Added to flask

app.py

+from flask import Flask, render_template, Response
+import cv2
+import numpy as np
+from tensorflow.keras.models import model_from_json  
+from tensorflow.keras.preprocessing import image  
+  
+
+#load model  
+model = model_from_json(open("model.json", "r").read())  
+
+#load weights  
+model.load_weights('model.h5')  
+
+
+face_haar_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')  
+
+
+app = Flask(__name__)
+
+camera = cv2.VideoCapture(0)
+
+def gen_frames():  # generate frame by frame from camera
+    while True:
+        # Capture frame by frame
+        success, frame = camera.read()
+        if not success:
+            break
+        else:
+            gray_img= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  
+        
+            faces_detected = face_haar_cascade.detectMultiScale(gray_img, 1.32, 5)  
+            
+        
+            for (x,y,w,h) in faces_detected:
+                print('WORKING')
+                cv2.rectangle(frame,(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  
+        
+                print(img_pixels.shape)
+                
+                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]  
+                print(predicted_emotion)
+                cv2.putText(frame, predicted_emotion, (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)  
+        
+            resized_img = cv2.resize(frame, (1000, 700))  
+            
+            ret, buffer = cv2.imencode('.jpg', frame)
+            
+            frame = buffer.tobytes()
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')  # concat frame one by one and show result
+
+
+@app.route('/video_feed')
+def video_feed():
+    #Video streaming route. Put this in the src attribute of an img tag
+    return Response(gen_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')
+
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+
+if __name__ == '__main__':
+    app.run(debug=True)