import io
import time
from PIL import Image
from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from transformers import pipeline
import uvicorn

app = FastAPI()

# Enable CORS so frontend on Vercel can access backend
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load model once at startup
detector = pipeline(
    "image-classification",
    model="google/vit-base-patch16-224"
)

@app.get("/")
def home():
    return {"message": "DeepSentry backend running"}

@app.post("/detect/image")
async def detect_image(file: UploadFile = File(...)):

    start = time.time()

    contents = await file.read()
    image = Image.open(io.BytesIO(contents)).convert("RGB")

    result = detector(image)[0]

    label = result["label"]
    confidence = round(result["score"] * 100, 2)

    elapsed = int((time.time() - start) * 1000)

    verdict = "Real" if "real" in label.lower() else "Fake"

    return {
        "label": verdict,
        "confidence": confidence,
        "media_type": "image",
        "elapsed_ms": elapsed
    }


@app.post("/detect/video")
async def detect_video(file: UploadFile = File(...)):

    start = time.time()

    # placeholder video detection
    elapsed = int((time.time() - start) * 1000)

    return {
        "label": "Real",
        "confidence": 50.0,
        "media_type": "video",
        "elapsed_ms": elapsed
    }


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)