test live detection

app.py

@@ -13,23 +13,20 @@ face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_fronta
 emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
 
 # Define the predict_emotion function
-def predict_emotion(vid):
-	cap = cv2.VideoCapture(vid)
-	# while True:
-	ret, frame = cap.read()
-	if ret:
-	    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-	    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
-	    for (x, y, w, h) in faces:
-	        face = gray[y:y+h, x:x+w]
-	        face = cv2.resize(face, (48, 48))
-	        face = np.expand_dims(face, axis=-1)
-	        face = np.expand_dims(face, axis=0)
-	        prediction = model.predict(face)
-	        emotion = emotions[np.argmax(prediction)]
-	        cv2.putText(frame, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-	        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
-	    return frame
+def predict_emotion(frame):
+	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+	faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+	for (x, y, w, h) in faces:
+		face = gray[y:y+h, x:x+w]
+		face = cv2.resize(face, (48, 48))
+		face = np.expand_dims(face, axis=-1)
+		face = np.expand_dims(face, axis=0)
+		prediction = model.predict(face)
+		emotion = emotions[np.argmax(prediction)]
+		cv2.putText(frame, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+		cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+
+	return frame
 
 # Start the video capture and emotion detection
 # cap = cv2.VideoCapture(0)