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from fastapi import FastAPI, File, UploadFile, Form
from fastapi.responses import JSONResponse
from gradio_client import Client, handle_file
import shutil
import base64
import os
app = FastAPI()
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)
# Version mapping from HTML to Gradio API
version_map = {
"M1": "v1.2",
"M2": "v1.3",
"M3": "v1.4"
}
@app.post("/upload/")
async def enhance_image(
file: UploadFile = File(...),
version: str = Form(...),
scale: int = Form(...)
):
# Map version from HTML to Gradio expected value
gradio_version = version_map.get(version, "v1.4")
# Save the uploaded image to a temporary file
temp_file_path = "temp_image.png"
with open(temp_file_path, "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
try:
# Use the Gradio client to process the image
result = client.predict(
img=handle_file(temp_file_path),
version=gradio_version,
scale=scale,
api_name="/predict"
)
# Read the result image and encode it in base64
with open(result[0], "rb") as img_file:
b64_string = base64.b64encode(img_file.read()).decode('utf-8')
# Clean up the temporary file
os.remove(temp_file_path)
return JSONResponse(content={"sketch_image_base64": f"data:image/png;base64,{b64_string}"})
except Exception as e:
# Log the error message for debugging
print(f"Error processing image: {e}")
return JSONResponse(status_code=500, content={"message": "Internal Server Error"})
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