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app.py

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+import streamlit as st
+import pandas as pd
+import openai
+from text_speech_utils import *  # Assuming this module exists for your audio functionality
+
+# Define filenames for audio and conversation output
+input_audio_filename = 'input.wav'
+output_audio_filename = 'chatgpt_response.wav'
+output_conversation_filename = 'ChatGPT_conversation.txt'
+
+# Initialize app
+if 'messages' not in st.session_state:
+    st.session_state['messages'] = [{"role": "system", "content": "You are a helpful assistant."}]
+
+# Allow user to input OpenAI API Key via Streamlit text input
+openai.api_key = st.text_input("Enter your OpenAI API Key", type="password")
+
+# Display a warning if API key is not provided
+if not openai.api_key:
+    st.warning("Please enter your OpenAI API key to proceed.")
+
+# UI components
+st.title("My awesome personal assistant")
+sec = st.slider("Select number of seconds of recording", min_value=2, max_value=8, value=5)
+
+# Record audio + transcribe with Whisper + get GPT-3 response
+if st.button('Record audio'):
+    if openai.api_key:  # Proceed only if API key is provided
+        st.write("Recording...")
+        record_audio(input_audio_filename, sec)
+
+        transcription = transcribe_audio(input_audio_filename)
+        st.write(f"Me: {transcription['text']}")
+        st.session_state['messages'].append({"role": "user", "content": transcription['text']})
+
+        bot = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=st.session_state['messages'])
+        response = bot.choices[0].message.content
+        st.write(f"GPT: {response}")
+
+        save_text_as_audio(response, output_audio_filename)
+        play_audio(output_audio_filename)
+
+        st.session_state['messages'].append({"role": "assistant", "content": response})
+    else:
+        st.error("API key is required to interact with GPT.")
+
+# # Download conversation button
+# st.download_button(label="Download conversation", 
+#                    data=pd.DataFrame(st.session_state['messages']).to_csv(index=False).encode('utf-8'), 
+#                    file_name=output_conversation_filename)
+
+# Function to generate conversation as plain text
+def generate_conversation_text(messages):
+    conversation_text = ""
+    for message in messages:
+        if message["role"] == "user":
+            conversation_text += f"Me: {message['content']}\n"
+        elif message["role"] == "assistant":
+            conversation_text += f"GPT: {message['content']}\n"
+        elif message["role"] == "system":
+            conversation_text += f"System: {message['content']}\n"
+    return conversation_text
+
+# Download conversation button
+st.download_button(
+    label="Download conversation", 
+    data=generate_conversation_text(st.session_state['messages']).encode('utf-8'), 
+    file_name=output_conversation_filename,
+    mime="text/plain"
+)

text_speech_utils.py

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+import openai
+import sounddevice as sd
+import audiofile as af
+from scipy.io.wavfile import write
+from gtts import gTTS
+
+import multiprocessing
+import pyttsx3
+import keyboard
+
+def say(text):
+		p = multiprocessing.Process(target=pyttsx3.speak, args=(text,))
+		p.start()
+		while p.is_alive():
+			if keyboard.is_pressed('enter'):
+				p.terminate()
+			else:
+				continue
+		p.join()
+
+
+def record_audio(filename, sec, sr = 44100):
+    audio = sd.rec(int(sec * sr), samplerate=sr, channels=2, blocking=False)
+    sd.wait()
+    write(filename, sr, audio)
+
+def record_audio_manual(filename, sr = 44100):
+    input("  ** Press enter to start recording **")
+    audio = sd.rec(int(10 * sr), samplerate=sr, channels=2)
+    input("  ** Press enter to stop recording **")
+    sd.stop()
+    write(filename, sr, audio)
+
+def play_audio(filename):
+    signal, sr = af.read(filename)
+    sd.play(signal, sr)
+
+def transcribe_audio(filename):
+    audio_file= open(filename, "rb")
+    transcript = openai.Audio.transcribe("whisper-1", audio_file)
+    audio_file.close()
+    return transcript
+
+def translate_audio(filename):
+    audio_file= open(filename, "rb")
+    translation = openai.Audio.translate("whisper-1", audio_file)
+    audio_file.close()
+    return translation
+
+def save_text_as_audio(text, audio_filename):
+    myobj = gTTS(text=text, lang='en', slow=False)  
+    myobj.save(audio_filename)