This is web_app.py file

web_app.py

+import mediapipe as mp
+import requests
+import json
+from tensorflow.keras.models import load_model
+import numpy as np
+import cv2
+
+data = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}
+model = load_model('emotion/model.h5')
+
+
+def pre_processing(img):
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img = cv2.equalizeHist(img)
+    img = img / 255
+    return img
+
+
+def emo_rec(img):
+    img = np.asarray(img)
+    img = cv2.resize(img, (48, 48))
+    img = pre_processing(img)
+    img = img.reshape(1, 48, 48, 1)
+    predict_x = model.predict(img)
+    classes_x = np.argmax(predict_x, axis=1)
+    return data[classes_x[0]], str(classes_x[0])
+
+
+def send_data_to_api(emotion):
+    url = "http://127.0.0.1:5000/from_web_app"
+    data = {'emotion': emotion
+            }
+
+    # sending post request and saving response as response object
+    headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
+    r = requests.post(url, data=json.dumps(data), headers=headers)
+    print('pushed')
+
+
+def web_camera():
+    cap = cv2.VideoCapture(0)
+    while cap.isOpened():
+        ret, frame = cap.read()
+
+        # BGR 2 RGB
+        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        cv2.imshow('Hand Tracking', image)
+
+        gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        haar_cascade = cv2.CascadeClassifier('Haarcascade_frontalface_default.xml')
+        faces_rect = haar_cascade.detectMultiScale(gray_img, 1.1, 9)
+
+        if len(faces_rect) > 0:
+            for (x, y, w, h) in faces_rect:
+                crop_img = image[y:y + h, x:x + w]
+                emo, emo_index = emo_rec(crop_img)
+                # send_data_to_api(emo_index)
+                cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
+                cv2.putText(image, emo, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+        if cv2.waitKey(10) & 0xFF == ord('q'):
+            break
+        resized_img = cv2.resize(image, (800, 600))
+        ret, buffer = cv2.imencode('.jpg', resized_img)
+        frame = buffer.tobytes()
+        yield (b'--frame\r\n'
+               b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')