Update app.py

app.py

@@ -775,7 +775,7 @@ import spaces
 import logging
 import random
 import warnings
-import gradio as gr
+#import gradio as gr
 import os
 import shutil
 import subprocess
@@ -924,34 +924,34 @@ async def infer(input_image: UploadFile = File(...),
 
     return JSONResponse(content={"base64_image": base64_image})
 
-def run_gradio_app():
-    with gr.Blocks() as app:
-        gr.Markdown("## Image Upscaler using ControlNet")
+# def run_gradio_app():
+#     with gr.Blocks() as app:
+#         gr.Markdown("## Image Upscaler using ControlNet")
 
-        # Define the inputs and outputs
-        input_image = gr.Image(type="pil", label="Input Image")
-        upscale_factor = gr.Slider(minimum=1, maximum=8, step=1, label="Upscale Factor")
-        seed = gr.Slider(minimum=0, maximum=100, step=1, label="Seed")
-        num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, label="Inference Steps")
-        controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="ControlNet Conditioning Scale")
+#         # Define the inputs and outputs
+#         input_image = gr.Image(type="pil", label="Input Image")
+#         upscale_factor = gr.Slider(minimum=1, maximum=8, step=1, label="Upscale Factor")
+#         seed = gr.Slider(minimum=0, maximum=100, step=1, label="Seed")
+#         num_inference_steps = gr.Slider(minimum=1, maximum=100, step=1, label="Inference Steps")
+#         controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label="ControlNet Conditioning Scale")
 
-        output_base64 = gr.Textbox(label="Base64 String", interactive=False)
+#         output_base64 = gr.Textbox(label="Base64 String", interactive=False)
 
-        # Create a button to trigger the processing
-        submit_button = gr.Button("Upscale Image")
+#         # Create a button to trigger the processing
+#         submit_button = gr.Button("Upscale Image")
 
-        # Define the function to run when the button is clicked
-        submit_button.click(run_inference, 
-                             inputs=[input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale], 
-                             outputs=[output_base64])
+#         # Define the function to run when the button is clicked
+#         submit_button.click(run_inference, 
+#                              inputs=[input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale], 
+#                              outputs=[output_base64])
 
-    app.launch()
+#     app.launch()
 
 if __name__ == "__main__":
-    # Run Gradio app in a separate thread
-    import threading
-    gradio_thread = threading.Thread(target=run_gradio_app)
-    gradio_thread.start()
+#     # Run Gradio app in a separate thread
+#     import threading
+#     gradio_thread = threading.Thread(target=run_gradio_app)
+#     gradio_thread.start()
 
     # Start FastAPI server
     uvicorn.run(app, host="0.0.0.0", port=7860)