Update app.py

app.py

@@ -1,20 +1,17 @@
-
 import streamlit as st
 import sounddevice as sd
 import numpy as np
 import wave
-import gradio as gr  
-import numpy as np      
-from audiorecorder import audiorecorder      
-import whisper      
-import os      
-import streamlit.components.v1 as components   
-import tempfile      
-import io      
-import requests    
-import json  
+import whisper
+import os
+import streamlit.components.v1 as components
+import tempfile
+import io
+import requests
+import json
 import openai
 
+# Define a function to split text into chunks
 def chunk_text(text, chunk_size=2000):
     chunks = []
     start = 0
@@ -46,10 +43,12 @@ if record_audio:
         if any(indata):
             audio_frames.append(indata.copy())
 
+    if st.button("Stop Recording"):  # Moved this button here to stop audio recording
+        sd.stop()
+
     with st.spinner("Recording..."):
         with sd.InputStream(callback=audio_callback):
             st.text("Recording audio. Click 'Stop Recording' when finished.")
-            st.button("Stop Recording")
 
     st.success("Recording stopped")
 
@@ -61,7 +60,8 @@ if record_audio:
             wf.setframerate(44100)
             wf.writeframes(audio_data.tobytes())
 
-if submit_button:
+# Moved the submit_button check here
+if 'submit_button' in st.session_state:
     model = whisper.load_model("base")
     audio_data = audio.export().read()
     audio_bytes_io = io.BytesIO(audio_data)
@@ -131,6 +131,5 @@ if userinput and api_key and st.button("Extract Claims", key="claims_extraction"
     # Display generated objectives for all chunks
     learning_status_placeholder.text(f"Patentable Claims Extracted!\n{all_extracted_claims.strip()}")
 
-
 # Citation
 st.markdown("<sub>This app was created by [Taylor Ennen](https://github.com/taylor-ennen/GPT-Streamlit-MVP) & [Tonic](https://huggingface.co/tonic)</sub>", unsafe_allow_html=True)