import time
import os
import joblib
import streamlit as st
from utils.questions_dataset import system_instruction, get_model_tools
from google.genai import types
from google import genai

st.set_page_config(page_title="LSAT Group A", page_icon="📘")

# GOOGLE_API_KEY=os.environ.get('GOOGLE_API_KEY')
GEMINI_API_KEY = "AIzaSyAjpHA08BUwLhK-tIlORxcB18RAp3541-M"
client = genai.Client(api_key=GEMINI_API_KEY)

new_chat_id = f'{time.time()}'
MODEL_ROLE = 'ai'
AI_AVATAR_ICON = '✨'

# Create a data/ folder if it doesn't already exist
try:
    os.mkdir('data/')
except:
    # data/ folder already exists
    pass

# Load past chats (if available)
try:
    past_chats: dict = joblib.load('data/past_chats_list')
except:
    past_chats = {}

# Sidebar allows a list of past chats
with st.sidebar:
    st.write('# Past Chats')
    if st.session_state.get('chat_id') is None:
        st.session_state.chat_id = st.selectbox(
            label='Pick a past chat',
            options=[new_chat_id] + list(past_chats.keys()),
            format_func=lambda x: past_chats.get(x, 'New Chat'),
            placeholder='_',
        )
    else:
        # This will happen the first time AI response comes in
        st.session_state.chat_id = st.selectbox(
            label='Pick a past chat',
            options=[new_chat_id, st.session_state.chat_id] + list(past_chats.keys()),
            index=1,
            format_func=lambda x: past_chats.get(x, 'New Chat' if x != st.session_state.chat_id else st.session_state.chat_title),
            placeholder='_',
        )

    # Save new chats after a message has been sent to AI
    st.session_state.chat_title = f'ChatSession-{st.session_state.chat_id}'
    

st.title("📘Logical Reasoning: Group A")
next_btn = st.button("Click here when finished")

st.write("Use this AI Tutor to help you understand the concepts. You can ask it to explain the concepts, provide examples, or clarify any doubts you have.")
st.write("Start by sending a hello message!")

# Chat history (allows to ask multiple questions)
try:
    st.session_state.messages = joblib.load(
        f'data/{st.session_state.chat_id}-st_messages'
    )
    st.session_state.gemini_history = joblib.load(
        f'data/{st.session_state.chat_id}-gemini_messages'
    )
except:
    st.session_state.messages = []
    st.session_state.gemini_history = []

sys_prompt = system_instruction % (
    st.session_state.prequiz_df['num_correct'][0],
    st.session_state.prequiz_df['num_questions'][0],
    st.session_state.prequiz_df['num_correct'][1],
    st.session_state.prequiz_df['num_questions'][1],
    st.session_state.prequiz_df['num_correct'][2],
    st.session_state.prequiz_df['num_questions'][2],
    st.session_state.prequiz_df['num_correct'][3],
    st.session_state.prequiz_df['num_questions'][3],
    st.session_state.prequiz_df['num_correct'][4],
    st.session_state.prequiz_df['num_questions'][4],
    st.session_state.prequiz_df['num_correct'][5],
    st.session_state.prequiz_df['num_questions'][5],
    st.session_state.prequiz_df['num_correct'][6],
    st.session_state.prequiz_df['num_questions'][6],
    st.session_state.prequiz_df['num_correct'][7],
    st.session_state.prequiz_df['num_questions'][7],
    st.session_state.prequiz_df['num_correct'][8],
    st.session_state.prequiz_df['num_questions'][8]
)
st.session_state.chat = client.chats.create(model='gemini-2.0-flash',
                                            config=types.GenerateContentConfig(
                                            tools=[get_model_tools()],
                                            system_instruction=sys_prompt),
                                            history=st.session_state.gemini_history
                                               )

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(
        name=message['role'],
        avatar=message.get('avatar'),
    ):
        st.markdown(message['content'])

# React to user input
if prompt := st.chat_input('Your message here...'):
    # Save this as a chat for later
    if st.session_state.chat_id not in past_chats.keys():
        past_chats[st.session_state.chat_id] = st.session_state.chat_title
        joblib.dump(past_chats, 'data/past_chats_list')
    # Display user message in chat message container
    with st.chat_message('user'):
        st.markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append(
        dict(
            role='user',
            content=prompt,
        )
    )
    ## Send message to AI
    response = st.session_state.chat.send_message_stream(
        prompt,
    )
    # Display assistant response in chat message container
    with st.chat_message(
        name=MODEL_ROLE,
        avatar=AI_AVATAR_ICON,
    ):
        message_placeholder = st.empty()
        full_response = ''
        assistant_response = response
        # Streams in a chunk at a time
        for chunk in response:
            # Simulate stream of chunk
            if chunk.text == None:
                chunk.text = "Let's try that one more time so I understand. Please tell me one more time."

            for ch in chunk.text.split(' '):
                full_response += ch + ' '
                time.sleep(0.05)
                # Rewrites with a cursor at end
                message_placeholder.write(full_response + '▌')
        # Write full message with placeholder
        message_placeholder.write(full_response)

            # Add assistant response to chat history
        st.session_state.messages.append(
            dict(
                role=MODEL_ROLE,
                content=full_response,
                avatar=AI_AVATAR_ICON,
            )
        )

    st.session_state.gemini_history = st.session_state.chat.get_history()
    
    # Save to file
    joblib.dump(
        st.session_state.messages,
        f'data/{st.session_state.chat_id}-st_messages',
    )
    joblib.dump(
        st.session_state.gemini_history,
        f'data/{st.session_state.chat_id}-gemini_messages',
    )


if next_btn:
    st.switch_page("pages/postquiz.py")