app.py

import base64
import numpy as np
import io
from PIL import Image
#import keras
#from keras import backend as k
#from keras.models import Sequential
#from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.models import load_model
#from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.preprocessing.image import img_to_array
from flask import Flask,request,jsonify

app=Flask(__name__)

def get_model():
    global model
    model=load_model('mask_detector.model')
    print('Model loaded')


def preprocess_image(image,target_size):
    if image.mode!="RGB":
        image=image.convert("RGB")
    image=image.resize(target_size)
    image=img_to_array(image)
    image=np.expand_dims(image,axis=0)
    return image

print('Loading keras model')
get_model()


@app.route("/predict",methods=['POST'])

def predict():
    message=request.get_json(force=True)
    encoded=message['image']
    decoded=base64.b64decode(encoded)
    image=Image.open(io.BytesIO(decoded))
    processed_image=preprocess_image(image,target_size=(224,224))
    prediction=model.predict(processed_image).tolist()

    response={
        'prediction':{
            'mask':prediction[0][0],
            'non_mask':prediction[0][1]
        }
    }
    return jsonify(response)