Spaces:
Build error
Build error
| # Library | |
| import openai | |
| import streamlit as st | |
| import pandas as pd | |
| from datetime import datetime | |
| from TTS.api import TTS | |
| import whisper | |
| from audio_recorder import record | |
| # Custom Streamlit app title and icon | |
| st.set_page_config( | |
| page_title="IELTS Speaking", | |
| page_icon=":robot_face:", | |
| ) | |
| # Set the title | |
| st.title("Part 1 Speaking") | |
| # Sidebar Configuration | |
| st.sidebar.title(":gear: Model Configuration") | |
| # Toggle for API activation | |
| api_toggle = st.sidebar.toggle("Activate free API") | |
| # Define an empty API key | |
| openai_key = "" | |
| # Check if the toggle is on | |
| if api_toggle: | |
| # If the toggle is on, access the API key from secrets | |
| openai_key = st.secrets["OPENAI_API_KEY"] | |
| openai.api_key = openai_key | |
| else: | |
| # If the toggle is off, allow the user to input the API key | |
| openai_key = st.sidebar.text_input('Your OpenAI API key here:', value="") | |
| openai.api_key = openai_key | |
| # User Input and AI Response | |
| user_input_type = st.sidebar.selectbox("Choose input type:", ["Chat", "Record Audio"]) | |
| # Model Name Selector | |
| model_name = st.sidebar.selectbox( | |
| "Select a Model", | |
| ["gpt-3.5-turbo", "gpt-4"], # Add more model names as needed | |
| key="model_name", | |
| ) | |
| # Temperature Slider | |
| temperature = st.sidebar.slider( | |
| ":thermometer: Temperature", | |
| min_value=0.2, | |
| max_value=2.0, | |
| value=1.0, | |
| step=0.1, | |
| key="temperature", | |
| ) | |
| # Max tokens Slider | |
| max_tokens = st.sidebar.slider( | |
| ":straight_ruler: Max Tokens", | |
| min_value=1, | |
| max_value=4095, | |
| value=256, | |
| step=1, | |
| key="max_tokens", | |
| ) | |
| # Top p Slider | |
| # top_p = st.sidebar.slider( | |
| # "🎯 Top P", | |
| # min_value=0.00, | |
| # max_value=1.00, | |
| # value=1.00, | |
| # step=0.01, | |
| # key="top_p", | |
| # ) | |
| # Presence penalty Slider | |
| # presence_penalty = st.sidebar.slider( | |
| # "🚫 Presence penalty", | |
| # min_value=0.00, | |
| # max_value=2.00, | |
| # value=0.00, | |
| # step=0.01, | |
| # key="presence_penalty", | |
| # ) | |
| # Frequency penalty Slider | |
| # frequency_penalty = st.sidebar.slider( | |
| # "🤐 Frequency penalty", | |
| # min_value=0.00, | |
| # max_value=2.00, | |
| # value=0.00, | |
| # step=0.01, | |
| # key="frequency_penalty", | |
| # ) | |
| # TEXT2SPEECH MODEL | |
| # Instantiate the TTS class | |
| tts = TTS(TTS().list_models()[13]) | |
| def convert_2_speech(given_text): | |
| tts.tts_to_file(text=given_text, file_path="response.wav") | |
| return("response.wav") | |
| # SPEECH2TEXT MODEL | |
| model_whisper = whisper.load_model("tiny.en") | |
| def convert_2_text(speech): | |
| user_message = model_whisper.transcribe(speech)["text"] | |
| return user_message | |
| # CHAT MODEL | |
| # Initialize DataFrame to store chat history | |
| chat_history_df = pd.DataFrame(columns=["Timestamp", "Chat"]) | |
| # Reset Button | |
| if st.sidebar.button(":arrows_counterclockwise: Reset Chat"): | |
| # Save the chat history to the DataFrame before clearing it | |
| if st.session_state.messages: | |
| timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") | |
| chat_history = "\n".join([f"{m['role']}: {m['content']}" for m in st.session_state.messages]) | |
| new_entry = pd.DataFrame({"Timestamp": [timestamp], "Chat": [chat_history]}) | |
| chat_history_df = pd.concat([chat_history_df, new_entry], ignore_index=True) | |
| # Save the DataFrame to a CSV file | |
| chat_history_df.to_csv("chat_history.csv", index=False) | |
| # Clear the chat messages and reset the full response | |
| st.session_state.messages = [] | |
| full_response = "" | |
| # Initialize Chat Messages | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| # Initialize full_response outside the user input check | |
| full_response = "" | |
| # Display Chat History | |
| for message in st.session_state.messages: | |
| if message["role"] != "system": | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| system_text="""As a helpful, thoughtful, and wise IELTS instructor responsible for testing Speaking Part 1. The users will provide the {subject} they want to talk about. | |
| It's important to follow these guidelines: | |
| - Give only original question for provided {subject}. | |
| - Give one question at a time. | |
| For example: | |
| {subject}: Work | |
| What is your job? | |
| Where do you work? | |
| {subject}: Study | |
| What do you study? | |
| Where do you study that? | |
| {subject}: Hometown | |
| Do you live in a house or a flat? | |
| How are the walls decorated? | |
| Let's start the test.""" | |
| # User Input and AI Response | |
| # For "Chat mode" | |
| # Use st.toggle to allow users to choose input type | |
| # record_audio_input = st.toggle("Record Audio Input", value=False) # for toggle only | |
| if user_input_type == "Chat": | |
| # if not record_audio_input: # for toggle only | |
| if prompt := st.chat_input("What is up?"): | |
| # System | |
| st.session_state.messages.append({"role": "system", "content": system_text}) | |
| # User | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Assistant | |
| with st.chat_message("assistant"): | |
| with st.status("Generating response..."): | |
| message_placeholder = st.empty() | |
| for response in openai.ChatCompletion.create( | |
| model=model_name, # Use the selected model name | |
| messages=[ | |
| {"role": m["role"], "content": m["content"]} | |
| for m in st.session_state.messages | |
| ], | |
| temperature=temperature, # Set temperature | |
| max_tokens=max_tokens, # Set max tokens | |
| # top_p=top_p, # Set top p | |
| # frequency_penalty=frequency_penalty, # Set frequency penalty | |
| # presence_penalty=presence_penalty, # Set presence penalty | |
| stream=True, | |
| ): | |
| full_response += response.choices[0].delta.get("content", "") | |
| message_placeholder.markdown(full_response + "▌") | |
| message_placeholder.markdown(full_response) | |
| st.session_state.messages.append({"role": "assistant", "content": full_response}) | |
| st.audio(convert_2_speech(full_response)) | |
| elif user_input_type == "Record Audio": | |
| # else: # for toggle only | |
| # Record audio when the "Record Audio" button is clicked | |
| if st.button("Record Audio"): | |
| st.write("Recording... Please speak for 10 seconds.") | |
| output = record(seconds=10, filename='my_recording.wav') | |
| st.write("Recording complete!") | |
| # Convert the recorded audio to text using the Whisper model | |
| user_message = convert_2_text(output) | |
| # Display the transcribed text as user input | |
| st.session_state.messages.append({"role": "user", "content": user_message}) | |
| with st.chat_message("user"): | |
| st.markdown(user_message) | |
| # Assistant | |
| with st.chat_message("assistant"): | |
| with st.status("Generating response..."): | |
| message_placeholder = st.empty() | |
| for response in openai.ChatCompletion.create( | |
| model=model_name, # Use the selected model name | |
| messages=[ | |
| {"role": m["role"], "content": m["content"]} | |
| for m in st.session_state.messages | |
| ], | |
| temperature=temperature, # Set temperature | |
| max_tokens=max_tokens, # Set max tokens | |
| # top_p=top_p, # Set top p | |
| # frequency_penalty=frequency_penalty, # Set frequency penalty | |
| # presence_penalty=presence_penalty, # Set presence penalty | |
| stream=True, | |
| ): | |
| full_response += response.choices[0].delta.get("content", "") | |
| message_placeholder.markdown(full_response + "▌") | |
| message_placeholder.markdown(full_response) | |
| st.session_state.messages.append({"role": "assistant", "content": full_response}) | |
| st.audio(convert_2_speech(full_response)) |