Home.py

import streamlit as st
import numpy as np
import pandas as pd
import requests as rq
import backoff
from openai import OpenAI
import openai


def main():
    """
    This script calculates retirement savings based on user inputs and displays the results using Streamlit.
    It also includes a chatbot powered by OpenAI LLM for assistance and an option to explain the results using OpenAI LLM.
    """
    import streamlit as st

    

    hi = False
    if not hi:
        questionnaire = st.container()

        # TODO: Take current or planned starting income.
        starting_income = st.number_input(
            "Current or Planned Starting Income", value=0.0
        )

        # TODO: Take expected income increase.
        expected_income_increase = st.number_input(
            "Expected Income Increase", value=0.0
        )

        # TODO: Take estimated cost of living.

        # TODO: Take mutex fund info.
        principal = st.number_input("Principal")

        interest = st.number_input("Interest rate")

        # TODO: Take planned retirement year.
        years_to_retirement = st.number_input("Years to retire")

        result = principal * pow(1 + interest / 100, years_to_retirement)

        if st.button("Start"):
            # TODO: Write income per year while retired and maximum number of years before money runs out.
            # with st.sidebar:
            #     st.write("Done")

            #     with st.expander("Explain My Results"):
            #         st.caption("powered by OpenAI LLM")
            #         openai_api_key = st.text_input("OpenAI Key", type="password")

            #         if "messages" not in st.session_state:
            #             st.session_state["messages"] = [
            #                 {"role": "assistant", "content": "How can I help you?"}
            #             ]

            #         for msg in st.session_state.messages:
            #             st.chat_message(msg["role"]).write(msg["content"])
            #             if prompt := st.text_input("Ask Questions", key="results_help"):
            #                 if not openai_api_key:
            #                     st.info("Please add your OpenAI API key.")

            #                 else:
            #                     try:
            #                         client = OpenAI(api_key=openai_api_key)

            #                         st.session_state.messages.append(
            #                             {"role": "user", "content": prompt}
            #                         )
            #                         st.chat_message("user").write(prompt)

            #                         @backoff.on_exception(
            #                             backoff.expo, openai.RateLimitError
            #                         )
            #                         def completions_with_backoff(**kwargs):
            #                             return client.chat.completions.create(**kwargs)

            #                         response = completions_with_backoff(
            #                             model="gpt-3.5-turbo",
            #                             messages=st.session_state.messages,
            #                         )

            #                         msg = response.choices[0].message
            #                         st.session_state.messages.append(msg)
            #                         st.chat_message("assistant").write(msg.content)

            #                     finally:
            #                         st.chat_message("assistant").write(
            #                             "Sorry, an error occurred. Try again in a moment."
            #                         )

            # TODO: Display line graph of mean income from accounts versus annual cost of living.

            col1, col2 = st.columns(2)
            with col1:
                st.metric("When you retire you'll make", f"${result} per year")
                st.metric(
                    "The most expensive metro area you can live in is",
                    "West Balm Peach",
                )
                st.metric(
                    f"For investing ${principal} you will get back",
                    f"${result - principal}",
                )

            with col2:
                chart_data = pd.DataFrame(
                    np.random.randn(20, 3), columns=["a", "b", "c"]
                )
                st.line_chart(chart_data)

                st.dataframe(chart_data)


if __name__ == "__main__":
    with st.sidebar:
        st.markdown('## :sparkles: Your Custom Tool :sparkles:')
    main()