app-1.py

import os
from matplotlib import image
from pathy import BasePath
import tensorflow as tf
import numpy as np
from PIL import Image
import cv2
from keras.models import load_model
from flask import Flask, request, render_template
from werkzeug.utils import secure_filename


app = Flask(__name__)

model=load_model('BrainTumor10Epochs.h5')
print('Model loaded. Check http://127.0.0.1/')

def get_className(classNo):
    if classNo==0:
         return"No Brain Tumor"
    elif classNo==1:
        return"Yes Brain Tumor"


def getResult(img):
    image=cv2.imread(img)
    image = Image.fromarray(image, 'RGB')
    image = image.resize((224, 224))
    image=np.array(image)
    input_img = np.expand_dims(image, axis=0)
    result=model.predict_step(input_img)
    return result


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


@app.route('/predict', methods=['GET', 'POST'])
def upload():
    if request.method == 'POST':
        f = request.files['file']

        basepath = os.path.dirname(__file__)
        file_path = os.path.join(
        basepath, 'uploads', secure_filename(f.filename))
        f.save(file_path)
        value=getResult(file_path)
        result=get_className(value)
        return result
    return None


#if __name__ == '_main_':
   # app.run(debug=True)

if __name__ == '__main__':
    app.run(host='0.0.0.0')