### # - Author: Jaelin Lee, Abhishek Dutta # - Date: Mar 23, 2024 # - Description: Streamlit UI for mental health support chatbot using sentiment analsys, RL, BM25/ChromaDB, and LLM. # - Note: # - Updated to UI to show predicted mental health condition in behind the scence regardless of the ositive/negative sentiment ### from dotenv import load_dotenv, find_dotenv import pandas as pd import streamlit as st from q_learning_chatbot import QLearningChatbot from xgb_mental_health import MentalHealthClassifier from bm25_retreive_question import QuestionRetriever as QuestionRetriever_bm25 from Chromadb_storage_JyotiNigam import QuestionRetriever as QuestionRetriever_chromaDB from llm_response_generator import LLLResponseGenerator import os from llama_guard import moderate_chat, get_category_name from gtts import gTTS from io import BytesIO from streamlit_mic_recorder import speech_to_text import re # Streamlit UI st.title("MindfulMedia Mentor") # Define states and actions states = [ "Negative", "Moderately Negative", "Neutral", "Moderately Positive", "Positive", ] actions = ["encouragement", "empathy", "spiritual"] # Initialize Q-learning chatbot and mental health classifier chatbot = QLearningChatbot(states, actions) # Initialize MentalHealthClassifier # data_path = "/Users/jaelinlee/Documents/projects/fomo/input/data.csv" data_path = os.path.join("data", "data.csv") print(data_path) tokenizer_model_name = "nlptown/bert-base-multilingual-uncased-sentiment" mental_classifier_model_path = "mental_health_model.pkl" mental_classifier = MentalHealthClassifier(data_path, mental_classifier_model_path) # Function to display Q-table def display_q_table(q_values, states, actions): q_table_dict = {"State": states} for i, action in enumerate(actions): q_table_dict[action] = q_values[:, i] q_table_df = pd.DataFrame(q_table_dict) return q_table_df def text_to_speech(text): # Use gTTS to convert text to speech tts = gTTS(text=text, lang="en") # Save the speech as bytes in memory fp = BytesIO() tts.write_to_fp(fp) return fp def speech_recognition_callback(): # Ensure that speech output is available if st.session_state.my_stt_output is None: st.session_state.p01_error_message = "Please record your response again." return # Clear any previous error messages st.session_state.p01_error_message = None # Store the speech output in the session state st.session_state.speech_input = st.session_state.my_stt_output def remove_html_tags(text): clean_text = re.sub("<.*?>", "", text) return clean_text # Initialize memory if "entered_text" not in st.session_state: st.session_state.entered_text = [] if "entered_mood" not in st.session_state: st.session_state.entered_mood = [] if "messages" not in st.session_state: st.session_state.messages = [] if "user_sentiment" not in st.session_state: st.session_state.user_sentiment = "Neutral" if "mood_trend" not in st.session_state: st.session_state.mood_trend = "Unchanged" if "predicted_mental_category" not in st.session_state: st.session_state.predicted_mental_category = "" if "ai_tone" not in st.session_state: st.session_state.ai_tone = "Empathy" if "mood_trend_symbol" not in st.session_state: st.session_state.mood_trend_symbol = "" if "show_question" not in st.session_state: st.session_state.show_question = False if "asked_questions" not in st.session_state: st.session_state.asked_questions = [] # Check if 'llama_guard_enabled' is already in session state, otherwise initialize it if "llama_guard_enabled" not in st.session_state: st.session_state["llama_guard_enabled"] = False # Default value to False # Select Question Retriever selected_retriever_option = st.sidebar.selectbox( "Choose Question Retriever", ("BM25", "ChromaDB") ) if selected_retriever_option == "BM25": retriever = QuestionRetriever_bm25() if selected_retriever_option == "ChromaDB": retriever = QuestionRetriever_chromaDB() for message in st.session_state.messages: with st.chat_message(message.get("role")): st.write(message.get("content")) section_visible = True # Collect user input # Add a radio button to choose input mode input_mode = st.sidebar.radio("Select input mode:", ["Text", "Speech"]) user_message = None if input_mode == "Speech": # Use the speech_to_text function to capture speech input speech_input = speech_to_text(key="my_stt", callback=speech_recognition_callback) # Check if speech input is available if "speech_input" in st.session_state and st.session_state.speech_input: # Display the speech input # st.text(f"Speech Input: {st.session_state.speech_input}") # Process the speech input as a query user_message = st.session_state.speech_input st.session_state.speech_input = None else: user_message = st.chat_input("Type your message here:") # Modify the checkbox call to include a unique key parameter llama_guard_enabled = st.sidebar.checkbox( "Enable LlamaGuard", value=st.session_state["llama_guard_enabled"], key="llama_guard_toggle", ) # Update the session state based on the checkbox interaction st.session_state["llama_guard_enabled"] = llama_guard_enabled # Take user input if user_message: st.session_state.entered_text.append(user_message) st.session_state.messages.append({"role": "user", "content": user_message}) with st.chat_message("user"): st.write(user_message) is_safe = True if st.session_state["llama_guard_enabled"]: # guard_status = moderate_chat(user_prompt) guard_status, error = moderate_chat(user_message) if error: st.error(f"Failed to retrieve data from Llama Guard: {error}") else: if "unsafe" in guard_status[0]["generated_text"]: is_safe = False # added on March 24th unsafe_category_name = get_category_name( guard_status[0]["generated_text"] ) if is_safe == False: response = f"I see you are asking something about {unsafe_category_name} Due to eithical and safety reasons, I can't provide the help you need. Please reach out to someone who can, like a family member, friend, or therapist. In urgent situations, contact emergency services or a crisis hotline. Remember, asking for help is brave, and you're not alone." st.session_state.messages.append({"role": "ai", "content": response}) with st.chat_message("ai"): st.markdown(response) speech_fp = text_to_speech(response) # Play the speech st.audio(speech_fp, format="audio/mp3") else: # Detect mental condition with st.spinner("Processing..."): mental_classifier.initialize_tokenizer(tokenizer_model_name) mental_classifier.preprocess_data() predicted_mental_category = mental_classifier.predict_category(user_message) print("Predicted mental health condition:", predicted_mental_category) # Detect sentiment user_sentiment = chatbot.detect_sentiment(user_message) # Retrieve question if user_sentiment in ["Negative", "Moderately Negative", "Neutral"]: question = retriever.get_response( user_message, predicted_mental_category ) show_question = True else: show_question = False question = "" # predicted_mental_category = "" # Update mood history / mood_trend chatbot.update_mood_history() mood_trend = chatbot.check_mood_trend() # Define rewards if user_sentiment in ["Positive", "Moderately Positive"]: if mood_trend == "increased": reward = +1 mood_trend_symbol = " ⬆️" elif mood_trend == "unchanged": reward = +0.8 mood_trend_symbol = "" else: # decreased reward = -0.2 mood_trend_symbol = " ⬇️" else: if mood_trend == "increased": reward = +1 mood_trend_symbol = " ⬆️" elif mood_trend == "unchanged": reward = -0.2 mood_trend_symbol = "" else: # decreased reward = -1 mood_trend_symbol = " ⬇️" print( f"mood_trend - sentiment - reward: {mood_trend} - {user_sentiment} - 🛑{reward}🛑" ) # Update Q-values chatbot.update_q_values( user_sentiment, chatbot.actions[0], reward, user_sentiment ) # Get recommended action based on the updated Q-values ai_tone = chatbot.get_action(user_sentiment) print(ai_tone) print(st.session_state.messages) # LLM Response Generator load_dotenv(find_dotenv()) llm_model = LLLResponseGenerator() temperature = 0.5 max_length = 128 # Collect all messages exchanged so far into a single text string all_messages = "\n".join( [message.get("content") for message in st.session_state.messages] ) # Question asked to the user: {question} template = """INSTRUCTIONS: {context} Respond to the user with a tone of {ai_tone}. Response by the user: {user_text} Response; """ context = f"You are a mental health supporting non-medical assistant. Provide some advice and ask a relevant question back to the user. {all_messages}" llm_response = llm_model.llm_inference( model_type="huggingface", question=question, prompt_template=template, context=context, ai_tone=ai_tone, questionnaire=predicted_mental_category, user_text=user_message, temperature=temperature, max_length=max_length, ) llm_response = remove_html_tags(llm_response) if show_question: llm_reponse_with_quesiton = f"{llm_response}\n\n{question}" else: llm_reponse_with_quesiton = llm_response # Append the user and AI responses to the chat history st.session_state.messages.append( {"role": "ai", "content": llm_reponse_with_quesiton} ) with st.chat_message("ai"): st.markdown(llm_reponse_with_quesiton) # Convert the response to speech speech_fp = text_to_speech(llm_reponse_with_quesiton) # Play the speech st.audio(speech_fp, format="audio/mp3") # st.write(f"{llm_response}") # if show_question: # st.write(f"{question}") # else: # user doesn't feel negative. # get question to ecourage even more positive behaviour # Update data to memory st.session_state.user_sentiment = user_sentiment st.session_state.mood_trend = mood_trend st.session_state.predicted_mental_category = predicted_mental_category st.session_state.ai_tone = ai_tone st.session_state.mood_trend_symbol = mood_trend_symbol st.session_state.show_question = show_question # Show/hide "Behind the Scene" section # section_visible = st.sidebar.button('Show/Hide Behind the Scene') with st.sidebar.expander("Behind the Scene", expanded=section_visible): st.subheader("What AI is doing:") # Use the values stored in session state st.write( f"- Detected User Tone: {st.session_state.user_sentiment} ({st.session_state.mood_trend.capitalize()}{st.session_state.mood_trend_symbol})" ) # if st.session_state.show_question: st.write( f"- Possible Mental Condition: {st.session_state.predicted_mental_category.capitalize()}" ) st.write(f"- AI Tone: {st.session_state.ai_tone.capitalize()}") st.write(f"- Question retrieved from: {selected_retriever_option}") st.write( f"- If the user feels negative, moderately negative, or neutral, at the end of the AI response, it adds a mental health condition related question. The question is retrieved from DB. The categories of questions are limited to Depression, Anxiety, ADHD, Social Media Addiction, Social Isolation, and Cyberbullying which are most associated with FOMO related to excessive social media usage." ) st.write( f"- Below q-table is continuously updated after each interaction with the user. If the user's mood increases, AI gets a reward. Else, AI gets a punishment." ) # Display Q-table st.dataframe(display_q_table(chatbot.q_values, states, actions))