app.py

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))