app.py

# coding=utf-8
import numpy as np

# Keras
from keras.applications.imagenet_utils import preprocess_input
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image

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


import os

# Define a flask app
app = Flask(__name__)

# Model saved with Keras model.save()
MODEL_PATH = 'vgg16-0014.hdf5'

# Load your trained model
model = load_model(MODEL_PATH)

def model_predict(img_path, model):
    
    img = image.load_img(img_path, target_size=(224, 224))
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = preprocess_input(img)
    
    preds = model.predict(img)
    return preds


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


@app.route('/predict', methods=['GET', 'POST'])
def upload():
    label = {'cardboard': 0, 
            'glass': 1, 
            'metal': 2, 
            'paper': 3, 
            'plastic': 4,
            'trash': 5}
    if request.method == 'POST':
        # Get the file from post request
        f = request.files['file']
#         file_path = secure_filename(f.filename)

        # Save the file to ./uploads
        basepath = os.path.dirname(__file__)
        file_path = os.path.join(
            basepath, 'uploads', secure_filename(f.filename))
        f.save(file_path)

        # Make prediction
        preds = model_predict(file_path, model)
        y_classes = preds.argmax()
        result = str(list(label.keys())[list(label.values()).index(y_classes)])

        return result
    return None


if __name__ == '__main__':
    app.run(debug=True)