Update app.py

app.py

@@ -254,51 +254,80 @@ def status():
         })
 
 
-# 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
+# Gradio Blocks setup
+css = """
+#col-container {
+    max-width: 800px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(
+        """
+        # ⚡ Flux.1-dev Upscaler ControlNet ⚡
+        This is an interactive demo of [Flux.1-dev Upscaler ControlNet](https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler) taking as input a low resolution image to generate a high resolution image.
+        Currently running on {device}.
+        *Note*: Even though the model can handle higher resolution images, due to GPU memory constraints, this demo was limited to a generated output not exceeding a pixel budget of 1024x1024. If the requested size exceeds that limit, the input will be first resized keeping the aspect ratio such that the output of the controlNet model does not exceed the allocated pixel budget. The output is then resized to the targeted shape using a simple resizing. This may explain some artifacts for high resolution input. To address this, run the demo locally or consider implementing a tiling strategy. Happy upscaling! 🚀
+        """.format(device=device)
+    )
 
-    # Create a unique process ID for this request
-    process_id = str(random.randint(1000, 9999))
+    with gr.Row():
+        run_button = gr.Button(value="Run")
+
+    with gr.Row():
+        with gr.Column(scale=4):
+            input_im = gr.Image(label="Input Image", type="pil")
+        with gr.Column(scale=1):
+            num_inference_steps = gr.Slider(
+                label="Number of Inference Steps",
+                minimum=8,
+                maximum=50,
+                step=1,
+                value=28,
+            )
+            upscale_factor = gr.Slider(
+                label="Upscale Factor",
+                minimum=1,
+                maximum=4,
+                step=1,
+                value=4,
+            )
+            controlnet_conditioning_scale = gr.Slider(
+                label="Controlnet Conditioning Scale",
+                minimum=0.1,
+                maximum=1.5,
+                step=0.1,
+                value=0.6,
+            )
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
 
-    # Set the status to 'in_progress'
-    app.config['image_outputs'][process_id] = None
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
-    # Run the inference in a separate thread
-    executor.submit(run_inference, process_id, input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale)
+    with gr.Row():
+        result = gr.Image(label="Output Image", type="pil", interactive=False)
 
-    # 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."
+    gr.Button("Run").click(
+        fn=gr_infer,
+        inputs=[seed, randomize_seed, input_im, num_inference_steps, upscale_factor, controlnet_conditioning_scale],
+        outputs=result
     )
-    iface.launch()
 
-# Start Gradio UI in a separate thread
-threading.Thread(target=launch_gradio).start()
+    gr.Markdown("**Disclaimer:**")
+    gr.Markdown(
+        "This demo is only for research purposes. Jasper cannot be held responsible for the generation of NSFW (Not Safe For Work) content through the use of this demo. Users are solely responsible for any content they create, and it is their obligation to ensure that it adheres to appropriate and ethical standards."
+    )
 
 if __name__ == '__main__':
-    app.run(debug=True,host='0.0.0.0')
+    demo.queue().launch(share=False, show_api=False)
+    app.run(host='0.0.0.0', port=5000)
+
+# if __name__ == '__main__':
+#     app.run(debug=True,host='0.0.0.0')