Update app.py

app.py

@@ -1,70 +1,48 @@
-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"
-)
+import streamlit as st
+from tempfile import NamedTemporaryFile
+from audiorecorder import audiorecorder
+from whispercpp import Whisper
+
+# Download whisper.cpp
+w = Whisper('tiny')
+
+def inference(audio):
+    # Save audio to a file:
+    with NamedTemporaryFile(suffix=".mp3") as temp:
+        with open(f"{temp.name}", "wb") as f:
+            f.write(audio.tobytes())
+        result = w.transcribe(f"{temp.name}")
+        text = w.extract_text(result)
+    return text[0]
+
+# Streamlit
+with st.sidebar:
+    audio = audiorecorder("Click to send voice message", "Recording... Click when you're done", key="recorder")
+    st.title("Echo Bot with Whisper")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# React to user input
+if (prompt := st.chat_input("Your message")) or len(audio):
+    # If it's coming from the audio recorder transcribe the message with whisper.cpp
+    if len(audio)>0:
+        prompt = inference(audio)
+
+    # Display user message in chat message container
+    st.chat_message("user").markdown(prompt)
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    response = f"Echo: {prompt}"
+    # Display assistant response in chat message container
+    with st.chat_message("assistant"):
+        st.markdown(response)
+    # Add assistant response to chat history
+    st.session_state.messages.append({"role": "assistant", "content": response})