app.py

import sys
import os
from datetime import datetime
import json
import uuid
from pathlib import Path
from huggingface_hub import CommitScheduler
import gradio as gr
import markdown
from together import Together

ROOT_FILE = os.path.join(os.path.dirname(os.path.abspath(__file__)), "./")
sys.path.append(ROOT_FILE)
from components.induce_personality import construct_big_five_words
from components.chat_conversation import (
    # format_message_history,
    format_user_message,
    format_context,
    gradio_to_huggingface_message,
    huggingface_to_gradio_message,
    # get_system_instruction,
    prepare_tokenizer,
    # format_rag_context,
    conversation_window,
    generate_response_local_api,
    generate_response_together_api,
    generate_response_debugging,
)
from components.constant import (
    CONV_WINDOW,
    API_URL,
)
from components.induce_personality import (
    build_personality_prompt,
)

LOG_DIR = os.path.join(ROOT_FILE, "log/api/")
if os.path.exists(LOG_DIR) is False:
    os.makedirs(LOG_DIR)

# Load Static Files
STATIC_FILE = os.path.join(ROOT_FILE, "_static")
LOG_DIR = os.path.join(ROOT_FILE, "log/test_session/")
INSTRUCTION_PAGE_FILE = os.path.join(STATIC_FILE, "html/instruction_page.html")
USER_NARRATIVE_FILE = os.path.join(STATIC_FILE, "html/user_narrative.html")
PREFERENCE_ELICITATION_TASK_FILE = os.path.join(STATIC_FILE, "html/system_instruction_preference_elicitation.html")
EVALUATION_INSTRUCTION_FILE = os.path.join(STATIC_FILE, "html/evaluation_instruction.html")
GENERAL_INSTRUCTION_FILE = os.path.join(STATIC_FILE, "html/general_instruction.html")
FINAL_EVALUATION_FILE = os.path.join(STATIC_FILE, "html/final_evaluation.html")
SYSTEM_INSTRUCTION_FILE = os.path.join(STATIC_FILE, "txt/system_instruction_with_user_persona.txt")
SYSTEM_INSTRUECTION_PREFERENCE_ELICITATION_FILE = os.path.join(
    STATIC_FILE, "txt/system_instruction_preference_elicitation.txt"
)
SUMMARIZATION_PROMPT_FILE = os.path.join(STATIC_FILE, "txt/system_summarization_user_preference_elicitation.txt")

uuid_this_session = str(uuid.uuid4())
feedback_file_stock_familiarity = Path("user_feedback/") / f"stock_familiarity_{uuid_this_session}.json"
feedback_file_interaction = Path("user_feedback/") / f"interaction_{uuid_this_session}.json"
feedback_file_summarization = Path("user_feedback/") / f"summarization_{uuid_this_session}.json"
feedback_file_round_evaluation = Path("user_feedback/") / f"round_evaluation_{uuid_this_session}.json"
feedback_file_final_ranking = Path("user_feedback/") / f"final_ranking_{uuid_this_session}.json"
feedback_file_final_survey = Path("user_feedback/") / f"final_survey_{uuid_this_session}.json"
feedback_folder = feedback_file_interaction.parent
feedback_folder.mkdir(parents=True, exist_ok=True)  # Ensure the directory exists

scheduler = CommitScheduler(
    repo_id="logging_test_space",
    repo_type="dataset",
    folder_path=feedback_folder,
    path_in_repo="data",
    token=os.getenv("HUGGINGFACE_HUB_TOKEN"),
    every=1,
)


# Function to save user feedback
def save_feedback(user_id: str, uuid: str, type: str, value, feedback_file) -> None:
    """
    Append input/outputs and user feedback to a JSON Lines file using a thread lock to avoid concurrent writes from different users.
    """
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    with scheduler.lock:
        with feedback_file.open("a") as f:
            f.write(
                json.dumps({"user_id": user_id, "uuid": uuid, "timestamp": timestamp, "type": type, "value": value})
            )
            f.write("\n")


# Load the required static content from files
def load_static_content(file_path):
    with open(file_path, "r") as f:
        return f.read()


def ensure_directory_exists(directory_path):
    """Ensures the given directory exists; creates it if it does not."""
    if not os.path.exists(directory_path):
        os.makedirs(directory_path)


INSTRUCTION_PAGE = load_static_content(INSTRUCTION_PAGE_FILE)
EVALUATION_INSTRUCTION = load_static_content(EVALUATION_INSTRUCTION_FILE)
GENERAL_INSTRUCTION = load_static_content(GENERAL_INSTRUCTION_FILE)
USER_NARRATIVE = load_static_content(USER_NARRATIVE_FILE)
PREFERENCE_ELICITATION_TASK = load_static_content(PREFERENCE_ELICITATION_TASK_FILE)
FINAL_EVALUATION = load_static_content(FINAL_EVALUATION_FILE)
SYSTEM_INSTRUCTION = load_static_content(SYSTEM_INSTRUCTION_FILE)
SYSTEM_INSTRUECTION_PREFERENCE_ELICITATION = load_static_content(SYSTEM_INSTRUECTION_PREFERENCE_ELICITATION_FILE)
SUMMARIZATION_PROMPT = load_static_content(SUMMARIZATION_PROMPT_FILE)

# Other constants
FIRST_MESSAGE = "Hey"
INFORMATION_SEEKING = True
USER_PREFERENCE_SUMMARY = True
DEBUG = False
API_TYPE = "together"
assert API_TYPE in ["together", "local", "debug"], "The API should be either 'together' or 'local'"
if API_TYPE == "together":
    TOGETHER_CLIENT = Together(api_key=os.getenv("TOGETHER_API_KEY"))

SESSION_DEBUG = True


def get_context_list(synthetic_data_path):
    # Load data from the synthetic data file
    with open(synthetic_data_path, "r") as f:
        data = [json.loads(line) for line in f]

    return data


def add_ticker_prefix(ticker_list, context_list):
    res = []
    for ticker, context in zip(ticker_list, context_list):
        res.append(f"{ticker}: {context}")
    return res


def build_raw_context_list(context_dict):
    return context_dict["data"]


def build_context(context_dict):
    return [build_context_element(context) for context in context_dict["data"]]


def build_context_element(context):
    # [{topic: ex, data: {}}, {..}, ..]
    # Extract information from the context
    ticker = context["ticker"]
    sector = context["sector"]
    business_summary = context["business_summary"]
    name = context["short_name"]
    stock_price = context["price_data"]
    earning = context["earning_summary"]
    beta = context["beta"]

    # Build the context string
    stock_candidate = f"Stock Candidate: {name}"
    stock_info = f"Stock Information: \nIndustry - {sector}, \nBeta (risk indicator) - {beta}, \nEarning Summary - {earning}\n, 2023 Monthly Stock Price - {stock_price}\n, Business Summary - {business_summary}"

    context_list = [stock_candidate, stock_info]

    # Combine all parts into a single string
    return "\n".join(context_list)


def get_user_narrative_html(user_narrative):
    return USER_NARRATIVE.replace("{user_narrative}", user_narrative).replace("\n", "<br>")


def get_user_narrative_from_raw(raw_narrative):
    return get_user_narrative_html(markdown.markdown(raw_narrative.replace("\n", "<br>")))


def get_task_instruction_for_user(context):
    ticker_name = context["short_name"]
    user_narrative = context["user_narrative"]
    user_narrative = user_narrative.replace("\n", "<br>")
    html_user_narrative = markdown.markdown(user_narrative)
    general_instruction = GENERAL_INSTRUCTION
    stock_instruction = f"""
<div style="background-color: #f9f9f9; padding: 20px; border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); margin-bottom: 20px; max-height: 780px; overflow-y: auto; overflow-x: hidden;">
    <!-- Stock Information (Bold label, Normal ticker name) -->
    <h4 style="color: #2c3e50; text-align: center; margin-bottom: 20px; font-size: 20px; font-weight: 600;">
        Stock Information
    </h4>
    <div style="text-align: left; font-weight: normal; font-size: 16px; margin-bottom: 20px;">
        <span style="font-weight: bold;">This Round's Stock:</span>
        {ticker_name}
    </div>
</div>"""
    user_narrative_instruction = f"""
<div style="background-color: #f9f9f9; padding: 20px; border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); margin-bottom: 20px; max-height: 780px; overflow-y: auto; overflow-x: hidden;">
    <!-- User Narrative (Bold label, Normal narrative) -->
    <h4 style="color: #2c3e50; text-align: center; margin-bottom: 20px; font-size: 20px; font-weight: 600;">
        User Narrative
    </h4>
    <div style="text-align: left; font-weight: normal; font-size: 16px; margin-bottom: 20px;">
        {html_user_narrative}
    </div>
</div>"""

    return general_instruction, stock_instruction, user_narrative_instruction


def display_system_instruction_with_html(
    system_instruction,
):
    html_system_instruction = f"""
        <p style="text-align: left; margin-bottom: 10px;">
            {system_instruction}
        </p>
    """
    return html_system_instruction


def log_action(user_id, tab_name, action, details):
    """
    Log actions for each tab (stock).
    """
    log_file_dir = os.path.join(LOG_DIR, f"{user_id}")
    if os.path.exists(log_file_dir) is False:
        os.makedirs(log_file_dir)
    log_file = os.path.join(log_file_dir, f"{tab_name}.txt")
    print(log_file)
    with open(log_file, "a") as f:
        f.write(f"Action: {action} | Details: {details}\n")


def add_user_profile_to_system_instruction(
    user_id, system_instruction, user_preference_elicitation_data, summary, terminator
):
    if summary:
        if user_preference_elicitation_data["summary_history"] == "":
            # Format prompt
            summarization_prompt = SUMMARIZATION_PROMPT + "\nPrevious Conversations: {}".format(
                user_preference_elicitation_data["history"]
            )
            summarization_instruction = [{"role": "system", "content": summarization_prompt}]
            if API_TYPE == "local":
                summ, _ = generate_response_local_api(summarization_instruction, terminator, 512, API_URL)
            elif API_TYPE == "together":
                summ, _ = generate_response_together_api(summarization_instruction, 512, TOGETHER_CLIENT)
            else:
                summ, _ = generate_response_debugging(summarization_instruction)
            user_preference_elicitation_data["summary_history"] = summ
            # log_action(user_id, "Prompt", "Preference Elicitation Summarization", summ)
            save_feedback(
                user_id,
                uuid_this_session,
                "preference_elicitation_summarization",
                {"summarization": summ},
                feedback_file_summarization,
            )
            # print(f"Preference Summary:{summ}")
        system_instruction += f"\nUser Profile collected in the previous conversations: {user_preference_elicitation_data['summary_history']}\n"
    else:
        system_instruction += (
            f"\nUser Profile collected in the previous conversations: {user_preference_elicitation_data['history']}\n"
        )
    return system_instruction


def likert_evaluation(content):
    return gr.Radio(
        [1, 2, 3, 4, 5, 6, 7],
        label=f"{content}",
        show_label=True,
    )


def create_demo():
    global personality_prompts, context_info_list, terminator

    def tab_creation_exploration_stage(order, comp, context):
        english_order = ["1", "2", "3", "4", "5"]
        with gr.Tab(f"{english_order[order]}-1:Discuss"):
            general_instruction = gr.HTML(label="General Instruction")
            with gr.Row():
                with gr.Column():
                    stock_instruction = gr.HTML(label="Candidate Stock")
                with gr.Column():
                    stock_familiarity = gr.Radio(
                        [1, 2, 3, 4, 5, 6, 7],
                        label="What is your level of familiarity with the candidate stock? (1 = Not Familiar, 7 = Very Familiar)",
                    )

            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        stock_familiarity_explain = gr.HTML(
                            """<div style="background-color: #f8d7da; color: #721c24; padding: 15px; border: 1px solid #f5c6cb; border-radius: 5px; margin-bottom: 20px;">
                    Please select your familiarity with the stock
                    </div>"""
                        )
                with gr.Column():
                    with gr.Row():
                        stock_familiarity_button = gr.Button(value="Submit Stock Familiarity")
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        user_narrative_instruction = gr.HTML(label="User Narrative")
                with gr.Column():
                    with gr.Row():
                        chatbot = gr.Chatbot(height=600)
                    with gr.Row():
                        start_conversation = gr.Button(value="Start Conversation", interactive=False)
                    with gr.Row():
                        msg = gr.Textbox(scale=1, label="User Input")
                    with gr.Row():
                        msg_button = gr.Button(value="Send This Message to Advisor", interactive=False)
                        continue_button = gr.Button(value="Show More of the Advisor’s Answer", interactive=False)
            with gr.Row():
                clear = gr.ClearButton([msg, chatbot])
        with gr.Tab(f"{english_order[order]}-2:Eval"):
            with gr.Row():
                gr.HTML(value=EVALUATION_INSTRUCTION)
            with gr.Row():
                dropdown = gr.Dropdown(
                    label="Would you like to purchase the stock?",
                    choices=["Yes", "No"],
                    show_label=True,
                )
                reason = gr.Textbox(
                    scale=1,
                    label="Reason for Your Choice (Explain Your Reasoning & Highlight Useful Parts of Conversation)[Optional]",
                    lines=5,
                )
            with gr.Row():
                likelihood = gr.Radio(
                    [1, 2, 3, 4, 5, 6, 7],
                    label="I am likely to purchase the stock (1 = Strongly Disagree, 7 = Strongly Agree)",
                    show_label=True,
                )
                confidence = gr.Radio(
                    [1, 2, 3, 4, 5, 6, 7],
                    label="I am confident in my decision (1 = Strongly Disagree, 7 = Strongly Agree)",
                    show_label=True,
                )
            with gr.Row():
                textbox = gr.HTML()
                evaluation_send_button = gr.Button(value="Send: Evaluation")
        return {
            "comp": comp,
            "system_instruction_context": context,
            "start_conversation": start_conversation,
            "msg_button": msg_button,
            "continue_button": continue_button,
            "chatbot": chatbot,
            "msg": msg,
            "dropdown": dropdown,
            "reason": reason,
            "likelihood": likelihood,
            "confidence": confidence,
            "evaluation_send_button": evaluation_send_button,
            "general_instruction": general_instruction,
            "stock_instruction": stock_instruction,
            "stock_familiarity_explain": stock_familiarity_explain,
            "stock_familiarity_button": stock_familiarity_button,
            "stock_familiarity": stock_familiarity,
            "user_narrative_instruction": user_narrative_instruction,
            "textbox": textbox,
        }

    def tab_creation_preference_stage():
        with gr.Row():
            gr.HTML(value=PREFERENCE_ELICITATION_TASK, label="Preference Elicitation Task")
        with gr.Row():
            with gr.Column():
                user_narrative = gr.HTML(label="User Narrative")
            with gr.Column():
                with gr.Row():
                    elicitation_chatbot = gr.Chatbot(height=600)
                with gr.Row():
                    start_conversation = gr.Button(value="Start Conversation")
                with gr.Row():
                    msg = gr.Textbox(scale=1, label="User Input")
                with gr.Row():
                    msg_button = gr.Button(value="Send This Message to Advisor", interactive=False)
                    continue_button = gr.Button(value="Show More of the Advisor’s Answer", interactive=False)
        return {
            "start_conversation": start_conversation,
            "msg_button": msg_button,
            "continue_button": continue_button,
            "msg": msg,
            "elicitation_chatbot": elicitation_chatbot,
            "user_narrative": user_narrative,
        }

    def tab_final_evaluation():
        with gr.Row():
            gr.HTML(value=FINAL_EVALUATION)
        with gr.Row():
            gr.HTML(value="<h3>Rank the individual stocks below according to your desire to invest in each one.</h3>")
        with gr.Row():
            ranking_first_comp = gr.Dropdown(choices=[1, 2, 3, 4, 5])
            ranking_second_comp = gr.Dropdown(choices=[1, 2, 3, 4, 5])
            ranking_third_comp = gr.Dropdown(choices=[1, 2, 3, 4, 5])
            ranking_fourth_comp = gr.Dropdown(choices=[1, 2, 3, 4, 5])
        with gr.Row():
            gr.HTML(
                value='<h3>Choose how strongly you agree with each statement about the advisor (<strong style="color:red;">1 for Strongly Disagree</strong>, <strong style="color:green;">7 for Strongly Agree</strong>).</h3>'
            )
        with gr.Row():
            perceived_personalization_1 = likert_evaluation("The advisor understands my needs")
            perceived_personalization_2 = likert_evaluation("The advisor knows what I want")
        with gr.Row():
            emotional_trust_1 = likert_evaluation("I feel secure about relying on this advisor for my decisions")
            emotional_trust_2 = likert_evaluation("I feel comfortable about relying on this advisor for my decisions")
        with gr.Row():
            emotional_trust_3 = likert_evaluation("I feel content about relying on this advisor for my decisions")

            trust_in_competence_1 = likert_evaluation("The advisor is a real expert in assessing the stock")
        with gr.Row():
            trust_in_competence_2 = likert_evaluation("The advisor has good knowledge of the stock")

            intention_to_use_1 = likert_evaluation(
                "I am willing to use this advisor as an aid to help with my decision about which stock to purchase"
            )
        with gr.Row():
            intention_to_use_2 = likert_evaluation(
                "I am willing to let this advisor assist me in deciding which stock to purchase"
            )

            usefulness_1 = likert_evaluation("The advisor helped me with the decision-making")
        with gr.Row():
            usefulness_2 = likert_evaluation("Using the advisor to find what I would like to invest in is easy")

            usefulness_3 = likert_evaluation("The advisor gave me good suggestions")
        with gr.Row():

            overall_satisfaction = likert_evaluation("Overall, I am satisfied with the advisor")

            providing_information_1 = likert_evaluation("The advisor presents financial knowledge")
        with gr.Row():
            providing_information_2 = likert_evaluation("The advisor provides the financial knowledge needed")
        with gr.Row():
            textbox = gr.HTML()
            submit_ranking = gr.Button(value="Submit Ranking")
        return {
            "first": ranking_first_comp,
            "second": ranking_second_comp,
            "third": ranking_third_comp,
            "fourth": ranking_fourth_comp,
            "evaluators": {
                "perceived_personalization_1": perceived_personalization_1,
                "perceived_personalization_2": perceived_personalization_2,
                "emotional_trust_1": emotional_trust_1,
                "emotional_trust_2": emotional_trust_2,
                "emotional_trust_3": emotional_trust_3,
                "trust_in_competence_1": trust_in_competence_1,
                "trust_in_competence_2": trust_in_competence_2,
                "intention_to_use_1": intention_to_use_1,
                "intention_to_use_2": intention_to_use_2,
                "usefulness_1": usefulness_1,
                "usefulness_2": usefulness_2,
                "usefulness_3": usefulness_3,
                "overall_satisfaction": overall_satisfaction,
                "providing_information_1": providing_information_1,
                "providing_information_2": providing_information_2,
            },
            "submit_ranking": submit_ranking,
            "text_box": textbox,
        }

    def click_control_exploration_stage(
        tabs, user_id, tab_session, user_preference_elicitation_session, system_description_without_context
    ):
        (
            comp,
            system_instruction_context,
            start_conversation,
            msg_button,
            continue_button,
            chatbot,
            msg,
            dropdown,
            reason,
            likelihood,
            confidence,
            evaluation_send_button,
            textbox,
            stock_familiarity,
            stock_familiarity_button,
            stock_familiarity_explain,
        ) = (
            tabs["comp"],
            tabs["system_instruction_context"],
            tabs["start_conversation"],
            tabs["msg_button"],
            tabs["continue_button"],
            tabs["chatbot"],
            tabs["msg"],
            tabs["dropdown"],
            tabs["reason"],
            tabs["likelihood"],
            tabs["confidence"],
            tabs["evaluation_send_button"],
            tabs["textbox"],
            tabs["stock_familiarity"],
            tabs["stock_familiarity_button"],
            tabs["stock_familiarity_explain"],
        )
        system_instruction = ""
        stock_familiarity_button.click(
            lambda user_id, comp, stock_familiarity, stock_familiarity_button, start_conversation: respond_familiarity(
                user_id, comp, stock_familiarity, stock_familiarity_button, start_conversation
            ),
            [user_id, comp, stock_familiarity, stock_familiarity_button, start_conversation],
            [stock_familiarity_explain, stock_familiarity_button, start_conversation],
        )

        start_conversation.click(
            lambda user_id, tab_session, history, comp, user_preference_elicitation_session, system_description_without_context, system_instruction_context: respond_start_conversation(
                user_id,
                tab_session,
                history,
                system_instruction,
                comp,
                user_preference_elicitation_data=user_preference_elicitation_session,
                system_description_without_context=system_description_without_context,
                system_instruction_context=system_instruction_context,
            ),
            [
                user_id,
                tab_session,
                chatbot,
                comp,
                user_preference_elicitation_session,
                system_description_without_context,
                system_instruction_context,
            ],
            [tab_session, chatbot, start_conversation, msg_button, continue_button],
        )
        msg_button.click(
            lambda user_id, tab_session, message, history, comp, user_preference_elicitation_session, system_description_without_context, system_instruction_context: respond(
                user_id,
                tab_session,
                message,
                tab_session["history"],
                system_instruction,
                comp,
                user_preference_elicitation_data=user_preference_elicitation_session,
                system_description_without_context=system_description_without_context,
                system_instruction_context=system_instruction_context,
            ),
            [
                user_id,
                tab_session,
                msg,
                chatbot,
                comp,
                user_preference_elicitation_session,
                system_description_without_context,
                system_instruction_context,
            ],
            [tab_session, msg, chatbot],
        )
        continue_button.click(
            lambda user_id, tab_session, history, comp, user_preference_elicitation_session, system_description_without_context, system_instruction_context: respond_continue(
                user_id,
                tab_session,
                tab_session["history"],
                system_instruction,
                comp,
                user_preference_elicitation_data=user_preference_elicitation_session,
                system_description_without_context=system_description_without_context,
                system_instruction_context=system_instruction_context,
            ),
            [
                user_id,
                tab_session,
                chatbot,
                comp,
                user_preference_elicitation_session,
                system_description_without_context,
                system_instruction_context,
            ],
            [tab_session, chatbot],
        )
        evaluation_send_button.click(
            lambda user_id, comp, tab_session, dropdown, reason, likelihood, confidence, evaluation_send_button, textbox: respond_evaluation(
                user_id,
                tab_session,
                {
                    "selection": dropdown,
                    "reason": reason,
                    "likelihood": likelihood,
                    "confidence": confidence,
                },
                comp,
                evaluation_send_button,
                textbox,
            ),
            [
                user_id,
                comp,
                tab_session,
                dropdown,
                reason,
                likelihood,
                confidence,
                evaluation_send_button,
                textbox,
            ],
            [tab_session, dropdown, reason, likelihood, confidence, evaluation_send_button, textbox],
        )

    def click_control_preference_stage(tabs, user_id, user_preference_elicitation_session):
        (
            start_conversation,
            msg_button,
            continue_button,
            elicitation_chatbot,
            msg,
        ) = (
            tabs["start_conversation"],
            tabs["msg_button"],
            tabs["continue_button"],
            tabs["elicitation_chatbot"],
            tabs["msg"],
        )
        # nonlocal user_id
        start_conversation.click(
            lambda user_id, user_preference_elicitation_data, history: respond_start_conversation(
                user_id,
                user_preference_elicitation_data,
                history,
                SYSTEM_INSTRUECTION_PREFERENCE_ELICITATION,
                user_elicitation=True,
            ),
            [user_id, user_preference_elicitation_session, elicitation_chatbot],
            [user_preference_elicitation_session, elicitation_chatbot, start_conversation, msg_button, continue_button],
        )
        msg_button.click(
            lambda user_id, tab_data, message, history: respond(
                user_id,
                tab_data,
                message,
                tab_data["history"],
                SYSTEM_INSTRUECTION_PREFERENCE_ELICITATION,
                user_elicitation=True,
            ),
            [user_id, user_preference_elicitation_session, msg, elicitation_chatbot],
            [user_preference_elicitation_session, msg, elicitation_chatbot],
        )
        continue_button.click(
            lambda user_id, tab_data, history: respond_continue(
                user_id,
                tab_data,
                tab_data["history"],
                SYSTEM_INSTRUECTION_PREFERENCE_ELICITATION,
                user_elicitation=True,
            ),
            [user_id, user_preference_elicitation_session, elicitation_chatbot],
            [user_preference_elicitation_session, elicitation_chatbot],
        )

    def click_control_final_evaluation(tabs, user_id, first_comp, second_comp, third_comp, fourth_comp, evaluators):
        (
            ranking_first_comp,
            ranking_second_comp,
            ranking_third_comp,
            ranking_fourth_comp,
        ) = (
            tabs["first"],
            tabs["second"],
            tabs["third"],
            tabs["fourth"],
        )
        (
            perceived_personalization_1,
            perceived_personalization_2,
            emotional_trust_1,
            emotional_trust_2,
            emotional_trust_3,
            trust_in_competence_1,
            trust_in_competence_2,
            intention_to_use_1,
            intention_to_use_2,
            usefulness_1,
            usefulness_2,
            usefulness_3,
            overall_satisfaction,
            providing_information_1,
            providing_information_2,
        ) = (
            evaluators["perceived_personalization_1"],
            evaluators["perceived_personalization_2"],
            evaluators["emotional_trust_1"],
            evaluators["emotional_trust_2"],
            evaluators["emotional_trust_3"],
            evaluators["trust_in_competence_1"],
            evaluators["trust_in_competence_2"],
            evaluators["intention_to_use_1"],
            evaluators["intention_to_use_2"],
            evaluators["usefulness_1"],
            evaluators["usefulness_2"],
            evaluators["usefulness_3"],
            evaluators["overall_satisfaction"],
            evaluators["providing_information_1"],
            evaluators["providing_information_2"],
        )
        result_textbox = tabs["text_box"]
        submit_ranking = tabs["submit_ranking"]
        submit_ranking.click(
            lambda user_id, first_comp, ranking_first_comp, second_comp, ranking_second_comp, third_comp, ranking_third_comp, fourth_comp, ranking_fourth_comp, perceived_personalization_1, perceived_personalization_2, emotional_trust_1, emotional_trust_2, emotional_trust_3, trust_in_competence_1, trust_in_competence_2, intention_to_use_1, intention_to_use_2, usefulness_1, usefulness_2, usefulness_3, overall_satisfaction, providing_information_1, providing_information_2, submit_ranking: respond_final_ranking(
                user_id,
                first_comp,
                ranking_first_comp,
                second_comp,
                ranking_second_comp,
                third_comp,
                ranking_third_comp,
                fourth_comp,
                ranking_fourth_comp,
                perceived_personalization_1,
                perceived_personalization_2,
                emotional_trust_1,
                emotional_trust_2,
                emotional_trust_3,
                trust_in_competence_1,
                trust_in_competence_2,
                intention_to_use_1,
                intention_to_use_2,
                usefulness_1,
                usefulness_2,
                usefulness_3,
                overall_satisfaction,
                providing_information_1,
                providing_information_2,
                submit_ranking,
            ),
            # Input components (names and rankings)
            [
                user_id,
                first_comp,
                ranking_first_comp,
                second_comp,
                ranking_second_comp,
                third_comp,
                ranking_third_comp,
                fourth_comp,
                ranking_fourth_comp,
                perceived_personalization_1,
                perceived_personalization_2,
                emotional_trust_1,
                emotional_trust_2,
                emotional_trust_3,
                trust_in_competence_1,
                trust_in_competence_2,
                intention_to_use_1,
                intention_to_use_2,
                usefulness_1,
                usefulness_2,
                usefulness_3,
                overall_satisfaction,
                providing_information_1,
                providing_information_2,
                submit_ranking,
            ],
            # Output component(s) where you want the result to appear, e.g., result_textbox
            [result_textbox, submit_ranking],
        )

    def respond(
        user_id,
        tab_data,
        message,
        history,
        system_instruction,
        tab_name=None,
        user_elicitation=False,
        user_preference_elicitation_data=None,
        system_description_without_context=None,
        system_instruction_context=None,
    ):
        """
        Return:
        msg
        chat_history
        retrieved_passage
        rewritten_query

        """
        assert (
            tab_name is not None or user_elicitation is True
        ), "Tab name is required for the start of the conversation unless it is not preference elicitation."
        # Add user profile to system instruction
        if system_description_without_context is not None and system_instruction_context is not None:
            system_instruction = system_description_without_context + "\n" + system_instruction_context
        if not user_elicitation:
            system_instruction = add_user_profile_to_system_instruction(
                user_id,
                system_instruction,
                user_preference_elicitation_data,
                summary=USER_PREFERENCE_SUMMARY,
                terminator=terminator,
            )
        # Formatting Input
        print(f"User Message: {message} in Tab: {tab_name}")
        # From string to list [{"role":"user", "content": message}, ...]
        history = gradio_to_huggingface_message(history)
        # We can implement context window here as we need all the system interaction. We can cut some of the early interactions if needed.
        history = conversation_window(history, CONV_WINDOW)
        # Add system instruction to the history
        history = format_context(system_instruction, history)
        # Add user message to the history
        history_with_user_utterance = format_user_message(message, history)
        # Call API instead of locally handle it
        if API_TYPE == "local":
            outputs_text, history = generate_response_local_api(history_with_user_utterance, terminator, 128, API_URL)
        elif API_TYPE == "together":
            outputs_text, history = generate_response_together_api(history_with_user_utterance, 128, TOGETHER_CLIENT)
        else:
            outputs_text, history = generate_response_debugging(history_with_user_utterance)
        # exclude system interaction and store the others in the history
        history = huggingface_to_gradio_message(history)
        if tab_name is not None:
            print(f"Tab: {tab_name}\nSystem Output: {outputs_text}")

            # Log the user message and response
            save_feedback(
                user_id,
                uuid_this_session,
                "interaction",
                {"type": tab_name, "role": "user", "content": message},
                feedback_file_interaction,
            )
            save_feedback(
                user_id,
                uuid_this_session,
                "interaction",
                {"type": tab_name, "role": "assistant", "content": outputs_text},
                feedback_file_interaction,
            )
            # log_action(user_id, tab_name, "User Message", message)
            # log_action(user_id, tab_name, "Response", outputs_text)
            # Store the updated history for this tab
            tab_data["history"] = history
        if user_elicitation:
            print(f"User Elicitation\nSystem Output: {outputs_text}")
            save_feedback(
                user_id,
                uuid_this_session,
                "Interaction",
                {"type": "user_elicitation", "role": "user", "content": message},
                feedback_file_interaction,
            )
            save_feedback(
                user_id,
                uuid_this_session,
                "Interaction",
                {"type": "user_elicitation", "role": "assistant", "content": outputs_text},
                feedback_file_interaction,
            )
            # log_action(user_id, "User_Elicitation", "User Message", message)
            # log_action(user_id, "User_Elicitation", "Response", outputs_text)
            tab_data["history"] = history
        # if SESSION_DEBUG:
        #     log_action(user_id, "Session", "History", history)

        return tab_data, "", history

    def respond_familiarity(user_id, tab_name, stock_familiarity, stock_familiarity_button, start_conversation):
        if stock_familiarity is None:
            return (
                """<div style="background-color: #f8d7da; color: #721c24; padding: 15px; border: 1px solid #f5c6cb; border-radius: 5px; margin-bottom: 20px;">
                    <strong>Please select your familiarity with the stock.</strong>
                    </div>""",
                stock_familiarity_button,
                start_conversation,
            )
        else:
            save_feedback(
                user_id,
                uuid_this_session,
                "familiarity",
                {"stock": tab_name, "familiarity": stock_familiarity},
                feedback_file_stock_familiarity,
            )
            return (
                """<div style="background-color: #d4edda; color: #155724; padding: 15px; border: 1px solid #c3e6cb; border-radius: 5px; margin-bottom: 20px;">
                        Thank you for submitting your familiarity rating. Please proceed by reading the user narrative, and then press ‘Start Conversation’ to begin the discussion.
                    </div>""",
                gr.Button(value="Familiarity Received", interactive=False),
                gr.Button(value="Start Conversation", interactive=True),
            )

    def respond_start_conversation(
        user_id,
        tab_data,
        history,
        system_instruction,
        tab_name=None,
        user_elicitation=False,
        user_preference_elicitation_data=None,
        system_description_without_context=None,
        system_instruction_context=None,
    ):
        assert (
            tab_name is not None or user_elicitation is True
        ), "Tab name is required for the start of the conversation unless it is not preference elicitation."
        if system_description_without_context is not None and system_instruction_context is not None:
            system_instruction = system_description_without_context + "\n" + system_instruction_context
        if not user_elicitation:
            print(f"User Preference Elicitation Data: {user_preference_elicitation_data}")
            print(f"Tab data: {tab_data}")
            system_instruction = add_user_profile_to_system_instruction(
                user_id,
                system_instruction,
                user_preference_elicitation_data,
                summary=USER_PREFERENCE_SUMMARY,
                terminator=terminator,
            )
            print(f"Tab: {tab_name}\nSystem Instruction:{system_instruction}")
        history = gradio_to_huggingface_message(history)
        history = format_context(system_instruction, history)
        first_message = FIRST_MESSAGE
        history_with_user_utterance = format_user_message(first_message, history)
        if API_TYPE == "local":
            outputs_text, history = generate_response_local_api(history_with_user_utterance, terminator, 128, API_URL)
        elif API_TYPE == "together":
            outputs_text, history = generate_response_together_api(history_with_user_utterance, 128, TOGETHER_CLIENT)
        else:
            outputs_text, history = generate_response_debugging(history_with_user_utterance)
        # Format
        history = huggingface_to_gradio_message(history)
        if tab_name is not None:
            print(f"Tab: {tab_name}\nHistory: {history}")
            # Log the user message and response
            save_feedback(
                user_id,
                uuid_this_session,
                "interaction",
                {"type": tab_name, "role": "user", "content": first_message},
                feedback_file_interaction,
            )
            save_feedback(
                user_id,
                uuid_this_session,
                "interaction",
                {"type": tab_name, "role": "assistant", "content": outputs_text},
                feedback_file_interaction,
            )
            # log_action(user_id, tab_name, "User Message", first_message)
            # log_action(user_id, tab_name, "Response", outputs_text)
            # Store the updated history for this tab
            tab_data["history"] = history
        if user_elicitation:
            print(f"User Elicitation\nHistory: {history}")
            save_feedback(
                user_id,
                uuid_this_session,
                "interaction",
                {"type": "user_elicitation", "role": "user", "content": first_message},
                feedback_file_interaction,
            )
            save_feedback(
                user_id,
                uuid_this_session,
                "Interaction",
                {"type": "user_elicitation", "role": "assistant", "content": outputs_text},
                feedback_file_interaction,
            )
            # log_action(user_id, "User_Elicitation", "User Message", first_message)
            # log_action(user_id, "User_Elicitation", "Response", outputs_text)
            tab_data["history"] = history
        # if SESSION_DEBUG:
        #     log_action(user_id, "Session", "History", history)
        return (
            tab_data,
            history,
            gr.Button(value="Start Conversation", interactive=False),
            gr.Button(value="Send This Message to Advisor", interactive=True),
            gr.Button(value="Show More of the Advisor’s Answer", interactive=True),
        )

    def respond_continue(
        user_id,
        tab_data,
        history,
        system_instruction,
        tab_name=None,
        user_elicitation=False,
        user_preference_elicitation_data=None,
        system_description_without_context=None,
        system_instruction_context=None,
    ):
        assert (
            tab_name is not None or user_elicitation is True
        ), "Tab name is required for the start of the conversation."
        # Add user profile to system instruction
        if system_description_without_context is not None and system_instruction_context is not None:
            system_instruction = system_description_without_context + "\n" + system_instruction_context
        if not user_elicitation:
            system_instruction = add_user_profile_to_system_instruction(
                user_id,
                system_instruction,
                user_preference_elicitation_data,
                summary=USER_PREFERENCE_SUMMARY,
                terminator=terminator,
            )
        message = "continue"
        history = gradio_to_huggingface_message(history)
        history = conversation_window(history, CONV_WINDOW)
        history = format_context(system_instruction, history)
        history_with_user_utterance = format_user_message(message, history)
        if API_TYPE == "local":
            outputs_text, history = generate_response_local_api(history_with_user_utterance, terminator, 128, API_URL)
        elif API_TYPE == "together":
            outputs_text, history = generate_response_together_api(history_with_user_utterance, 128, TOGETHER_CLIENT)
        else:
            outputs_text, history = generate_response_debugging(history_with_user_utterance)
        history = huggingface_to_gradio_message(history)
        if tab_name is not None:
            save_feedback(
                user_id,
                uuid_this_session,
                "interaction",
                {
                    "type": tab_name,
                    "role": "user",
                    "content": message,
                },
                feedback_file_interaction,
            )
            save_feedback(
                user_id,
                uuid_this_session,
                "interaction",
                {"type": tab_name, "role": "assistant", "content": outputs_text},
                feedback_file_interaction,
            )
            # log_action(user_id, tab_name, "Show More of the Advisor’s Answer", "User continued the conversation")
            # log_action(user_id, tab_name, "Response", outputs_text)

            # Update history for this tab
            tab_data["history"] = history
        if user_elicitation:
            print(f"User Elicitation\nSystem Output: {outputs_text}")
            save_feedback(
                user_id,
                uuid_this_session,
                "interaction",
                {"type": "user_elicitation", "role": "user", "content": message},
                feedback_file_interaction,
            )
            save_feedback(
                user_id,
                uuid_this_session,
                "interaction",
                {"type": "user_elicitation", "role": "assistant", "content": outputs_text},
                feedback_file_interaction,
            )
            # log_action(user_id, "User_Elicitation", "Response", outputs_text)
            tab_data["history"] = history
        # if SESSION_DEBUG:
        #     log_action(user_id, "Session", "History", history)
        return tab_data, history

    def respond_evaluation(user_id, tab_data, evals, tab_name, evaluation_send_button, textbox):

        # dropdown, readon_button, multi-evaluator
        print(f"Tab: {tab_name}\nEvaluation: {evals}")
        if evals["selection"] is None or evals["likelihood"] is None or evals["confidence"] is None:
            return (
                tab_data,
                evals["selection"],
                evals["reason"],
                evals["likelihood"],
                evals["confidence"],
                evaluation_send_button,
                """<div style="background-color: #f8d7da; color: #721c24; padding: 15px; border: 1px solid #f5c6cb; border-radius: 5px; margin-bottom: 20px;">
                    <strong>Please make sure that you answer all the questions.</strong>
                    </div>""",
            )
        else:
            save_feedback(
                user_id,
                uuid_this_session,
                "round_evaluation",
                {**evals, "company": tab_name},
                feedback_file_round_evaluation,
            )
            # log_action(user_id, tab_name, "Round Evaluation", "Following")
            # for key, value in evals.items():
            #     log_action(user_id, tab_name, key, value)
            # Store the reason for this tab
            tab_data["multi_evaluator"] = evals
            evaluation_send_button = gr.Button(value="Evaluation receirved", interactive=False)
            return (
                tab_data,
                evals["selection"],
                evals["reason"],
                evals["likelihood"],
                evals["confidence"],
                evaluation_send_button,
                """<div style="background-color: #d4edda; color: #155724; padding: 15px; border: 1px solid #c3e6cb; border-radius: 5px; margin-bottom: 20px;">
                        <strong>Thank you for submitting your evaluation. You may proceed to the next tab..</strong>
                    </div>""",
            )

    def respond_final_ranking(
        user_id,
        first_comp,
        ranking_first_comp,
        second_comp,
        ranking_second_comp,
        third_comp,
        ranking_third_comp,
        fourth_comp,
        ranking_fourth_comp,
        perceived_personalization_1,
        perceived_personalization_2,
        emotional_trust_1,
        emotional_trust_2,
        emotional_trust_3,
        trust_in_competence_1,
        trust_in_competence_2,
        intention_to_use_1,
        intention_to_use_2,
        usefulness_1,
        usefulness_2,
        usefulness_3,
        overall_satisfaction,
        providing_information_1,
        providing_information_2,
        submit_ranking,
    ):
        # make sure that they are not the same
        ranking_list = [
            ranking_first_comp,
            ranking_second_comp,
            ranking_third_comp,
            ranking_fourth_comp,
        ]
        if len(set(ranking_list)) != len(ranking_list):
            return (
                """<div style="background-color: #f8d7da; color: #721c24; padding: 15px; border: 1px solid #f5c6cb; border-radius: 5px; margin-bottom: 20px;">
    <strong>Please make sure that you are not ranking the same stock multiple times.</strong>
</div>""",
                submit_ranking,
            )
        if any(
            var is None
            for var in [
                emotional_trust_1,
                emotional_trust_2,
                emotional_trust_3,
                trust_in_competence_1,
                trust_in_competence_2,
                intention_to_use_1,
                intention_to_use_2,
                usefulness_1,
                usefulness_2,
                usefulness_3,
                overall_satisfaction,
                providing_information_1,
                providing_information_2,
            ]
        ):
            return (
                """<div style="background-color: #f8d7da; color: #721c24; padding: 15px; border: 1px solid #f5c6cb; border-radius: 5px; margin-bottom: 20px;">
    <strong>Please make sure that you answer all the statements.</strong>
</div>""",
                submit_ranking,
            )
        else:
            save_feedback(
                user_id,
                uuid_this_session,
                "final_ranking",
                {
                    "comp_order": [first_comp, second_comp, third_comp, fourth_comp],
                    "ranking": ranking_list,
                },
                feedback_file_final_ranking,
            )

            save_feedback(
                user_id,
                uuid_this_session,
                "final_ranking_survey",
                {
                    "perceived_personalization_1": perceived_personalization_1,
                    "perceived_personalization_2": perceived_personalization_2,
                    "emotional_trust_1": emotional_trust_1,
                    "emotional_trust_2": emotional_trust_2,
                    "emotional_trust_3": emotional_trust_3,
                    "trust_in_competence_1": trust_in_competence_1,
                    "trust_in_competence_2": trust_in_competence_2,
                    "intention_to_use_1": intention_to_use_1,
                    "intention_to_use_2": intention_to_use_2,
                    "usefulness_1": usefulness_1,
                    "usefulness_2": usefulness_2,
                    "usefulness_3": usefulness_3,
                    "overall_satisfaction": overall_satisfaction,
                    "providing_information_1": providing_information_1,
                    "providing_information_2": providing_information_2,
                },
                feedback_file_final_survey,
            )
            submit_ranking = gr.Button(value="Final evaluaiotn received", interactive=False)
            return (
                """<div style="background-color: #d4edda; color: #155724; padding: 15px; border: 1px solid #c3e6cb; border-radius: 5px; margin-bottom: 20px;">
                        <strong>Thank you for participating in the experiment. This concludes the session. You may now close the tab.</strong>
                    </div>""",
                submit_ranking,
            )

    def get_context(index, raw_context_list, stock_context_list):
        comp = raw_context_list[index]["short_name"]
        context = stock_context_list[index]
        general_instruction, stock_instruction, user_narrative_instruction = get_task_instruction_for_user(
            raw_context_list[index]
        )
        return comp, context, general_instruction, stock_instruction, user_narrative_instruction

    def set_user_id(request: gr.Request):
        user_id = request.username
        narrative_id = user_id.split("_")[-2]
        personality_id = user_id.split("_")[-1]
        print(f"User ID: {user_id}, Narrative ID: {narrative_id}, Personality ID: {personality_id}")
        return user_id, narrative_id, personality_id

    def get_inst_without_context(personality_id):
        return SYSTEM_INSTRUCTION + "\n" + personality_prompts[int(personality_id)]

    def get_stock_related_context(narrative_id):
        raw_context_list = build_raw_context_list(context_info_list[int(narrative_id)])
        stock_context_list = build_context(context_info_list[int(narrative_id)])
        return raw_context_list, stock_context_list

    def set_initial_values(request: gr.Request):
        # Set user specific information (Session State)
        user_id, narrative_id, personality_id = set_user_id(request)
        # System instruction without prompt
        system_description_without_context = get_inst_without_context(personality_id)
        # Stock related context
        raw_context_list, stock_context_list = get_stock_related_context(narrative_id)
        # User Narrative
        user_narrative = get_user_narrative_from_raw(raw_context_list[0]["user_narrative"])
        # Tab Context
        (
            first_comp,
            first_context,
            first_general_instruction,
            first_stock_instruction,
            first_user_narrative_instruction,
        ) = get_context(0, raw_context_list, stock_context_list)
        (
            second_comp,
            second_context,
            second_general_instruction,
            second_stock_instruction,
            second_user_narrative_instruction,
        ) = get_context(1, raw_context_list, stock_context_list)
        (
            third_comp,
            third_context,
            third_general_instruction,
            third_stock_instruction,
            thired_user_narrative_instruction,
        ) = get_context(2, raw_context_list, stock_context_list)
        (
            fourth_comp,
            fourth_context,
            fourth_general_instruction,
            fourth_stock_instruction,
            fourth_user_narrative_instruction,
        ) = get_context(3, raw_context_list, stock_context_list)
        # Final Evaluation
        ranking_first_comp = gr.Dropdown(choices=[1, 2, 3, 4, 5], label=first_comp)
        ranking_second_comp = gr.Dropdown(choices=[1, 2, 3, 4, 5], label=second_comp)
        ranking_third_comp = gr.Dropdown(choices=[1, 2, 3, 4, 5], label=third_comp)
        ranking_fourth_comp = gr.Dropdown(choices=[1, 2, 3, 4, 5], label=fourth_comp)
        return (
            user_id,
            narrative_id,
            personality_id,
            system_description_without_context,
            raw_context_list,
            stock_context_list,
            user_narrative,
            first_comp,
            first_context,
            first_general_instruction,
            first_stock_instruction,
            first_user_narrative_instruction,
            second_comp,
            second_context,
            second_general_instruction,
            second_stock_instruction,
            second_user_narrative_instruction,
            third_comp,
            third_context,
            third_general_instruction,
            third_stock_instruction,
            third_user_narrative_instruction,
            fourth_comp,
            fourth_context,
            fourth_general_instruction,
            fourth_stock_instruction,
            fourth_user_narrative_instruction,
            ranking_first_comp,
            ranking_second_comp,
            ranking_third_comp,
            ranking_fourth_comp,
        )

    with gr.Blocks(title="RAG Chatbot Q&A", theme="Soft") as demo:
        # Set user specific information (Session State)
        user_id = gr.State()
        narrative_id = gr.State()
        personality_id = gr.State()
        system_description_without_context = gr.State()
        # Context data
        raw_context_list = gr.State()
        stock_context_list = gr.State()
        first_comp = gr.State()
        first_context = gr.State()
        second_comp = gr.State()
        second_context = gr.State()
        third_comp = gr.State()
        third_context = gr.State()
        fourth_comp = gr.State()
        fourth_context = gr.State()
        # Tab data
        if DEBUG:
            user_preference_elicitation_session = gr.State(
                value={
                    "history": "",
                    "summary_history": """User Profile collected in the previous conversations: Based on our previous conversation, here's a summary of your investment preferences:

    #     1. **Preferred Industries:** You're interested in investing in the healthcare sector, without a specific preference for sub-industries such as pharmaceuticals, medical devices, biotechnology, or healthcare services.
    #     2. **Value vs. Growth Stocks:** You prefer growth stocks, which have the potential for high returns but may be riskier.
    #     3. **Dividend vs. Non-Dividend Stocks:** You're open to both dividend and non-dividend growth stocks, focusing on reinvesting profits for future growth.
    #     4. **Cyclical vs. Non-Cyclical Stocks:** You're interested in cyclical stocks, which are sensitive to economic fluctuations and tend to perform well during economic expansions.""",
                }
            )
        else:
            user_preference_elicitation_session = gr.State(value={"history": "", "summary_history": ""})
        first_comp_session = gr.State(value={"history": [], "selection": "", "reason": ""})
        second_comp_session = gr.State(value={"history": [], "selection": "", "reason": ""})
        third_comp_session = gr.State(value={"history": [], "selection": "", "reason": ""})
        fourth_comp_session = gr.State(value={"history": [], "selection": "", "reason": ""})
        # EXperiment Instruction
        with gr.Tab("Experiment Instruction") as instruction_tab:
            gr.HTML(value=INSTRUCTION_PAGE, label="Experiment Instruction")
        # User Preference Elicitation Tab
        with gr.Tab("Preference Elicitation Stage") as preference_elicitation_tab:
            user_preference_elicitation_tab = tab_creation_preference_stage()
            user_narrative = user_preference_elicitation_tab["user_narrative"]
            click_control_preference_stage(
                user_preference_elicitation_tab, user_id, user_preference_elicitation_session
            )
        with gr.Tab("Financial Decision Stage") as financial_decision:
            # Experiment Tag
            first_tab = tab_creation_exploration_stage(0, first_comp, first_context)
            first_general_instruction, first_stock_instruction, first_user_narrative_instruction = (
                first_tab["general_instruction"],
                first_tab["stock_instruction"],
                first_tab["user_narrative_instruction"],
            )
            click_control_exploration_stage(
                first_tab,
                user_id,
                first_comp_session,
                user_preference_elicitation_session,
                system_description_without_context,
            )
            second_tab = tab_creation_exploration_stage(1, second_comp, second_context)
            second_general_instruction, second_stock_instruction, second_user_narrative_instruction = (
                second_tab["general_instruction"],
                second_tab["stock_instruction"],
                second_tab["user_narrative_instruction"],
            )
            click_control_exploration_stage(
                second_tab,
                user_id,
                second_comp_session,
                user_preference_elicitation_session,
                system_description_without_context,
            )
            third_tab = tab_creation_exploration_stage(2, third_comp, third_context)
            third_general_instruction, third_stock_instruction, third_user_narrative_instruction = (
                third_tab["general_instruction"],
                third_tab["stock_instruction"],
                third_tab["user_narrative_instruction"],
            )
            click_control_exploration_stage(
                third_tab,
                user_id,
                third_comp_session,
                user_preference_elicitation_session,
                system_description_without_context,
            )
            fourth_tab = tab_creation_exploration_stage(3, fourth_comp, fourth_context)
            fourth_general_instruction, fourth_stock_instruction, fourth_user_narrative_instruction = (
                fourth_tab["general_instruction"],
                fourth_tab["stock_instruction"],
                fourth_tab["user_narrative_instruction"],
            )
            click_control_exploration_stage(
                fourth_tab,
                user_id,
                fourth_comp_session,
                user_preference_elicitation_session,
                system_description_without_context,
            )
        with gr.Tab("Final Evaluation Stage") as final_evaluation:
            final_evaluation_tab = tab_final_evaluation()
            (
                ranking_first_comp,
                ranking_second_comp,
                ranking_third_comp,
                ranking_fourth_comp,
                evaluators,
            ) = (
                final_evaluation_tab["first"],
                final_evaluation_tab["second"],
                final_evaluation_tab["third"],
                final_evaluation_tab["fourth"],
                final_evaluation_tab["evaluators"],
            )
            click_control_final_evaluation(
                final_evaluation_tab, user_id, first_comp, second_comp, third_comp, fourth_comp, evaluators
            )

        demo.load(
            set_initial_values,
            inputs=None,
            outputs=[
                user_id,
                narrative_id,
                personality_id,
                system_description_without_context,
                raw_context_list,
                stock_context_list,
                user_narrative,
                first_comp,
                first_context,
                first_general_instruction,
                first_stock_instruction,
                first_user_narrative_instruction,
                second_comp,
                second_context,
                second_general_instruction,
                second_stock_instruction,
                second_user_narrative_instruction,
                third_comp,
                third_context,
                third_general_instruction,
                third_stock_instruction,
                third_user_narrative_instruction,
                fourth_comp,
                fourth_context,
                fourth_general_instruction,
                fourth_stock_instruction,
                fourth_user_narrative_instruction,
                ranking_first_comp,
                ranking_second_comp,
                ranking_third_comp,
                ranking_fourth_comp,
            ],
        )
    return demo


if __name__ == "__main__":
    file_path = os.path.join(ROOT_FILE, "./data/single_stock_data/experiment_processed_data.jsonl")
    topics = [
        "healthcare_growth_defensive",
        "dividend_value_defensive",
        "nondividend_value_cyclical",
    ]
    context_info_list = get_context_list(file_path)  # str to List of Dict
    print(context_info_list)
    # system instruction consist of Task, Personality, and Context
    """
    Personality
    ["extroverted", "introverted"]
    ["agreeable", "antagonistic"]
    ["conscientious", "unconscientious"]
    ["neurotic", "emotionally stable"]
    ["open to experience", "closed to experience"]]
    """
    # Global variables
    personality = {
        1: [
            "extroverted",
            "agreeable",
            "conscientious",
            "emotionally stable",
            "open to experience",
        ]
    }
    personality_prompts = {i: build_personality_prompt(p) for i, p in personality.items()}
    terminator = ["<eos>", "<unk>", "<sep>", "<pad>", "<cls>", "<mask>"]
    demo = create_demo()
    demo_user_pwd_list = [(f"demo_{i}_1", f"demo_{i}_1") for i in range(1, 11)]
    demo.launch(
        share=True,
        auth=demo_user_pwd_list
        + [
            ("user_1_1", "pw1"),
            ("user_2_1", "pw2"),
            ("user_3_1", "pw3"),
        ],
    )