from fastai.vision import *
from starlette.applications import Starlette
from starlette.responses import JSONResponse
from starlette.middleware.cors import CORSMiddleware
import uvicorn
import aiohttp
import asyncio
import keras
import numpy as np
from tensorflow.keras.preprocessing import image

app = Starlette()
app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_headers=["*"], allow_methods=["*"])

model = keras.models.load_model("Detection_Covid_19.h5")

async def save_file(request):
    try:
        form = await request.form()
        file = form['file']
        file_bytes = await file.read()
        file_name = "test.jpg"
        with open(file_name, 'wb') as f:
            f.write(file_bytes)
        
        # Preprocess the image before feeding it to the model
        img = image.load_img(file_name, target_size=(224, 224))
        img = image.img_to_array(img)
        img = np.expand_dims(img, axis=0)
        img = img / 255.0  # Normalize the pixel values

        pred = model.predict(img)
        prediction = "1" if pred[0][0] <= 0.5 else "0"
        
        return JSONResponse({"prediction": prediction})
    
    except Exception as e:
        return JSONResponse(content={"error": str(e)}, status_code=500)

app.add_route("/", save_file, methods=['POST'])