diff --git a/emotion_detection system b/emotion_detection system
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+++ b/emotion_detection system	
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+########## train.py ################
+
+import sys, os  
+import pandas as pd  
+import numpy as np  
+  
+from keras.models import Sequential  
+from keras.layers import Dense, Dropout, Activation, Flatten  
+from keras.layers import Conv2D, MaxPooling2D, BatchNormalization,AveragePooling2D  
+from keras.losses import categorical_crossentropy  
+from keras.optimizers import Adam  
+from keras.regularizers import l2  
+from keras.utils import np_utils  
+# pd.set_option('display.max_rows', 500)  
+# pd.set_option('display.max_columns', 500)  
+# pd.set_option('display.width', 1000)  
+  
+df=pd.read_csv('fer2013.csv')  
+  
+# print(df.info())  
+# print(df["Usage"].value_counts())  
+  
+# print(df.head())  
+X_train,train_y,X_test,test_y=[],[],[],[]  
+  
+for index, row in df.iterrows():  
+    val=row['pixels'].split(" ")  
+    try:  
+        if 'Training' in row['Usage']:  
+           X_train.append(np.array(val,'float32'))  
+           train_y.append(row['emotion'])  
+        elif 'PublicTest' in row['Usage']:  
+           X_test.append(np.array(val,'float32'))  
+           test_y.append(row['emotion'])  
+    except:  
+        print(f"error occured at index :{index} and row:{row}")  
+  
+  
+num_features = 64  
+num_labels = 7  
+batch_size = 64  
+epochs = 30  
+width, height = 48, 48  
+  
+  
+X_train = np.array(X_train,'float32')  
+train_y = np.array(train_y,'float32')  
+X_test = np.array(X_test,'float32')  
+test_y = np.array(test_y,'float32')  
+  
+train_y=np_utils.to_categorical(train_y, num_classes=num_labels)  
+test_y=np_utils.to_categorical(test_y, num_classes=num_labels)  
+  
+#cannot produce  
+#normalizing data between oand 1  
+X_train -= np.mean(X_train, axis=0)  
+X_train /= np.std(X_train, axis=0)  
+  
+X_test -= np.mean(X_test, axis=0)  
+X_test /= np.std(X_test, axis=0)  
+  
+X_train = X_train.reshape(X_train.shape[0], 48, 48, 1)  
+  
+X_test = X_test.reshape(X_test.shape[0], 48, 48, 1)  
+  
+# print(f"shape:{X_train.shape}")  
+##designing the cnn  
+#1st convolution layer  
+model = Sequential()  
+  
+model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', input_shape=(X_train.shape[1:])))  
+model.add(Conv2D(64,kernel_size= (3, 3), activation='relu'))  
+# model.add(BatchNormalization())  
+model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))  
+model.add(Dropout(0.5))  
+  
+#2nd convolution layer  
+model.add(Conv2D(64, (3, 3), activation='relu'))  
+model.add(Conv2D(64, (3, 3), activation='relu'))  
+# model.add(BatchNormalization())  
+model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))  
+model.add(Dropout(0.5))  
+  
+#3rd convolution layer  
+model.add(Conv2D(128, (3, 3), activation='relu'))  
+model.add(Conv2D(128, (3, 3), activation='relu'))  
+# model.add(BatchNormalization())  
+model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2)))  
+  
+model.add(Flatten())  
+  
+#fully connected neural networks  
+model.add(Dense(1024, activation='relu'))  
+model.add(Dropout(0.2))  
+model.add(Dense(1024, activation='relu'))  
+model.add(Dropout(0.2))  
+  
+model.add(Dense(num_labels, activation='softmax'))  
+  
+# model.summary()  
+  
+#Compliling the model  
+model.compile(loss=categorical_crossentropy,  
+              optimizer=Adam(),  
+              metrics=['accuracy'])  
+  
+#Training the model  
+model.fit(X_train, train_y,  
+          batch_size=batch_size,  
+          epochs=epochs,  
+          verbose=1,  
+          validation_data=(X_test, test_y),  
+          shuffle=True)  
+  
+  
+#Saving the  model to  use it later on  
+fer_json = model.to_json()  
+with open("fer.json", "w") as json_file:  
+    json_file.write(fer_json)  
+model.save_weights("fer.h5")  
+
+
+
+############# videotester.py ##############
+
+import os  
+import cv2  
+import numpy as np  
+from keras.models import model_from_json  
+from keras.preprocessing import image  
+  
+#load model  
+model = model_from_json(open("fer.json", "r").read())  
+#load weights  
+model.load_weights('fer.h5')  
+  
+  
+face_haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')  
+  
+  
+cap=cv2.VideoCapture(0)  
+  
+while True:  
+    ret,test_img=cap.read()# captures frame and returns boolean value and captured image  
+    if not ret:  
+        continue  
+    gray_img= cv2.cvtColor(test_img, cv2.COLOR_BGR2GRAY)  
+  
+    faces_detected = face_haar_cascade.detectMultiScale(gray_img, 1.32, 5)  
+  
+  
+    for (x,y,w,h) in faces_detected:  
+        cv2.rectangle(test_img,(x,y),(x+w,y+h),(255,0,0),thickness=7)  
+        roi_gray=gray_img[y:y+w,x:x+h]#cropping region of interest i.e. face area from  image  
+        roi_gray=cv2.resize(roi_gray,(48,48))  
+        img_pixels = image.img_to_array(roi_gray)  
+        img_pixels = np.expand_dims(img_pixels, axis = 0)  
+        img_pixels /= 255  
+  
+        predictions = model.predict(img_pixels)  
+  
+        #find max indexed array  
+        max_index = np.argmax(predictions[0])  
+  
+        emotions = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')  
+        predicted_emotion = emotions[max_index]  
+  
+        cv2.putText(test_img, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)  
+  
+    resized_img = cv2.resize(test_img, (1000, 700))  
+    cv2.imshow('Facial emotion analysis ',resized_img)  
+  
+  
+  
+    if cv2.waitKey(10) == ord('q'):#wait until 'q' key is pressed  
+        break  
+  
+cap.release()  
+cv2.destroyAllWindows  
+
+