diff --git a/CODE b/CODE
index d44e18f..6ea569b 100644
--- a/CODE
+++ b/CODE
@@ -1 +1,153 @@
-start
+import cv2
+import sys,os
+
+data_path='dataset'
+categories=os.listdir(data_path)
+labels=[i for i in range(len(categories))]
+
+label_dict=dict(zip(categories,labels)) #empty dictionary
+
+print(label_dict)
+print(categories)
+print(labels)
+
+img_size=100
+data=[]
+target=[]
+
+
+for category in categories:
+    folder_path=os.path.join(data_path,category)
+    img_names=os.listdir(folder_path)
+        
+    for img_name in img_names:
+        img_path=os.path.join(folder_path,img_name)
+        img=cv2.imread(img_path)
+
+        try:
+            gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)           
+            #Coverting the image into gray scale
+            resized=cv2.resize(gray,(img_size,img_size))
+            #resizing the gray scale into 50x50, since we need a fixed common size for all the images in the dataset
+            data.append(resized)
+            target.append(label_dict[category])
+            #appending the image and the label(categorized) into the list (dataset)
+
+        except Exception as e:
+            print('Exception:',e)
+            #if any exception rasied, the exception will be printed here. And pass to the next image
+            
+import numpy as np
+
+data=np.array(data)/255.0
+data=np.reshape(data,(data.shape[0],img_size,img_size,1))
+target=np.array(target)
+
+from keras.utils import np_utils
+
+new_target=np_utils.to_categorical(target)
+
+np.save('data',data)
+np.save('target',new_target)
+
+#####################################################################################################################################################################################################################
+#Part 2
+# Training the CNN 
+import numpy as np
+
+data=np.load('data.npy')
+target=np.load('target.npy')
+
+#loading the save numpy arrays in the previous code
+from keras.models import Sequential
+from keras.layers import Dense,Activation,Flatten,Dropout
+from keras.layers import Conv2D,MaxPooling2D
+from keras.callbacks import ModelCheckpoint
+
+model=Sequential()
+model.add(Conv2D(200,(3,3),input_shape=data.shape[1:]))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2,2)))
+#The first CNN layer followed by Relu and MaxPooling layers
+model.add(Conv2D(100,(3,3)))
+model.add(Activation('relu'))
+model.add(MaxPooling2D(pool_size=(2,2)))
+#The second convolution layer followed by Relu and MaxPooling layers
+model.add(Flatten())
+model.add(Dropout(0.5))
+#Flatten layer to stack the output convolutions from second convolution layer
+model.add(Dense(50,activation='relu'))
+#Dense layer of 64 neurons
+model.add(Dense(2,activation='softmax'))
+#The Final layer with two outputs for two categories
+model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
+
+from sklearn.model_selection import train_test_split
+train_data,test_data,train_target,test_target=train_test_split(data,target,test_size=0.1)
+
+checkpoint = ModelCheckpoint('model-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')
+history=model.fit(train_data,train_target,epochs=20,callbacks=[checkpoint],validation_split=0.2)
+
+from matplotlib import pyplot as plt
+
+plt.plot(history.history['loss'],'r',label='training loss')
+plt.plot(history.history['val_loss'],label='validation loss')
+plt.xlabel('# epochs')
+plt.ylabel('loss')
+plt.legend()
+plt.show()
+
+plt.plot(history.history['accuracy'],'r',label='training accuracy')
+plt.plot(history.history['val_accuracy'],label='validation accuracy')
+plt.xlabel('# epochs')
+plt.ylabel('loss')
+plt.legend()
+plt.show()
+print(model.evaluate(test_data,test_target))
+
+#######################################################################################################################################
+
+# Part 3
+# Detecting Mask
+
+from keras.models import load_model
+import cv2
+import numpy as np
+
+model = load_model('model-012.model')
+#face_clsfr=cv2.CascadeClassifier(f"{cv2.haarcascades}haarcascade_frontalface_default.xml")
+#face_clsfr=cv2.CascadeClassifier(cv2.data.haarcascades +"haarcascade_frontalface_default.xml")
+face_clsfr=cv2.CascadeClassifier('H:\\haarcascades/haarcascade_frontalface_default.xml')
+source=cv2.VideoCapture(2)
+labels_dict={0:'MASK',1:'NO MASK'}
+color_dict={0:(0,255,0),1:(0,0,255)}
+
+while(True):
+
+    ret,img=source.read()
+    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+    faces=face_clsfr.detectMultiScale(gray,1.3,5)  
+
+    for (x,y,w,h) in faces:
+    
+        face_img=gray[y:y+w,x:x+w]
+        resized=cv2.resize(face_img,(100,100))
+        normalized=resized/255.0
+        reshaped=np.reshape(normalized,(1,100,100,1))
+        result=model.predict(reshaped)
+
+        label=np.argmax(result,axis=1)[0]
+      
+        cv2.rectangle(img,(x,y),(x+w,y+h),color_dict[label],2)
+        cv2.rectangle(img,(x,y-40),(x+w,y),color_dict[label],-1)
+        cv2.putText(img, labels_dict[label], (x, y-10),cv2.FONT_HERSHEY_SIMPLEX,0.8,(255,255,255),2)
+        
+        
+    cv2.imshow('LIVE',img)
+    key=cv2.waitKey(1)
+    
+    if(key==27):
+        break
+        
+cv2.destroyAllWindows()
+source.release()