try to reduce lag

app.py

@@ -13,20 +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(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
+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 gray
 
 # Start the video capture and emotion detection
 # cap = cv2.VideoCapture(0)
@@ -49,7 +49,7 @@ output_image = gr.Image( type="pil", label="Detected Emotion" )
 
 
 iface = gr.Interface(
-	# fn = predict_emotion, 
+	fn = predict_emotion, 
 	inputs=input_image, 
 	outputs=output_image,
 	batch = True,