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 import re # 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 = "mental_health_model.pkl" mental_classifier = MentalHealthClassifier(data_path, mental_classifier_model_path) if not os.path.exists(mental_classifier_model_path): mental_classifier.initialize_tokenizer(tokenizer_model_name) X, y = mental_classifier.preprocess_data() y_test, y_pred = mental_classifier.train_model(X, y) mental_classifier.save_model() else: mental_classifier.load_model() mental_classifier.initialize_tokenizer(tokenizer_model_name) # Ensure tokenizer is initialized if loading model from pickle # X, y = mental_classifier.preprocess_data() # Preprocess data again if needed # mental_classifier.model.fit(X, y) # Fit the loaded model to the data # 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 remove_html_tags(text): # clean_text = re.sub("<.*?>", "", text) clean_text = re.sub(r'<.*?>|- |"|\\n', '', text) # Remove indentation clean_text = clean_text.strip() # Remove new lines clean_text = clean_text.replace('\n', ' ') 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 = [] # 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 = False # 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", "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 HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN') 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[-3:-1]]) #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 brief advice. DO NOT ASK ANY QUESTION. DO NOT REPEAT YOURSELF. {all_messages}" # 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, ) 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) # 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, and ADHD 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))