try to reduce lag

app.py

@@ -3,7 +3,6 @@ import gradio as gr
 import tensorflow as tf
 import cv2
 import numpy as np
-import time
 
 
 # Load the saved model
@@ -16,19 +15,18 @@ emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
 # Define the predict_emotion function
 def predict_emotion(frame):
 	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-	time.sleep(.01)
-	# 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
+	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)