# -*- coding: utf-8 -*-

"""

Created on Wed Mar  2 18:23:11 2022

@author: Rushi

"""

#api libraries

from flask import Flask, request, render_template

#predicting libraries

from tensorflow.keras import optimizers, preprocessing

import tensorflow_addons as tfa

import tensorflow as tf

from keras.models import model_from_json

import numpy as np

import os

app = Flask(__name__)

def get_model():

    global model

    DATA_PATH = "D:/RK/Marwadi University/Sem-8/Project/C-NMC_Leukemia/"

    os.chdir(DATA_PATH)

    json_file = open("model.json","r")

    loaded_model_json = json_file.read()

    json_file.close()

    model = model_from_json(loaded_model_json)

    model.load_weights("model.h5")

    LEARNING_RATE = 3e-5

    optimizer = optimizers.Adam(lr=LEARNING_RATE)

    model.compile(optimizer=optimizer, 

                  loss='binary_crossentropy', 

                  metrics=['accuracy',tfa.metrics.F1Score(num_classes=2, average='weighted')])

def load_image(img_path):

    img = tf.keras.preprocessing.image.load_img(img_path, target_size=(200, 200))

    img_tensor = tf.keras.preprocessing.image.img_to_array(img)                    # (height, width, channels)

    img_tensor = np.expand_dims(img_tensor, axis=0)         # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels)

    img_tensor /= 255

    return img_tensor

def predictions(img_path):

    new_image = load_image(img_path)

    preds = model.predict(new_image)

    prediction = np.argmax(preds)

    pct = "{:.2f}".format(np.max(preds)*100)

    if prediction == 1:

        return ["The Prediction of the sample is: ALL", pct]

    else:

        return ["The Prediction of the sample is: HEM", pct]

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

def home():

    return render_template('home.html')

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

def predict():

    if request.method == 'POST':

        get_model()

        file = request.files['file']

        filename = file.filename

        file_path = os.path.join('static', filename)

        file.save(file_path)

        print(file_path)

        product = predictions(file_path)

        str1 = str(product[1])

        value = ("Prediction Confidence Percentage is: " + str1 + "%")

        print(value)

        return render_template('predict.html', user_image = file_path, product = product[0], value = value) 

if __name__ == "__main__":

    app.run()