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from fastapi import FastAPI, APIRouter
from fastapi.staticfiles import StaticFiles
from starlette.responses import FileResponse
from fastapi.middleware.cors import CORSMiddleware
import base64
from pydantic import BaseModel
import time
from facenet_pytorch import InceptionResnetV1, MTCNN
import warnings
import face_compare
warnings.filterwarnings('ignore', category=FutureWarning, module='facenet_pytorch')
mtcnn = MTCNN(keep_all=False, device='cpu')
model = InceptionResnetV1(pretrained='vggface2').eval()
app = FastAPI()
router = APIRouter()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
pdf = 0
class ImageData(BaseModel):
image: str
class ImagesData(BaseModel):
idCard: str
profileImage: str
@router.get("/")
async def index() -> FileResponse:
return FileResponse(path="front/dist/index.html", media_type="text/html")
@router.get("/verification")
async def verif() -> FileResponse:
return FileResponse(path="front/dist/index.html", media_type="text/html")
@router.post("/uploadpdf")
async def upload_pdf(data: ImageData):
header, encoded = data.image.split(',', 1)
binary_data = base64.b64decode(encoded)
# Save the pdf
pdf = binary_data
return {"message": "Image reçue et sauvegardée"}
@router.post("/uploadids")
async def upload_ids(data: ImagesData):
header, encoded1 = data.idCard.split(',', 1)
binary_data1 = base64.b64decode(encoded1)
header, encoded2 = data.profileImage.split(',', 1)
binary_data2 = base64.b64decode(encoded2)
output = face_compare.compare_faces(binary_data1, binary_data2)
if output > 0.6:
return {"message": "Les images correspondent"}
else:
return {"message": "Les images ne correspondent pas"}
app.include_router(router)
app.mount("/", StaticFiles(directory="front/dist", html=True), name="static")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000) |