from datetime import datetime

import streamlit as st
import os
from openai import OpenAI


class ChatBot:
    def __init__(self):
        self.client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
        self.history = [{"role": "system", "content": "You are a helpful assistant."}]

    def generate_response(self, prompt: str) -> str:
        self.history.append({"role": "user", "content": prompt})
        
        completion = self.client.chat.completions.create(
            model="gpt-3.5-turbo", # NOTE: feel free to change it to "gpt-4" or "our LLM"
            messages=self.history
        )
        
        response = completion.choices[0].message.content
        self.history.append({"role": "assistant", "content": response})
        
        return response

    def get_history(self) -> list:
        return self.history


# Read the content of the Markdown file
def read_markdown_file(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        return file.read()


# Credit: Time
def current_year():
    now = datetime.now()
    return now.year


st.set_page_config(layout="wide")
st.title("Yin's Profile 🤖")


with st.sidebar:
    with st.expander("Instruction Manual"):
        st.markdown("""
            ## Yin's Profile 🤖 Chatbot
            This Streamlit app allows you to chat with our LLM model.

            ### How to Use:
            1. **Input**: Type your prompt into the chat input box labeled "What is up?".
            2. **Response**: The app will display a response from our LLM.
            3. **Chat History**: Previous conversations will be shown on the app.

            ### Credits:
            - **Developer**: [Yiqiao Yin](https://www.y-yin.io/) | [App URL](https://huggingface.co/spaces/eagle0504/y-yin-homepage) | [LinkedIn](https://www.linkedin.com/in/yiqiaoyin/) | [YouTube](https://youtube.com/YiqiaoYin/)
            Enjoy chatting with Yin's assistant, Ava!
        """)

    # Example:
    with st.expander("Examples"):
        st.success("Example: Who is Yiqiao Yin?")
        st.success("Example: What did Yiqiao do at graduate school?")
        st.success("Example: Where to find published papers by Yiqiao?")
        st.success("Example: What is Yiqiao's view on AI?")
        st.success("Example: What are some online links by Yiqiao I can read about?")
        st.success("Example: What is Yiqiao's view on stock market?")

    # Add a button to clear the session state
    if st.button("Clear Session"):
        st.session_state.messages = []
        st.experimental_rerun()

    # Credit:
    current_year = current_year()  # This will print the current year
    st.markdown(
        f"""
            <h6 style='text-align: left;'>Copyright © 2010-{current_year} Present Yiqiao Yin</h6>
        """,
        unsafe_allow_html=True,
    )

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Ensure messages are a list of dictionaries
if not isinstance(st.session_state.messages, list):
    st.session_state.messages = []
if not all(isinstance(msg, dict) for msg in st.session_state.messages):
    st.session_state.messages = []

# Path to the Markdown file
md_file_path = 'docs/yiqiao_yin.md'

# Get the content of the Markdown file
yiqiaoyin_profile = read_markdown_file(md_file_path)

# Add the system message with the profile information to the chat history if it hasn't been added yet
if not any(msg["role"] == "system" for msg in st.session_state.messages):
    st.session_state.messages.append({"role": "system", "content": f"You know the following about Mr. Yiqiao Yin: {yiqiaoyin_profile}"})

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    if message["role"] != "system":  # Skip system messages
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

# React to user input
if prompt := st.chat_input("😉 Ask any question or feel free to use the examples provided in the left sidebar."):

    # Display user message in chat message container
    st.chat_message("user").markdown(prompt)

    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})

    # API Call
    bot = ChatBot()
    bot.history = st.session_state.messages.copy()  # Update history from messages
    response = bot.generate_response(prompt)

    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        st.markdown(response)

    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})