Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import cv2
+import tensorflow as tf
+from PIL import Image
+
+def preprocess_image(image):
+    # Convert to numpy array
+    image = np.array(image)
+    
+    # Paint a black rectangle on the image
+    h, w, _ = image.shape
+    x1, y1, x2, y2 = w//4, h//4, 3*w//4, 3*h//4  # Example position
+    image[y1:y2, x1:x2] = (0, 0, 0)
+    
+    # Resize to 256x256
+    image = cv2.resize(image, (256, 256))
+    
+    # Normalize to 0-1
+    image = image / 255.0
+    
+    return image
+
+def predict(image):
+    # Load model
+    model = tf.keras.models.load_model("initial_generator_model.h5")
+    
+    # Preprocess image
+    processed_image = preprocess_image(image)
+    
+    # Expand dimensions to match model input shape
+    input_image = np.expand_dims(processed_image, axis=0)
+    
+    # Run model prediction
+    output_image = model.predict(input_image)[0]
+    
+    # Convert output back to image format (denormalize)
+    output_image = (output_image * 255).astype(np.uint8)
+    
+    return Image.fromarray(output_image)
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Image Inpainting Model",
+    description="Upload an image. A black rectangle will be drawn on it, and the model will attempt to fill it in."
+)
+
+demo.launch()