Create app.py

app.py

@@ -0,0 +1,50 @@
+import os
+
+import pyaudio
+import streamlit as st
+from langchain.memory import ConversationBufferMemory
+
+from utils import record_audio_chunk, transcribe_audio, get_response_llm, play_text_to_speech, load_whisper
+
+chunk_file = 'temp_audio_chunk.wav'
+model = load_whisper()
+def main():
+    st.markdown('<h1 style="color: darkblue;">AI Voice Assistant️</h1>', unsafe_allow_html=True)
+
+    memory = ConversationBufferMemory(memory_key="chat_history")
+
+    if st.button("Start Recording"):
+        while True:
+            # Audio Stream Initialization
+            audio = pyaudio.PyAudio()
+            stream = audio.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=1024)
+
+            # Record and save audio chunk
+            record_audio_chunk(audio, stream)
+
+            text = transcribe_audio(model, chunk_file)
+
+            if text is not None:
+                st.markdown(
+                    f'<div style="background-color: #f0f0f0; padding: 10px; border-radius: 5px;">Customer 👤: {text}</div>',
+                    unsafe_allow_html=True)
+
+                os.remove(chunk_file)
+
+                response_llm = get_response_llm(user_question=text, memory=memory)
+                st.markdown(
+                    f'<div style="background-color: #f0f0f0; padding: 10px; border-radius: 5px;">AI Assistant 🤖: {response_llm}</div>',
+                    unsafe_allow_html=True)
+
+                play_text_to_speech(text=response_llm)
+            else:
+                stream.stop_stream()
+                stream.close()
+                audio.terminate()
+                break  # Exit the while loop
+        print("End Conversation")
+
+
+
+if __name__ == "__main__":
+    main()