--- a
+++ b/app.py
@@ -0,0 +1,173 @@
+import numpy as np  # dealing with arrays
+import os  # dealing with directories
+from random import shuffle  # mixing up or currently ordered data that might lead our network astray in training.
+from tqdm import \
+    tqdm  # a nice pretty percentage bar for tasks. Thanks to viewer Daniel BA1/4hler for this suggestion
+import tflearn
+from tflearn.layers.conv import conv_2d, max_pool_2d
+from tflearn.layers.core import input_data, dropout, fully_connected
+from tflearn.layers.estimator import regression
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from flask import Flask, render_template, url_for, request
+import sqlite3
+import cv2
+import shutil
+
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template('home.html')
+
+@app.route('/userlog', methods=['GET', 'POST'])
+def userlog():
+    if request.method == 'POST':
+
+        connection = sqlite3.connect('user_data.db')
+        cursor = connection.cursor()
+
+        name = request.form['name']
+        password = request.form['password']
+
+        query = "SELECT name, password FROM user WHERE name = '"+name+"' AND password= '"+password+"'"
+        cursor.execute(query)
+
+        result = cursor.fetchall()
+
+        if len(result) == 0:
+            return render_template('index.html', msg='Sorry, Incorrect Credentials Provided,  Try Again')
+        else:
+            return render_template('userlog.html')
+
+    return render_template('index.html')
+
+
+@app.route('/userreg', methods=['GET', 'POST'])
+def userreg():
+    if request.method == 'POST':
+
+        connection = sqlite3.connect('user_data.db')
+        cursor = connection.cursor()
+
+        name = request.form['name']
+        password = request.form['password']
+        mobile = request.form['phone']
+        email = request.form['email']
+        
+        print(name, mobile, email, password)
+
+        command = """CREATE TABLE IF NOT EXISTS user(name TEXT, password TEXT, mobile TEXT, email TEXT)"""
+        cursor.execute(command)
+
+        cursor.execute("INSERT INTO user VALUES ('"+name+"', '"+password+"', '"+mobile+"', '"+email+"')")
+        connection.commit()
+
+        return render_template('index.html', msg='Successfully Registered')
+    
+    return render_template('index.html')
+
+@app.route('/image', methods=['GET', 'POST'])
+def image():
+    if request.method == 'POST':
+        
+                
+        dirPath = "static/images"
+        fileList = os.listdir(dirPath)
+        for fileName in fileList:
+            os.remove(dirPath + "/" + fileName)
+        fileName=request.form['filename']
+        dst = "static/images"
+        
+
+        shutil.copy("test\\"+fileName, dst)
+        
+        verify_dir = 'static/images'
+        IMG_SIZE = 50
+        LR = 1e-3
+        MODEL_NAME = 'HEMMORRHAGE-{}-{}.model'.format(LR, '2conv-basic')
+    ##    MODEL_NAME='keras_model.h5'
+        def process_verify_data():
+            verifying_data = []
+            for img in os.listdir(verify_dir):
+                path = os.path.join(verify_dir, img)
+                img_num = img.split('.')[0]
+                img = cv2.imread(path, cv2.IMREAD_COLOR)
+                img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+                verifying_data.append([np.array(img), img_num])
+                np.save('verify_data.npy', verifying_data)
+            return verifying_data
+
+        verify_data = process_verify_data()
+        #verify_data = np.load('verify_data.npy')
+
+        
+        tf.compat.v1.reset_default_graph()
+        #tf.reset_default_graph()
+
+        convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3], name='input')
+
+        convnet = conv_2d(convnet, 32, 3, activation='relu')
+        convnet = max_pool_2d(convnet, 3)
+
+        convnet = conv_2d(convnet, 64, 3, activation='relu')
+        convnet = max_pool_2d(convnet, 3)
+
+        convnet = conv_2d(convnet, 128, 3, activation='relu')
+        convnet = max_pool_2d(convnet, 3)
+
+        convnet = conv_2d(convnet, 32, 3, activation='relu')
+        convnet = max_pool_2d(convnet, 3)
+
+        convnet = conv_2d(convnet, 64, 3, activation='relu')
+        convnet = max_pool_2d(convnet, 3)
+
+        convnet = fully_connected(convnet, 1024, activation='relu')
+        convnet = dropout(convnet, 0.8)
+
+        convnet = fully_connected(convnet, 2, activation='softmax')
+        convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
+
+        model = tflearn.DNN(convnet, tensorboard_dir='log')
+
+        if os.path.exists('{}.meta'.format(MODEL_NAME)):
+            model.load(MODEL_NAME)
+            print('model loaded!')
+
+
+        accuracy=" "
+        str_label=" "
+        for num, data in enumerate(verify_data):
+
+            img_num = data[1]
+            img_data = data[0]
+
+            #y = fig.add_subplot(3, 4, num + 1)
+            orig = img_data
+            data = img_data.reshape(IMG_SIZE, IMG_SIZE, 3)
+            # model_out = model.predict([data])[0]
+            model_out = model.predict([data])[0]
+            print(model_out)
+            print('model {}'.format(np.argmax(model_out)))
+
+            if np.argmax(model_out) == 0:
+                str_label = 'HEMORRHAGE'
+                print("The predicted image of the brain with hemmorrhage detected with a accuracy of {} %".format(model_out[0]*90))
+                accuracy = "The predicted image of the brain with hemmorrhage detected  with a accuracy of {} %".format(model_out[0]*90)
+           
+            elif np.argmax(model_out) == 1:
+                str_label = 'NORMAL'
+                print("The predicted image of the brain is normal with a accuracy of {} %".format(model_out[1]*100))
+                accuracy = "The predicted image of the brain is normal with a accuracy of {} %".format(model_out[1]*100)
+
+           
+
+           
+
+        return render_template('home.html', status=str_label,accuracy=accuracy, ImageDisplay="http://127.0.0.1:5000/static/images/"+fileName)
+    return render_template('home.html')
+
+if __name__ == "__main__":
+
+    app.run(debug=True, use_reloader=False)