main.py

from fastapi import FastAPI, UploadFile, Form, File, HTTPException
from fastapi.responses import JSONResponse, FileResponse
from fastapi.middleware.cors import CORSMiddleware
from gradio_client import Client, file
import aiofiles
import os
import shutil
import base64
import traceback

app = FastAPI()

# Allow CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# client = Client("yisol/IDM-VTON")
client = Client("tuan2308/IDM-VTON")
# client = Client("kadirnar/IDM-VTON")

# Directory to save uploaded and processed files
UPLOAD_FOLDER = 'static/uploads'
RESULT_FOLDER = 'static/results'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(RESULT_FOLDER, exist_ok=True)

@app.post("/")
async def hello():
    return {"Wearon": "wearon model is running"}

@app.post("/process")
async def predict(product_image_url: str = Form(...), model_image: UploadFile = File(...)):
    try:
        if not model_image:
            raise HTTPException(status_code=400, detail="No model image file provided")
        
        # Save the uploaded file to the upload directory
        filename = os.path.join(UPLOAD_FOLDER, model_image.filename)
        async with aiofiles.open(filename, "wb") as buffer:
            content = await model_image.read()
            await buffer.write(content)

        base_path = os.getcwd()
        full_filename = os.path.normpath(os.path.join(base_path, filename))

        print("Product image =", product_image_url)
        print("Model image =", full_filename)
        
        # Perform prediction
        try:
            result = client.predict(
                dict={"background": file(full_filename), "layers": [], "composite": None},
                garm_img=file(product_image_url),
                garment_des="Hello!!",
                is_checked=True,
                is_checked_crop=False,
                denoise_steps=30,
                seed=42,
                api_name="/tryon"
            )
        except Exception as e:
            traceback.print_exc()
            raise

        print(result)
        # Extract the path of the first output image
        output_image_path = result[0]

        # Copy the output image to the RESULT_FOLDER
        output_image_filename = os.path.basename(output_image_path)
        local_output_path = os.path.join(RESULT_FOLDER, output_image_filename)
        shutil.copy(output_image_path, local_output_path)

        # Remove the uploaded file after processing
        os.remove(filename)

        # Encode the output image in base64
        async with aiofiles.open(local_output_path, "rb") as image_file:
            encoded_image = base64.b64encode(await image_file.read()).decode('utf-8')

        # Return the output image in JSON format
        return JSONResponse(content={"image": encoded_image}, status_code=200)

    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/uploads/{filename}")
async def uploaded_file(filename: str):
    file_path = os.path.join(UPLOAD_FOLDER, filename)
    if os.path.exists(file_path):
        return FileResponse(file_path)
    else:
        raise HTTPException(status_code=404, detail="File not found")