Update app.py

app.py

@@ -458,9 +458,9 @@ def main():
             st.write(f"You can ask up to {remaining} more question(s).")
 
             st.write("### Record Your Follow-Up Question:")
-            # Use streamlit-webrtc
             from streamlit_webrtc import webrtc_streamer, WebRtcMode, RTCConfiguration
             from qa import AudioBufferProcessor
+
             RTC_CONFIGURATION = {"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]}
 
             webrtc_ctx = webrtc_streamer(
@@ -474,18 +474,19 @@ def main():
             if "audio-processor" not in st.session_state:
                 st.session_state["audio-processor"] = None
 
+            # If the stream is currently playing, store the processor
             if webrtc_ctx.state.playing and webrtc_ctx.audio_processor:
                 st.session_state["audio-processor"] = webrtc_ctx.audio_processor
 
-            # Once the user clicks "Stop", we can finalize the frames
-            if webrtc_ctx.state.status == webrtc_ctx.state.STATUS.DISCONNECTED:
+            # Instead of checking .state.status, we check .state.playing
+            if not webrtc_ctx.state.playing:
                 st.write("Recording Stopped. You may now submit your question.")
 
+            # Submit button
             if st.button("Submit Q&A"):
                 if used_questions >= MAX_QA_QUESTIONS:
                     st.warning("You have reached the Q&A limit.")
                 else:
-                    # 1) Finalize WAV
                     processor = st.session_state.get("audio-processor")
                     if not processor or not getattr(processor, "frames", None):
                         st.warning("No recorded audio found. Please record your question first.")
@@ -494,7 +495,6 @@ def main():
                         if not local_wav_path:
                             st.warning("No audio frames found. Please record again.")
                         else:
-                            # 2) Transcribe with Deepgram (same logic as your old approach)
                             from qa import transcribe_audio_deepgram
                             st.write("Transcribing your voice question via Deepgram...")
                             question_text = transcribe_audio_deepgram(local_wav_path)
@@ -503,7 +503,6 @@ def main():
                             else:
                                 st.write(f"**You asked**: {question_text}")
 
-                                # 3) Generate an LLM answer
                                 conversation_so_far = st.session_state["conversation_history"]
                                 ans_audio, ans_text = handle_qa_exchange(conversation_so_far, question_text)
                                 if ans_audio: