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

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

@@ -0,0 +1,52 @@
+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)