diff --git a/emotion_predictor.py b/emotion_predictor.py
index b5d5751..62d98d8 100644
--- a/emotion_predictor.py
+++ b/emotion_predictor.py
@@ -10,7 +10,7 @@
 clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
 detector = get_frontal_face_detector()
 predictor = shape_predictor("/home/deeplearning/Desktop/shape_predictor_68_face_landmarks.dat")
-clf = pickle.load(open("/home/deeplearning/PycharmProjects/project2/model.xml", "rb"))
+clf = pickle.load(open("/home/deeplearning/PycharmProjects/project2/model.sav", "rb"))
 emotions = ["happy", "anger", "fear", "sadness", "disgust", "surprise", "neutral"]
 
 
@@ -21,14 +21,17 @@ def get_landmarks(image):
         xlist = []
         ylist = []
         for i in range(1,68):
+            cv2.circle(image, (shape.part(i).x, shape.part(i).y), 1, (0, 0, 255), thickness=2)
             xlist.append(shape.part(i).x)
             ylist.append(shape.part(i).y)
+        cv2.imshow("got landmarks", image)
+        cv2.waitKey(3000)
         xmean = np.mean(xlist)
         ymean = np.mean(ylist)
         xcentral = [(x-xmean) for x in xlist]
         ycentral = [(y-ymean) for y in ylist]
         vectorized_landmarks = []
-        for x,y,w,z in zip(xcentral, ycentral, xlist, ylist):
+        for x, y, w, z in zip(xcentral, ycentral, xlist, ylist):
             vectorized_landmarks.append(w)
             vectorized_landmarks.append(z)
             mean_array = np.asarray((ymean,xmean))
@@ -41,13 +44,54 @@ def get_landmarks(image):
     data["landmark_vectorization"] = vectorized_landmarks
 
 
-prediction_image = cv2.imread("/home/deeplearning/Desktop/face_recognition examples/biden.jpg") #input your image
+prediction_image = cv2.imread("/home/deeplearning/Desktop/face_recognition examples/obama-720p.jpg")
 gray = cv2.cvtColor(prediction_image, cv2.COLOR_BGR2GRAY)
-clahe_image = clahe.apply(gray)
-get_landmarks(clahe_image)
+cv2.imshow("initial image", gray)
+cv2.waitKey(3000)
+faceDet = cv2.CascadeClassifier("/home/deeplearning/PycharmProjects/project2/haarcascade_frontalface_default.xml")
+faceDet2 = cv2.CascadeClassifier("/home/deeplearning/PycharmProjects/project2/haarcascade_frontalface_alt2.xml")
+faceDet3 = cv2.CascadeClassifier("/home/deeplearning/PycharmProjects/project2/haarcascade_frontalface_alt.xml")
+faceDet4 = cv2.CascadeClassifier("/home/deeplearning/PycharmProjects/project2/haarcascade_frontalface_alt_tree.xml")
+face = faceDet.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
+face2 = faceDet2.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
+face3 = faceDet3.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
+face4 = faceDet4.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5), flags=cv2.CASCADE_SCALE_IMAGE)
+
+if len(face) == 1:
+    facefeatures = face
+elif len(face2) == 1:
+    facefeatures = face2
+elif len(face3) == 1:
+    facefeatures = face3
+elif len(face4) == 1:
+    facefeatures = ""
+for (x, y, w, h) in facefeatures:  # Get coordinates and size of rectangle containing face
+        gray = gray[y:y+h, x:x+w]  # Cut the frame to size
+        try:
+            out = cv2.resize(gray, (350, 350))
+            clahe_image = clahe.apply(out)
+            cv2.imshow("clahe image", clahe_image)
+            cv2.waitKey(4000)
+            get_landmarks(clahe_image)
+        except:
+            pass
+
+
 if data["landmark_vectorization"] == "error":
     print("no faces detected")
 else:
     prediction_data.append(data["landmark_vectorization"])
 nparray = np.array(prediction_data)
-print(clf.predict_proba(nparray))
+final_results = clf.predict_proba(nparray)[0]
+font = cv2.FONT_HERSHEY_SIMPLEX
+initial_point = 100
+for a,b in zip(emotions, final_results):
+    confidence_text = str(str(a) + " : " + str(b * 100) + " %")
+    cv2.putText(prediction_image, confidence_text, (30, initial_point), font, 1, (255,0,0), 1, cv2.LINE_AA)
+    initial_point = initial_point + 50
+final_text = str("Emotion : " + str(emotions[int(np.argmax(final_results))]))
+cv2.putText(prediction_image, final_text, (30, initial_point), font, 1, (255,0,0), 1, cv2.LINE_AA)
+cv2.imshow("final output", prediction_image)
+cv2.waitKey(6000)
+
+