Update app.py

app.py

@@ -775,7 +775,6 @@ import spaces
 import logging
 import random
 import warnings
-#import gradio as gr
 import os
 import shutil
 import subprocess
@@ -792,6 +791,7 @@ from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
 from concurrent.futures import ThreadPoolExecutor
 import uvicorn
+import asyncio
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -919,40 +919,15 @@ async def infer(input_image: UploadFile = File(...),
     contents = await input_image.read()
     image = Image.open(io.BytesIO(contents))
 
+    # Get the current event loop
+    loop = asyncio.get_event_loop()
+
     # Run inference in a separate thread
-    base64_image = await executor.submit(run_inference, image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale)
+    base64_image = await loop.run_in_executor(executor, run_inference, image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale)
 
     return JSONResponse(content={"base64_image": base64_image})
 
-# 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")
-
-#         output_base64 = gr.Textbox(label="Base64 String", interactive=False)
-
-#         # 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])
-
-#     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()
-
     # Start FastAPI server
     uvicorn.run(app, host="0.0.0.0", port=7860)