app.py

# -*- coding:UTF-8 -*-
from fastapi import FastAPI, UploadFile, File, HTTPException
from fastapi.responses import Response
from fastapi.middleware.cors import CORSMiddleware
import cv2
import numpy as np
from PIL import Image
import os
import shutil
import logging
import requests
from pathlib import Path
import uvicorn

app = FastAPI()

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Update with Framer domain in production
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global flag to prevent multiple downloads
MODEL_DOWNLOADED = False

def download_model():
    global MODEL_DOWNLOADED
    if MODEL_DOWNLOADED:
        logger.info("Model already downloaded, skipping.")
        return

    model_dir = Path("models")
    model_path = model_dir / "inswapper_128.onnx"
    model_url = "https://huggingface.co/ezioruan/inswapper_128.onnx/resolve/main/inswapper_128.onnx"

    if not model_path.exists():
        logger.info("Model not found. Downloading inswapper_128.onnx...")
        model_dir.mkdir(exist_ok=True)
        try:
            response = requests.get(model_url, stream=True, timeout=30)
            response.raise_for_status()
            with open(model_path, 'wb') as f:
                for chunk in response.iter_content(chunk_size=8192):
                    f.write(chunk)
            logger.info("Model downloaded successfully.")
            MODEL_DOWNLOADED = True
        except Exception as e:
            logger.error(f"Failed to download model: {e}")
            raise RuntimeError("Could not download inswapper_128.onnx. Please check logs.")
    else:
        logger.info("Model already exists at: %s", model_path)
        MODEL_DOWNLOADED = True

@app.on_event("startup")
async def startup_event():
    """Run startup tasks like downloading the model."""
    logger.info("Starting up application...")
    try:
        download_model()
        logger.info("Startup completed successfully.")
    except Exception as e:
        logger.error(f"Startup failed: {e}")
        raise

def get_many_faces(image):
    """Simplified face detection using insightface."""
    try:
        from insightface.app import FaceAnalysis
        app = FaceAnalysis(name="buffalo_l")
        app.prepare(ctx_id=0, det_size=(640, 640))
        faces = app.get(image)
        return faces if faces else []
    except Exception as e:
        logger.error(f"Face detection failed: {e}")
        raise

def swap_faces(source_img, target_img):
    """Perform face swapping using insightface and inswapper model."""
    try:
        from insightface.utils import face_align
        from insightface.model_zoo import face_swapper

        # Initialize face analysis
        face_analyzer = FaceAnalysis(name="buffalo_l")
        face_analyzer.prepare(ctx_id=0, det_size=(640, 640))

        # Detect faces
        source_faces = face_analyzer.get(source_img)
        target_faces = face_analyzer.get(target_img)

        if not source_faces or not target_faces:
            raise ValueError("No faces detected in one or both images.")
        if len(source_faces) > 1 or len(target_faces) > 1:
            raise ValueError("Multiple faces detected; only one face per image is supported.")

        source_face = source_faces[0]
        target_face = target_faces[0]

        # Load the face swapper model
        model_path = Path("models/inswapper_128.onnx")
        if not model_path.exists():
            raise FileNotFoundError("Model file inswapper_128.onnx not found.")
        swapper = face_swapper.FaceSwapper(model_path)

        # Perform face swap
        result = swapper.get(target_img, target_face, source_face, paste_back=True)

        # Resize to match target image size
        target_pil = Image.fromarray(cv2.cvtColor(target_img, cv2.COLOR_BGR2RGB))
        result_pil = Image.fromarray(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
        result_pil = result_pil.resize(target_pil.size, Image.Resampling.LANCZOS)

        return cv2.cvtColor(np.array(result_pil), cv2.COLOR_RGB2BGR)
    except Exception as e:
        logger.error(f"Face swap failed: {e}")
        raise

@app.post("/swap-face/")
async def swap_face(source_file: UploadFile = File(...), target_file: UploadFile = File(...), doFaceEnhancer: bool = True):
    try:
        # Save uploaded files temporarily
        source_path = "temp_source.jpg"
        target_path = "temp_target.jpg"
        output_path = "output.jpg"

        # Read and save source image
        source_content = await source_file.read()
        with open(source_path, "wb") as f:
            f.write(source_content)
        source_img = cv2.imread(source_path)
        if source_img is None:
            raise ValueError("Failed to load source image.")

        # Read and save target image
        target_content = await target_file.read()
        with open(target_path, "wb") as f:
            f.write(target_content)
        target_img = cv2.imread(target_path)
        if target_img is None:
            raise ValueError("Failed to load target image.")

        # Perform face swap
        result_img = swap_faces(source_img, target_img)

        # Save the result
        cv2.imwrite(output_path, result_img)

        # Read the output image
        with open(output_path, "rb") as f:
            image_data = f.read()

        # Clean up temporary files
        for path in [source_path, target_path, output_path]:
            if os.path.exists(path):
                os.remove(path)

        # Return the swapped image
        return Response(content=image_data, media_type="image/jpeg")

    except Exception as e:
        logger.error("Error in swap_face: %s", str(e))
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    # Hugging Face Spaces expects the app to run on port 7860
    uvicorn.run(app, host="0.0.0.0", port=7860)