Delete main.py; filename=_main.py_;.py

main.py; filename=_main.py_;.py

@@ -1,52 +0,0 @@
-from fastapi import FastAPI, File, UploadFile, Form
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
-from gradio_client import Client, handle_file
-import os
-import tempfile
-import base64
-
-app = FastAPI()
-
-# Setup CORS
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Adjust as needed
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-# Initialize the Gradio client with the token
-client = Client("Makhinur/Image_Face_Upscale_Restoration-GFPGAN", hf_token=HF_TOKEN)
-
-@app.post("/upload/")
-async def upload_file(file: UploadFile = File(...), version: str = Form(...), scale: int = Form(...)):
-    # Save the uploaded file temporarily
-    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
-        temp_file.write(await file.read())
-        temp_file_path = temp_file.name
-
-    try:
-        # Use handle_file to prepare the file for the Gradio client
-        result = client.predict(handle_file(temp_file_path), version, scale, api_name="/predict")
-
-        # Check if the result is valid
-        if result and len(result) == 2:
-            # Convert the image data to a base64 string
-            with open(result[0], "rb") as image_file:
-                image_data = base64.b64encode(image_file.read()).decode("utf-8")
-
-            return JSONResponse({
-                "sketch_image_base64": f"data:image/png;base64,{image_data}",
-                "result_file": result[1]
-            })
-        else:
-            return JSONResponse({"error": "Invalid result from the prediction API."}, status_code=500)
-    except Exception as e:
-        return JSONResponse({"error": str(e)}, status_code=500)
-    finally:
-        # Clean up the temporary file
-        if os.path.exists(temp_file_path):
-            os.unlink(temp_file_path)