Serving a Trained Model Behind a REST Endpoint

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AI & Machine Learning python

Published on July 14, 2026

Code Snippet

from fastapi import FastAPI, UploadFile
import tensorflow as tf
import numpy as np
from PIL import Image
import io

app = FastAPI()
model = tf.keras.models.load_model("model.keras")   # loaded once
CLASSES = ["normal", "review", "urgent"]

@app.post("/predict")
async def predict(file: UploadFile):
    img = Image.open(io.BytesIO(await file.read())).resize((128, 128))
    arr = np.expand_dims(np.array(img), 0)
    probs = model.predict(arr)[0]
    idx = int(np.argmax(probs))
    return {"label": CLASSES[idx], "confidence": float(probs[idx])}

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