File size: 1,867 Bytes
ee3e372 e482b3f ee3e372 e482b3f ee3e372 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 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=["*"],
)
# Initialize Gradio client
hf_token = os.environ.get('HF_TOKEN')
client = Client("https://Makhinur/Image_Face_Upscale_Restoration-GFPGAN.hf.space/", 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) |