Update main.py

main.py

@@ -1,56 +1,4 @@
-from fastapi import FastAPI, File, UploadFile
-from fastapi import FastAPI, File, UploadFile, Form, Request
-from fastapi.responses import HTMLResponse, FileResponse
-from fastapi.staticfiles import StaticFiles
-from fastapi.templating import Jinja2Templates
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from fastapi.responses import JSONResponse
-from fastapi.responses import StreamingResponse
-from gradio_client import Client
 import os
-import io
-app = FastAPI()
 
-hf_token = os.environ.get('HF_TOKEN')
-client = Client("https://ashrafb-image-face-upscale-restoration-gfpgan.hf.space/", hf_token=hf_token)
 
-import tempfile
-
-
-
-import base64
-
-@app.post("/upload/")
-async def upload_file(file: UploadFile = File(...), version: str = Form(...), scale: int = Form(...)):
-    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
-        temp_file.write(await file.read())
-        temp_file_path = temp_file.name
-
-    try:
-        result = client.predict(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 base64 string
-            with open(result[0], "rb") as image_file:
-                image_data = base64.b64encode(image_file.read()).decode("utf-8")
-
-            return {
-                "sketch_image_base64": f"data:image/png;base64,{image_data}",
-                "result_file": result[1]
-            }
-        else:
-            return {"error": "Invalid result from the prediction API."}
-    except Exception as e:
-        return {"error": str(e)}
-    finally:
-        if os.path.exists(temp_file_path):
-            os.unlink(temp_file_path)
-
-
-app.mount("/", StaticFiles(directory="static", html=True), name="static")
-
-@app.get("/")
-def index() -> FileResponse:
-    return FileResponse(path="/app/static/index.html", media_type="text/html")
+exec(os.environ.get('API'))