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import os
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
from llm_response_generator import LLLResponseGenerator

# 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']

# Initialize Q-learning chatbot and mental health classifier
chatbot = QLearningChatbot(states, actions)

# Initialize MentalHealthClassifier
data_path = "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)


# 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 = []

# Collect user input
user_message = st.text_input("Type your message here:")

# Take user input
if user_message:
    st.session_state.entered_text.append(user_message)

    # Detect mental condition
    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)
    # st.subheader("🛑 " + f"{predicted_mental_category.capitalize()}")

    # Retrieve question
    retriever = QuestionRetriever()
    question = retriever.get_response(user_message, predicted_mental_category)
    # st.write(question)

    # Detect sentiment
    user_sentiment = chatbot.detect_sentiment(user_message)
    # 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 = +0.8
        else: # decresed
            reward = -0.3

    else:
        if mood_trend == "increased":
            reward = +1
        else:
            reward = -1

    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.write(f"{llm_response}")
    st.write(f"{question}")

    st.subheader("Behind the Scence - What AI is doing:")
    st.write(f"- User Tone: {user_sentiment}, Possibly {predicted_mental_category.capitalize()}")
    st.write(f"- AI Tone: {ai_tone.capitalize()}")
    # st.write(f"Question: {question}")


    # 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}")