import os

from flask import Flask, flash, request, redirect, url_for, render_template, send_from_directory

from werkzeug.utils import secure_filename

from tensorflow.keras.models import load_model

import numpy as np

#from keras.preprocessing import image

from tensorflow.keras.preprocessing import image

model=load_model("Esophageal_model.h5")

UPLOAD_FOLDER = 'static/img'

if not os.path.exists(UPLOAD_FOLDER):

    os.makedirs(UPLOAD_FOLDER)

ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}

app = Flask(__name__)

app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

def allowed_file(filename):

    return '.' in filename and \

           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

# Home Page

@app.route('/')

def index():

    return render_template('home.html')

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

def upload_file():

    if request.method == 'POST':

        import uuid

        u = uuid.uuid4()

        # check if the post request has the file part

        if 'file' not in request.files:

            flash('No file part')

            return redirect(request.url)

        file = request.files['file']

        # if user does not select file, browser also

        # submit an empty part without filename

        if file.filename == '':

            flash('No selected file')

            return redirect(request.url)

        if file and allowed_file(file.filename):

            filename = secure_filename(file.filename)

            filename="temp"+u.hex+".jpg"

            fullname=os.path.join(UPLOAD_FOLDER, "temp"+u.hex+".jpg")

            file.save(fullname)

            test_image = image.load_img('static/img/'+filename, target_size = (224,224))

            test_image = image.img_to_array(test_image)

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

            test_image = test_image.astype('float') / 255

            result = model.predict(test_image)

            pred_prob = result.item()

            print(result)

            if result[0]>0.5:

                label = 'NON-Esophageal'

                accuracy = round(pred_prob * 100, 2)

            else:

                pred_1 = round((1 - pred_prob) * 100, 2)

                if pred_1 < 75:

                    label = 'Early Detection of Esophageal'

                    accuracy = round((1 - pred_prob) * 100, 2)

                else:

                    label = 'Esophageal'

                    accuracy = round((1 - pred_prob) * 100, 2)

    return render_template('index.html', label=label, image_file_name=filename, accuracy=accuracy)

@app.route('/upload/<filename>')

def send_file(filename):

    return send_from_directory(UPLOAD_FOLDER, filename)

if __name__ == '__main__':

    app.run(debug=False)