Upload app.py

app.py

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+import cv2
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+
+trained_model = tf.keras.models.load_model('facial_emotions_model.h5')
+classes=['Angry','Disgust','Fear','Happy','Neutral','Sad','Surprise']
+
+
+st.title('Emotion Detection')
+
+# run = st.checkbox('Run')
+
+frame_window = st.image([])
+frame_window2 = st.image([])
+
+cam = cv2.VideoCapture(0)
+
+# pred = 'Detecting'
+
+a = st.empty()
+face_cascade = cv2.CascadeClassifier('haarcascade.xml')
+# a.text_area(value='detecting', label='')
+while True:
+    ret, frame = cam.read()
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    frame_window2.image(frame)
+
+    for (x, y, w, h) in faces:
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+        frame = frame[y:y+h, x:x+h]
+
+
+
+
+
+    if frame.any():
+        resized = cv2.resize(frame, (48, 48))
+        resized_gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
+        reshaped_array = np.reshape(resized_gray, (1, 48, 48, 1))
+        result=trained_model.predict(reshaped_array)
+        y_pred=np.argmax(result[0])
+
+        pred = classes[y_pred]
+        # print('The person facial emotion is:',classes[y_pred])
+        a.write(pred)
+    # a = st.empty()
+    
+
+    frame_window.image(resized_gray)
+
+
+