基于Flask和VGG19的图像分类Web应用

import os
import sys

# Flask
from flask import Flask, redirect, url_for, request, render_template, Response, jsonify, redirect
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

from keras.applications.vgg19 import preprocess_input, decode_predictions
from keras.models import load_model
from keras.preprocessing import image

# Some utilites
import numpy as np
from util import base64_to_pil


# Declare a flask app
app = Flask(__name__)


# Load the VGG19 model
model = load_model('models/my_model.h5')
model.make_predict_function()
print('Model loaded. Start serving...')


def model_predict(img, model):
    img = img.resize((224, 224))

    # Preprocessing the image
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)

    preds = model.predict(x)
    return preds


@app.route('/', methods=['GET'])
def index():
    # Main page
    return render_template('index.html')


@app.route('/predict', methods=['GET', 'POST'])
def predict():
    if request.method == 'POST':
        # Get the image from post request
        img = base64_to_pil(request.json)

        # Make prediction
        preds = model_predict(img, model)

        # Process the result
        pred_class = np.argmax(preds, axis=1)
        class_names = ['class1', 'class2', 'class3', 'class4', 'class5', 'class6', 'class7', 'class8', 'class9', 'class10']
        result = []

        for i in range(5):
            result.append(class_names[pred_class[0][i]])

        # Serialize the result, you can add additional fields
        return jsonify(result=result)

    return None


if __name__ == '__main__':
    # Serve the app with gevent
    http_server = WSGIServer(('0.0.0.0', 5000), app)
    http_server.serve_forever()