diff --git a/Attendence/attendence.py b/Attendence/attendence.py
new file mode 100644
index 0000000..97d3c2c
--- /dev/null
+++ b/Attendence/attendence.py
@@ -0,0 +1,547 @@
+from flask import Flask, render_template, request, redirect, Response, send_file
+import multiprocessing
+import face_recognition
+#from numba import jit
+import numpy as np
+import os
+#from flask_cors import CORS
+app = Flask(__name__)
+#CORS(app)
+lst = []
+
+
+@app.route('/', methods=['GET'])
+def resume():
+    #return render_template('index.html')
+    return 'Attendence app running'
+
+def createEncodings(image):
+    print("Encoding")
+    """
+    Create face encodings for a given image and also return face locations in the given image.
+    Parameters
+    -------
+    image : cv2 mat
+        Image you want to detect faces from.
+    Returns
+    -------
+    known_encodings : list of np array
+        List of face encodings in a given image
+    face_locations : list of tuples
+        list of tuples for face locations in a given image
+    """
+    # Find face locations for all faces in an image
+    face_locations = face_recognition.face_locations(image)
+    # Create encodings for all faces in an image
+    known_encodings = face_recognition.face_encodings(image, known_face_locations=face_locations)
+    return known_encodings, face_locations
+#@app.route('/registered', methods=["POST","GET"])
+def registered(url_list):
+    input=url_list
+
+    from pathlib import Path
+    Path("ppeople").mkdir(exist_ok=True)
+    Path("ppeople/" + input["FileName"]).mkdir(exist_ok=True)
+
+    a = input
+    # print(a)
+    x = a['FileData']
+    # print(x)
+    y = a['FileName']
+    #z = a['FileType']
+    z='jpg'
+    # CreatedBy=a['CreatedBy']
+
+    name = y+ '.'+ z
+    print(name)
+    # print(y)
+    # image = y.split("/")
+    # filename=image[-1]
+
+    # print(x)
+    img_data = x.encode()
+
+    import base64
+
+    with open("ppeople/" + input["FileName"] + "/" + name, "wb") as fh:
+        fh.write(base64.decodebytes(img_data))
+
+    img = "ppeople/" + y + "/" + name
+    saveLocation = "ppeople/" + y + "/" + y + ".pickle"
+
+    ############ detecting no of faceses #######################
+
+
+    # import cv2
+    # import numpy as np
+    # import dlib
+
+
+    # # Connects to your computer's default camera
+    # cap = cv2.imread(img)
+
+    # # Detect the coordinates
+    # detector = dlib.get_frontal_face_detector()
+
+    # number_of_faces=[]
+    # # Capture frames continuously
+    # # while True:
+
+    # # Capture frame-by-frame
+    # # ret, frame = cap
+    # frame = cap
+
+    # # RGB to grayscale
+    # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    # faces = detector(gray)
+
+    # # Iterator to count faces
+    # i = 0
+    # for face in faces:
+
+    #     # Get the coordinates of faces
+    #     x, y = face.left(), face.top()
+    #     x1, y1 = face.right(), face.bottom()
+    #     cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)
+    # # Increment iterator for each face in faces
+    #     i = i+1
+
+    # # Display the box and faces
+    #     cv2.putText(frame, 'face num'+str(i), (x-10, y-10),
+    #                 cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+        
+    #     # if len(i)>1:
+    #     #     print(i)
+    #     number_of_faces.append(i)
+
+
+    # if (len(number_of_faces))>1:
+    #     print("Group Photo")
+    #     return "Group Photo"
+    # elif (len(number_of_faces))==1:
+    #     print("Single Photo")
+    #     pass
+
+
+
+
+
+    def saveEncodings(encs, names, fname='encodings.pickle'):
+        """
+        Save encodings in a pickle file to be used in future.
+
+        Parameters
+        ----------
+        encs : List of np arrays
+            List of face encodings.
+        names : List of strings
+            List of names for each face encoding.
+        fname : String, optional
+            Name/Location for pickle file. The default is "encodings.pickle".
+
+        Returns
+        -------
+        None.
+
+        """
+
+        data = []
+        d = [{"name": nm, "encoding": enc} for (nm, enc) in zip(names, encs)]
+        data.extend(d)
+
+        encodingsFile = fname
+
+        # dump the facial encodings data to disk
+        print("[INFO] serializing encodings...")
+        print("[INFO] Encodings Created sucessfully")
+        f = open(encodingsFile, "wb")
+        f.write(pickle.dumps(data))
+        f.close()
+
+    # Function to create encodings and get face locations
+
+
+    def processKnownPeopleImages(img=img, saveLocation=saveLocation):
+        """
+        Process images of known people and create face encodings to compare in future.
+        Eaach image should have just 1 face in it.
+        Parameters
+        ----------
+        path : STRING, optional
+            Path for known people dataset. The default is "C:/inetpub/vhosts/port82/wwwroot/_files/People".
+            It should be noted that each image in this dataset should contain only 1 face.
+        saveLocation : STRING, optional
+            Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
+        Returns
+        -------
+        None.
+        """
+        known_encodings = []
+        known_names = []
+        # for img in os.listdir(path):
+
+        imgPath = img
+        # Read image
+        image = cv2.imread(imgPath)
+        name = img.rsplit('.')[0]
+        # Resize
+        try:
+            print(image.shape)
+        except AttributeError:
+            return "Successfully saved encoding........."
+        image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
+        # Get locations and encodings
+        encs, locs = createEncodings(image)
+        try:
+            known_encodings.append(encs[0])
+        except IndexError:
+            os.remove(saveLocation)
+            print('------------------------------------- save location --------------------------------')
+            print(saveLocation)
+            return "hello world!"
+        
+            
+        # known_encodings.append(encs[0])
+        known_names.append(name)
+        for loc in locs:
+            top, right, bottom, left = loc
+        # Show Image
+        # cv2.rectangle(image, (left, top), (right, bottom), color=(255, 0, 0), thickness=2)
+        # cv2.imshow("Image", image)
+        # cv2.waitKey(1)
+        # cv2.destroyAllWindows()
+        saveEncodings(known_encodings, known_names, saveLocation)
+
+    import cv2
+    #import face_recognition
+    import pickle
+
+    processKnownPeopleImages(img, saveLocation)
+    return 'Successfully saved encoding.........'
+
+
+# ********************************   COMPARUISION *********************************************************
+#@app.route('/submit', methods=["POST","GET"])
+def submit(url_list):
+
+    from datetime import datetime
+    import pytz
+    tz_NY = pytz.timezone('Asia/Kolkata')
+    datetime_NY = datetime.now(tz_NY)
+    India_Date = (datetime_NY.strftime("%Y-%m-%d"))
+    India_Date = str(India_Date)
+    # India_Time = (datetime_NY.strftime("%I:%M:%S %p"))
+    # India_Time = str(India_Time)
+    input=url_list
+    import pickle
+    import cv2
+
+    from pathlib import Path
+    Path("GGallery/"+ India_Date).mkdir(exist_ok=True)
+    Path("GGallery/"+ India_Date +'/'+ input["FileName"]).mkdir(exist_ok=True)
+
+    a = input
+    # print(a)
+    x = a['FileData']
+    # print(x)
+    y = a['FileName']
+   # z = a['FileType']
+    z='jpg'
+    # CreatedBy=a['CreatedBy']
+
+    name = y + '.' + z
+    # print(name)
+    # print(y)
+    # image = y.split("/")
+    # filename=image[-1]
+
+    # print(x)
+    img_data = x.encode()
+
+    import base64
+    
+
+    with open("GGallery/"+India_Date+'/' + input["FileName"] + "/" + name, "wb") as fh:
+        fh.write(base64.decodebytes(img_data))
+
+    path = "GGallery/" +India_Date+'/'+ y + "/" + name
+    pickle_location = "ppeople/" + y + "/" + y + ".pickle"
+    import pathlib
+    file = pathlib.Path(pickle_location)
+    if file.exists ():
+        pass
+    else:
+        print ("pickle File not exist")
+        print(name)
+        return "Face not found in profile (please change your profile)"
+
+    check_faces="ppeople/" + y + "/" + y + ".jpg"
+    print(check_faces)
+
+
+    ############ detecting no of faceses #######################
+
+
+    import cv2
+    import numpy as np
+    import dlib
+
+
+    # Connects to your computer's default camera
+    cap = cv2.imread(check_faces)
+
+    # Detect the coordinates
+    detector = dlib.get_frontal_face_detector()
+
+    number_of_faces=[]
+    # Capture frames continuously
+    # while True:
+
+    # Capture frame-by-frame
+    # ret, frame = cap
+    frame = cap
+
+    # RGB to grayscale
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = detector(gray)
+
+    # Iterator to count faces
+    i = 0
+    for face in faces:
+
+        # Get the coordinates of faces
+        x, y = face.left(), face.top()
+        x1, y1 = face.right(), face.bottom()
+        cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2)
+    # Increment iterator for each face in faces
+        i = i+1
+
+    # Display the box and faces
+        cv2.putText(frame, 'face num'+str(i), (x-10, y-10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+        
+        # if len(i)>1:
+        #     print(i)
+        number_of_faces.append(i)
+
+
+    if (len(number_of_faces))>1:
+        print("Group Photo")
+        return "Group Photo"
+    elif (len(number_of_faces))==1:
+        print("Single Photo")
+        pass
+
+
+
+
+    def readEncodingsPickle(fname):
+        """
+        Read Pickle file.
+
+        Parameters
+        ----------
+        fname : String
+            Name of pickle file.(Full location)
+
+        Returns
+        -------
+        encodings : list of np arrays
+            list of all saved encodings
+        names : List of Strings
+            List of all saved names
+
+        """
+
+
+        data = pickle.loads(open(fname, "rb").read())
+
+
+        data = np.array(data)
+        encodings = [d["encoding"] for d in data]
+        names = [d["name"] for d in data]
+        return encodings, names
+
+
+    def compareFaceEncodings(unknown_encoding, known_encodings, known_names):
+        """
+        Compares face encodings to check if 2 faces are same or not.
+
+        Parameters
+        ----------
+        unknown_encoding : np array
+            Face encoding of unknown people.
+        known_encodings : np array
+            Face encodings of known people.
+        known_names : list of strings
+            Names of known people
+
+        Returns
+        -------
+        acceptBool : Bool
+            face matched or not
+        duplicateName : String
+            Name of matched face
+        distance : Float
+            Distance between 2 faces
+
+        """
+        duplicateName = ""
+        distance = 0.0
+        matches = face_recognition.compare_faces(known_encodings, unknown_encoding, tolerance=0.54)
+        face_distances = face_recognition.face_distance(known_encodings, unknown_encoding)
+
+        best_match_index = np.argmin(face_distances)
+
+        distance = face_distances[best_match_index]
+        if matches[best_match_index]:
+            acceptBool = True
+            duplicateName = known_names[best_match_index]
+        else:
+            acceptBool = False
+            duplicateName = ""
+        return acceptBool, duplicateName, distance
+
+        #p = []
+
+
+    def processDatasetImages(path=path, pickle_location=pickle_location):
+        """
+        Process image in dataset from where you want to separate images.
+        It separates the images into directories of known people, groups and any unknown people images.
+        Parameters
+        ----------
+        path : STRING, optional
+            Path for known people dataset. The default is "D:/port1004/port1004/wwwroot/_files/People".
+            It should be noted that each image in this dataset should contain only 1 face.
+        saveLocation : STRING, optional
+            Path for storing encodings for known people dataset. The default is "./known_encodings.pickle in current directory".
+
+        Returns
+        -------
+        None.
+
+        """
+        # Read pickle file for known people to compare faces from
+
+        people_encodings, names = readEncodingsPickle(pickle_location)
+        # print(p)
+        #  imgPath = path + img
+
+        # Read image
+        # path=r"C:\Users\katku\Pictures\final\100011460000611.jpg"
+        image = cv2.imread(path)
+        #orig = image.copy()
+
+        # Resize
+        image = cv2.resize(image, (0, 0), fx=0.9, fy=0.9, interpolation=cv2.INTER_LINEAR)
+
+        # Get locations and encodings
+        encs, locs = createEncodings(image)
+
+        # Save image to a group image folder if more than one face is in image
+        # if len(locs) > 1:
+        #     saveImageToDirectory(orig, "Group", img)
+
+        # Processing image for each face
+        i = 0
+        knownFlag = 0
+        for loc in locs:
+            top, right, bottom, left = loc
+            unknown_encoding = encs[i]
+            i += 1
+            acceptBool, duplicateName, distance = compareFaceEncodings(unknown_encoding, people_encodings, names)
+            if acceptBool:
+                # saveImageToDirectory(orig, duplicateName,name)
+                knownFlag = 1
+        if knownFlag == 1:
+            print("Match Found")
+
+
+            #print(path)
+            with_extension = path.split("/")[-1]
+            without_extension = with_extension.split(".")[0]
+
+            # output_s = {"FileID": without_extension,
+            #             "Date": India_Date,
+            #             "Time": India_Time}
+            # output_json = json.dumps(output_s)
+            output_json='Matched successfully'
+            print(loc)
+            lst.append(output_json)
+
+            print(output_json)
+            # exit()
+
+        else:
+            print('Not Matched')
+            pass
+            # saveImageToDirectory(orig, "0",name)
+
+
+    import numpy as np
+    import json
+
+    processDatasetImages(path, pickle_location)
+    return lst[0]
+    #return 'matched successfully'
+
+
+@app.route('/detect', methods=["POST"])
+def detect():
+    if __name__ == "__main__":
+
+        url_list=[]
+        Dataset=  request.get_json()
+        # id = "100013660000125"
+        url_list.append(Dataset)
+        # multiprocessing
+        pool_size = multiprocessing.cpu_count() * 2
+        with multiprocessing.Pool(pool_size) as pool:
+            try:
+                results = pool.map(submit, url_list)
+            except FileNotFoundError:
+                return 'plese get registered with your PhotoID'
+            except IndexError:
+                #return 'unable to recognize face'
+                return 'failed'
+
+        pool.close()
+
+        return results[0]
+
+
+
+@app.route('/register', methods=["POST"])
+def register():
+    print("hello start..........")
+    if __name__ == "__main__":
+
+        url_list=[]
+        Dataset=  request.get_json()
+        # id = "100013660000125"
+        url_list.append(Dataset)
+        # multiprocessing
+        pool_size = multiprocessing.cpu_count() * 2
+        with multiprocessing.Pool(pool_size) as pool:
+            try:
+                results = pool.map(registered, url_list)
+            except IndexError:
+                pass
+                print('face not found')
+            except FileNotFoundError:
+                pass
+                
+            
+                #os.remove(img)
+               # return 'unable to recognize face'
+
+        pool.close()
+        #return results[0]
+        return 'Successfully saved encoding.........'
+
+
+
+
+if __name__ == "__main__":
+    app.run(host='0.0.0.0',port =5003,debug=False)