app.py

import tensorflow as tf
from tensorflow import keras
from flask import Flask
from flask import request, jsonify
from PIL import Image
import os

# To force inference using CPU only
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

# Model definition

image_input = keras.Input(shape=(None,None,3))

x = keras.layers.Resizing(height=224, width=224, interpolation='lanczos3', crop_to_aspect_ratio=False)(image_input)

x = keras.layers.Rescaling(scale=1./255, offset=0.0)(x)

mobilenet = keras.applications.MobileNetV2(
    alpha=1.0,
    include_top=True,
    weights="imagenet",
    input_tensor=image_input,
    classes=1000,
    classifier_activation="softmax"
)

model_output = mobilenet(x)

model = keras.Model(inputs=image_input, outputs=model_output)

# Function for inference

def inference(image: tf.Tensor):
    y = model(image).numpy()
    preds = keras.applications.imagenet_utils.decode_predictions(y, top=5)
    result = {i[1] : str(i[2]) for i in preds[0]}
    result = {k: v for k, v in sorted(result.items(), key=lambda item: item[1])}
    return result

# Defining simple Flask app and two endpoints for it

app = Flask(__name__)

@app.route("/", methods=['GET'])
def health_check():
    result = {
        'outcome':'endpoint working successfully'
    }
    return jsonify(result)

@app.route("/inference", methods=['POST'])
def perform_inference():
    image = request.files['image']
    pil_img = Image.open(image.stream)
    tensor = keras.preprocessing.image.img_to_array(pil_img)
    tensor = tf.expand_dims(tensor, axis=0)
    result = inference(tensor)
    return jsonify(result)