app/main.py

import sys
from pathlib import Path
sys.path.append(str(Path(__file__).resolve().parent.parent))

from fastapi import FastAPI, Request, File, UploadFile
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from fastapi.middleware.cors import CORSMiddleware

from app.config import settings
from app.Hackathon_setup import face_recognition, exp_recognition

import numpy as np
from PIL import Image

app = FastAPI(
    title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json"
)

# Mount static folder
app.mount("/static", StaticFiles(directory="app/static"), name="static")

# Templates folder
templates = Jinja2Templates(directory="app/templates")

#################################### Home Page ####################################
@app.get("/")
async def root(request: Request):
    return templates.TemplateResponse("index.html", {"request": request})

#################################### Face Similarity ####################################
@app.get("/similarity/")
async def similarity_root(request: Request):
    return templates.TemplateResponse("similarity.html", {"request": request})

@app.post("/predict_similarity/")
async def predict_similarity(request: Request, file1: UploadFile = File(...), file2: UploadFile = File(...)):
    
    # Save uploaded images
    path1 = f'app/static/{file1.filename}'
    path2 = f'app/static/{file2.filename}'
    
    contents1 = await file1.read()
    with open(path1, 'wb') as f:
        f.write(contents1)
        
    contents2 = await file2.read()
    with open(path2, 'wb') as f:
        f.write(contents2)

    # Open images as RGB
    img1 = np.array(Image.open(path1).convert("RGB"))
    img2 = np.array(Image.open(path2).convert("RGB"))

    # Compute similarity
    result = face_recognition.get_similarity(img1, img2)
    
    return templates.TemplateResponse("predict_similarity.html", {
        "request": request,
        "result": result,  # <-- only this line changed
        "simi_filename1": f"/static/{file1.filename}",
        "simi_filename2": f"/static/{file2.filename}"
    })

#################################### Face Recognition ####################################
@app.get("/face_recognition/")
async def face_recognition_root(request: Request):
    return templates.TemplateResponse("face_recognition.html", {"request": request})

@app.post("/predict_face_recognition/")
async def predict_face_recognition(request: Request, file3: UploadFile = File(...)):

    path = f'app/static/{file3.filename}'
    contents = await file3.read()
    with open(path, 'wb') as f:
        f.write(contents)

    img = np.array(Image.open(path).convert("RGB"))

    result = face_recognition.get_face_class(img)

    return templates.TemplateResponse("predict_face_recognition.html", {
        "request": request,
        "result": result,
        "face_rec_filename": f"/static/{file3.filename}"
    })

#################################### Expression Recognition ####################################
@app.get("/expr_recognition/")
async def expr_recognition_root(request: Request):
    return templates.TemplateResponse("expr_recognition.html", {"request": request})

@app.post("/predict_expr_recognition/")
async def predict_expr_recognition(request: Request, file4: UploadFile = File(...)):

    path = f'app/static/{file4.filename}'
    contents = await file4.read()
    with open(path, 'wb') as f:
        f.write(contents)

    img = np.array(Image.open(path).convert("RGB"))

    result = exp_recognition.get_expression(img)

    return templates.TemplateResponse("predict_expr_recognition.html", {
        "request": request,
        "result": result,
        "expr_rec_filename": f"/static/{file4.filename}"
    })

#################################### CORS Middleware ####################################
if settings.BACKEND_CORS_ORIGINS:
    app.add_middleware(
        CORSMiddleware,
        allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS],
        allow_credentials=True,
        allow_methods=["*"],
        allow_headers=["*"],
    )

#################################### Run App ####################################
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8001)