Update app.py

app.py

@@ -251,6 +251,51 @@ def status():
             "output_image": image_base64
         })
 
+
+# Gradio interface function
+def gradio_infer(input_image, upscale_factor, num_inference_steps, controlnet_conditioning_scale, randomize_seed):
+    # Convert the image to Base64 for internal processing
+    buffered = io.BytesIO()
+    input_image.save(buffered, format="JPEG")
+
+    # Prepare parameters
+    seed = random.randint(0, MAX_SEED) if randomize_seed else 42
+
+    # Create a unique process ID for this request
+    process_id = str(random.randint(1000, 9999))
+
+    # Set the status to 'in_progress'
+    app.config['image_outputs'][process_id] = None
+
+    # Run the inference in a separate thread
+    executor.submit(run_inference, process_id, input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale)
+
+    # Wait for the processing to complete and return the output image
+    while app.config['image_outputs'][process_id] is None:
+        pass  # Polling until the process is complete
+
+    return app.config['image_outputs'][process_id]
+
+# Gradio UI setup
+def launch_gradio():
+    iface = gr.Interface(
+        fn=gradio_infer,
+        inputs=[
+            gr.inputs.Image(type="pil", label="Input Image"),
+            gr.inputs.Slider(1, 8, step=1, default=4, label="Upscale Factor"),
+            gr.inputs.Slider(1, 100, step=1, default=28, label="Number of Inference Steps"),
+            gr.inputs.Slider(0.0, 2.0, step=0.1, default=0.6, label="ControlNet Conditioning Scale"),
+            gr.inputs.Checkbox(default=True, label="Randomize Seed")
+        ],
+        outputs="image",
+        title="Image Upscaling with ControlNet",
+        description="Upload an image to upscale it using ControlNet."
+    )
+    iface.launch()
+
+# Start Gradio UI in a separate thread
+threading.Thread(target=launch_gradio).start()
+
 if __name__ == '__main__':
     app.run(debug=True,host='0.0.0.0')