from flask import Flask, render_template, request
import keras
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
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
from keras.applications.resnet50 import ResNet50

app = Flask(__name__)
model = ResNet50()

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

@app.route('/', methods=['POST'])
def predict():
    imagefile= request.files['imagefile']
    image_path = "./" + imagefile.filename
    imagefile.save(image_path)

    image = load_img(image_path, 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)
    # [0][0] = (class label and probability) EX "cat, (85.00%)"
    label = label[0][0]

    classification = '%s (%.2f%%)' % (label[1], label[2]*100)


    return render_template('index.html', prediction=classification)


if __name__ == '__main__':
    app.run(port=3000, debug=True)