app.py

from fastapi import FastAPI, UploadFile, File
import torch
from PIL import Image
import torchvision.transforms as T
import io

app = FastAPI()
model = torch.load("model.pt", map_location="cpu")
model.eval()

transform = T.Compose([
    T.Resize((128, 128)),
    T.ToTensor()
])

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    image_bytes = await file.read()
    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
    img_tensor = transform(image).unsqueeze(0)
    with torch.no_grad():
        output = model(img_tensor)
        prediction = "FIRE" if output.item() > 0.5 else "NO FIRE"
    return {"prediction": prediction}