+model = tf.keras.models.load_model('unet_model.h5')
+
+# Function to preprocess the images
+def preprocess_images(old_image, current_image, img_size=128):
+    old_image_resized = cv2.resize(old_image, (img_size, img_size)) / 255.0
+    current_image_resized = cv2.resize(current_image, (img_size, img_size)) / 255.0
+    input_combined = np.concatenate([old_image_resized, current_image_resized], axis=-1)
+    input_combined = np.expand_dims(input_combined, axis=0)
+    return input_combined
+
+# Prediction function
+def predict_mask(old_image, current_image):
+    # Convert from PIL to numpy array
+    old_image_np = np.array(old_image)
+    current_image_np = np.array(current_image)
+
+    # Preprocess images
+    preprocessed_input = preprocess_images(old_image_np, current_image_np)
+
+    # Predict the mask
+    prediction = model.predict(preprocessed_input)
+    prediction_mask = (prediction[0] > 0.5).astype(np.uint8) * 255  # Binary mask
+
+    # Resize mask back to original size
+    mask_resized = cv2.resize(prediction_mask, (old_image_np.shape[1], old_image_np.shape[0]))
+
+    return mask_resized
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_mask,
+    inputs=[
+        gr.Image(label="Old Image"),
+        gr.Image(label="Current Image")
+    ],
+    outputs=gr.Image(label="Predicted Mask"),
+    title="Change Detection with U-Net",
+    description="Upload two images (old and current) to detect changes using a U-Net model."
+)
+
+if __name__ == "__main__":
+    interface.launch()