app.py

from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import numpy as np
from io import BytesIO
from PIL import Image
import tensorflow as tf

app = FastAPI()

origins = ["*"]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

MODEL = tf.keras.models.load_model("main")
CLASS_NAMES = ["glioma", "meningioma", "notumor", "pituitary"]

@app.get("/ping")
async def ping():
    return "Hello, I am alive"

def read_file_as_image(data) -> np.ndarray:
    image = np.array(Image.open(BytesIO(data)))
    return image

@app.post("/predict")
async def predict(
        file: UploadFile = File(...)
):
    image = read_file_as_image(await file.read())
    img_batch = np.expand_dims(image, 0)
    predictions = MODEL.predict(img_batch)[0]
    predicted_class = CLASS_NAMES[np.argmax(predictions)]
    confidence = np.max(predictions)
    return {
        'class': predicted_class,
        'confidence': float(confidence)
    }

if __name__ == "__main__":
    uvicorn.run(app)