--- a
+++ b/app.py
@@ -0,0 +1,179 @@
+from __future__ import division, print_function
+import json
+# coding=utf-8
+import sys
+import os
+import glob
+import re
+import numpy as np
+import cv2
+import pandas as pd
+import numpy as np
+import biosppy
+import matplotlib.pyplot as plt
+# Keras
+from keras.applications.imagenet_utils import preprocess_input, decode_predictions
+from keras.models import load_model
+from 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()
+
+# Load your trained model
+model = load_model('path to the model')
+model._make_predict_function()          # Necessary
+print('Model loaded. Start serving...')
+output = []
+# You can also use pretrained model from Keras
+# Check https://keras.io/applications/
+#from keras.applications.resnet50 import ResNet50
+#model = ResNet50(weights='imagenet')
+#print('Model loaded. Check http://127.0.0.1:5000/')
+
+def model_predict(uploaded_files, model):
+    flag = 1
+    
+    for path in uploaded_files:
+        #index1 = str(path).find('sig-2') + 6
+        #index2 = -4
+        #ts = int(str(path)[index1:index2])
+        APC, NORMAL, LBB, PVC, PAB, RBB, VEB = [], [], [], [], [], [], []
+        output.append(str(path))
+        result = {"APC": APC, "Normal": NORMAL, "LBB": LBB, "PAB": PAB, "PVC": PVC, "RBB": RBB, "VEB": VEB}
+
+        
+        indices = []
+        
+        kernel = np.ones((4,4),np.uint8)
+        
+        csv = pd.read_csv(path)
+        csv_data = csv[' Sample Value']
+        data = np.array(csv_data)
+        signals = []
+        count = 1
+        peaks =  biosppy.signals.ecg.christov_segmenter(signal=data, sampling_rate = 200)[0]
+        for i in (peaks[1:-1]):
+           diff1 = abs(peaks[count - 1] - i)
+           diff2 = abs(peaks[count + 1]- i)
+           x = peaks[count - 1] + diff1//2
+           y = peaks[count + 1] - diff2//2
+           signal = data[x:y]
+           signals.append(signal)
+           count += 1
+           indices.append((x,y))
+
+            
+        for count, i in enumerate(signals):
+            fig = plt.figure(frameon=False)
+            plt.plot(i) 
+            plt.xticks([]), plt.yticks([])
+            for spine in plt.gca().spines.values():
+                spine.set_visible(False)
+
+            filename = 'fig' + '.png'
+            fig.savefig(filename)
+            im_gray = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
+            im_gray = cv2.erode(im_gray,kernel,iterations = 1)
+            im_gray = cv2.resize(im_gray, (128, 128), interpolation = cv2.INTER_LANCZOS4)
+            cv2.imwrite(filename, im_gray)
+            im_gray = cv2.imread(filename)
+            pred = model.predict(im_gray.reshape((1, 128, 128, 3)))
+            pred_class = pred.argmax(axis=-1)
+            if pred_class == 0:
+                APC.append(indices[count]) 
+            elif pred_class == 1:
+                NORMAL.append(indices[count]) 
+            elif pred_class == 2:    
+                LBB.append(indices[count])
+            elif pred_class == 3:
+                PAB.append(indices[count])
+            elif pred_class == 4:
+                PVC.append(indices[count])
+            elif pred_class == 5:
+                RBB.append(indices[count]) 
+            elif pred_class == 6:
+                VEB.append(indices[count])
+        
+
+
+        result = sorted(result.items(), key = lambda y: len(y[1]))[::-1]   
+        output.append(result)
+        data = {}
+        data['filename'+ str(flag)] = str(path)
+        data['result'+str(flag)] = str(result)
+
+        json_filename = 'data.txt'
+        with open(json_filename, 'a+') as outfile:
+            json.dump(data, outfile) 
+        flag+=1 
+    
+
+
+
+    with open(json_filename, 'r') as file:
+        filedata = file.read()
+    filedata = filedata.replace('}{', ',')
+    with open(json_filename, 'w') as file:
+        file.write(filedata) 
+    os.remove('fig.png')      
+    return output
+    
+    
+
+    
+    
+
+
+@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
+        uploaded_files = []
+
+        # Save the file to ./uploads
+        print(uploaded_files)
+        for f in request.files.getlist('file'):
+
+            basepath = os.path.dirname(__file__)
+            file_path = os.path.join(
+            basepath, 'uploads', secure_filename(f.filename))
+            print(file_path)
+            if file_path[-4:] == '.csv':
+                uploaded_files.append(file_path)
+                f.save(file_path)
+        print(uploaded_files)        
+        # Make prediction
+        pred = model_predict(uploaded_files, model)
+
+
+        # Process your result for human
+                    # Simple argmax
+        #pred_class = decode_predictions(pred, top=1)   # ImageNet Decode
+        #result = str(pred_class[0][0][1])               # Convert to string
+        result = str(pred)
+        
+
+        return result
+    return None
+
+
+if __name__ == '__main__':
+    # app.run(port=5002, debug=True)
+
+    # Serve the app with gevent
+    http_server = WSGIServer(('', 5000), app)
+    http_server.serve_forever()