diff --git a/updatesApi.py b/updatesApi.py
new file mode 100644
index 0000000..7c17d69
--- /dev/null
+++ b/updatesApi.py
@@ -0,0 +1,73 @@
+import requests
+import cv2
+import numpy as np
+import face_recognition
+import time
+from PIL import Image
+from keras.models import load_model
+from flask import Flask, request, jsonify
+def preprocess_input(x, v2=True):
+    x = x.astype('float32')
+    x = x / 255.0
+    if v2:
+        #x = x - 0.5
+        x= x - 1
+        #done to increase the accuracy
+        x = x * 2.0
+    return x
+#------------------------------------------------------------------------------------------
+emotion_model_path = 'emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
+labels = {0: 'angry', 1: 'disgust', 2: 'fear', 3: 'happy',
+                4: 'sad', 5: 'surprise', 6: 'neutral'}
+emotion_classifier = load_model(emotion_model_path, compile=False)
+emotion_target_size = emotion_classifier.input_shape[1:3]
+emotion_window = []
+
+
+#-----------------------------------------------------------------------------------------------
+
+web = "http://172.16.1.213:5435/shot.jpg"
+start_time = time.time()
+counter = 0
+def emoti():
+    emoticon = []
+    page = requests.get(web)
+    imgN = np.array(bytearray(page.content), dtype = np.uint8)
+    frame = cv2.imdecode(imgN, -1)
+    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+    #cv2.imshow('Frame', small_frame)
+    rgb_small_frame = small_frame[:, :, ::-1]
+    gray_image = cv2.cvtColor(small_frame, cv2.COLOR_BGR2GRAY)
+    # Processing frames
+    face_locations = face_recognition.face_locations(rgb_small_frame)
+    face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
+    for face_location in face_locations:
+        top, right, bottom, left = face_location
+        face_image = gray_image[top:bottom, left:right]
+        pil_image = Image.fromarray(face_image)
+        try:
+            face_image = cv2.resize(face_image, (emotion_target_size))
+        except:
+            continue
+        face_image = preprocess_input(face_image, True)
+        face_image = np.expand_dims(face_image, 0)
+        face_image = np.expand_dims(face_image, -1)
+        emotion_prediction = emotion_classifier.predict(face_image)
+        emotion_probability = np.max(emotion_prediction)
+        emotion_label_arg = np.argmax(emotion_prediction)
+        emotion_text = labels[emotion_label_arg]
+        emoticon.append(emotion_text)
+        return emoticon
+
+application = Flask(__name__)
+
+@application.route("/")
+def hello():
+    emoticon = emoti()
+    data = {
+    'dat':emoticon
+    }
+    return jsonify(data)
+
+if __name__ == "__main__":
+    application.run(host='127.0.0.1',port='8008')