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 import QuestionRetriever as QuestionRetriever_chromaDB from llm_response_generator import LLLResponseGenerator import os # Streamlit UI st.title("FOMO Fix - RL-based Mental Health Assistant") # 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 = "data/data.csv" tokenizer_model_name = "nlptown/bert-base-multilingual-uncased-sentiment" mental_classifier_model_path = "app/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 # 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 = [] # 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")) # Collect user input user_message = st.chat_input("Type your message here:") # 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) # 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"]: question = retriever.get_response(user_message, predicted_mental_category) show_question = True else: show_question = False question = "" predicted_mental_category = "" # Update mood history / moode_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: # decresed 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}🛑 -- (a)" ) # 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) # -------------- # LLM Response Generator HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN') llm_model = LLLResponseGenerator() temperature = 0.1 max_length = 128 template = """INSTRUCTIONS: {context} Respond to the user with a tone of {ai_tone}. Question asked to the user: {question} Response by the user: {user_text} Response; """ context = "You are a mental health supporting non-medical assistant. Provide some advice and ask a relevant question back to the user." 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, ) st.session_state.messages.append({"role": "ai", "content": llm_response}) with st.chat_message("ai"): st.markdown(llm_response) # 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 st.subheader("Behind the Scence - What AI is doing:") st.write( f"- Detected User Tone: {user_sentiment} ({mood_trend.capitalize()}{mood_trend_symbol})" ) if show_question: st.write( f"- Possible Mental Condition: {predicted_mental_category.capitalize()}" ) st.write(f"- AI Tone: {ai_tone.capitalize()}") st.write(f"- Question retrieved from: {selected_retriever_option}") st.write( f"- If the user feels neagative or moderately negative, at the end of the AI response, it adds a mental health condition realted question. The question is retrieved from DB. The categories of questions are limited to Depression, Anxiety, and ADHD which are most associated with FOMO related to excessive social media usage." ) st.write( f"- Below q-table is continously updated after each interaction with the user. If the user's mood increases, AI gets reward. Else, AI gets punishment." ) # Display results # st.subheader(f"{user_sentiment.capitalize()}") # st.write("->" + f"{ai_tone.capitalize()}") # st.write(f"Mood {chatbot.check_mood_trend()}") # st.write(f"{ai_tone.capitalize()}, {chatbot.check_mood_trend()}") # Display Q-table st.dataframe(display_q_table(chatbot.q_values, states, actions)) # Display mood history # st.subheader("Mood History (Recent 5):") # for mood_now in reversed(chatbot.mood_history[-5:]): #st.session_state.entered_mood[-5:], chatbot.mood_history[-5:]): #st.session_state.entered_text[-5:] # st.write(f"{mood_now}")