from __future__ import division, print_function

# coding=utf-8

import sys

import os

import glob

import re

import numpy as np

# Keras

from tensorflow.keras.applications.imagenet_utils import preprocess_input, decode_predictions

from tensorflow.keras.models import load_model

from tensorflow.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

app = Flask(__name__)

# Model saved with Keras model.save()

MODEL_PATH ='model_resnet50.h5'

# 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))

    # Preprocessing the image

    x = image.img_to_array(img)

    # x = np.true_divide(x, 255)

    ## Scaling

    x=x/255

    x = np.expand_dims(x, axis=0)

    preds = model.predict(x)

    preds=np.argmax(preds, axis=1)

    if preds==0:

        preds="The Patient has Lymphotic Cancer"

    elif preds==1:

        preds="The Patient has Promyelocytic Cancer"

    else:

        preds="The Patient has Segmented Neutrophils Cancer"

    return preds

@app.route('/', methods=['GET'])

def index():

    # Main page

    return render_template('index.html')

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

def upload():

    if request.method == 'POST':

        # Get the file from post request

        f = request.files['file']

        # 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)

        result=preds

        return result

    return None

if __name__ == '__main__':

    app.run(debug=True)