Spaces:
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db57546 | 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 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | import io
import traceback
from typing import Optional
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse, HTMLResponse, JSONResponse
from huggingface_hub import hf_hub_download
import insightface
import numpy as np
from PIL import Image
import cv2
app = FastAPI(title="FaceSwap API", version="1.0.0")
# CORS (so that GitHub Pages frontend can call this API)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=False,
allow_methods=["*"],
allow_headers=["*"],
)
MODEL_REPO = "deepinsight/inswapper"
MODEL_FILE = "inswapper_128.onnx"
@app.on_event("startup")
def load_models():
# Download ONNX model from Hub
model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE, repo_type="model")
# Load swap model and face analyzer (CPU providers only on free Spaces)
app.state.swapper = insightface.model_zoo.get_model(model_path, providers=["CPUExecutionProvider"])
app.state.analyser = insightface.app.FaceAnalysis(name="buffalo_l", providers=["CPUExecutionProvider"])
app.state.analyser.prepare(ctx_id=0, det_size=(640, 640))
@app.get("/health")
def health():
return {"status": "ok"}
@app.get("/", response_class=HTMLResponse)
def index():
return """
<h2>FaceSwap API</h2>
<form action="/swap" enctype="multipart/form-data" method="post">
Source: <input name="source" type="file" accept="image/*"><br><br>
Target: <input name="target" type="file" accept="image/*"><br><br>
<button type="submit">Swap</button>
</form>
<p>POST /swap with form-data: source, target</p>
"""
def _read_image(file: UploadFile) -> np.ndarray:
data = file.file.read()
pil = Image.open(io.BytesIO(data)).convert("RGB")
return np.array(pil)
@app.post("/swap")
def swap_faces(source: UploadFile = File(...), target: UploadFile = File(...)):
try:
src = _read_image(source)
tgt = _read_image(target)
faces_src = app.state.analyser.get(src)
faces_tgt = app.state.analyser.get(tgt)
if not faces_src:
raise HTTPException(status_code=400, detail="No face detected in source image.")
if not faces_tgt:
raise HTTPException(status_code=400, detail="No face detected in target image.")
face_src = faces_src[0]
face_tgt = faces_tgt[0]
# InsightFace returns BGR
result = app.state.swapper.get(tgt, face_tgt, face_src, paste_back=True)
result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
buf = io.BytesIO()
Image.fromarray(result_rgb).save(buf, format="PNG")
buf.seek(0)
return StreamingResponse(buf, media_type="image/png")
except HTTPException:
raise
except Exception as e:
traceback.print_exc()
return JSONResponse(status_code=500, content={"error": str(e)})
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