Update app.py

app.py

@@ -4,7 +4,7 @@ import streamlit as st
 import cv2
 import av
 import mediapipe as mp
-from streamlit_webrtc import webrtc_streamer, RTCConfiguration
+from streamlit_webrtc import webrtc_streamer, VideoTransformerBase, RTCConfiguration, VideoProcessorBase, WebRtcMode
 
 
 # Initializing the Model
@@ -26,8 +26,8 @@ label = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',
          'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']
 
 # cap = cv2.VideoCapture(0)
-class signDetection:
-    def recv(self, frame):
+class signDetection(VideoTransformerBase):
+    def transfrom(self, frame):
         img = frame.to_ndarray(format="bgr24")
         imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
         lmsList = []
@@ -45,14 +45,15 @@ class signDetection:
             r = model.predict(lmsList)
             r = np.argmax(r)
             cv2.putText(img, f'Result = {label[r]}', (50, 40), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 255), 2)
-        return av.VideoFrame.from_ndarray(img, format='bgr24')
+        # return av.VideoFrame.from_ndarray(img, format='bgr24')
+        return img
 
 def main():
     st.title('Real Time Sign Language Detection')
 
     st.header('Webcam Live Feed')
     st.write("Click on start to use webcam and detect finger spellings")
-    webrtc_streamer(key='key', rtc_configuration=RTC_CONFIGURATION) #, video_processor_factory=signDetection)
+    webrtc_streamer(key='key', mode=WebRtcMode.SENDRECV, rtc_configuration=RTC_CONFIGURATION, video_processor_factory=signDetection)
 
     with st.sidebar:
         st.header('Finger Spellings')