main.py

from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions
from keras.applications.vgg16 import VGG16
# Keras
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.models import load_model
from keras.preprocessing import image

# Flask utils
from flask import Flask, redirect, url_for, request, render_template
from werkzeug.utils import secure_filename
from gevent.pywsgi import WSGIServer
# Define a flask app
# a#pp = Flask(__name__)

# Model saved with Keras model.save()
MODEL_PATH = 'hydro.h5'


def getPrediction(filename):

    # Load your trained model
    model = load_model(MODEL_PATH)
    #model = VGG16()
    image = load_img('uploads/'+filename, target_size=(224, 224))
    image = img_to_array(image)
    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
    image = preprocess_input(image)
    yhat = model.predict(image)
    label = decode_predictions(yhat)
    label = label[0][0]
    print('%s (%.2f%%)' % (label[1], label[2]*100))
    return label[1], label[2]*100