import streamlit as st
import uuid
import sys
import requests
from peft import *
import bitsandbytes as bnb
import pandas as pd
import torch
import torch.nn as nn
import transformers
from datasets import load_dataset
from huggingface_hub import notebook_login
from peft import (
    LoraConfig,
    PeftConfig,
    get_peft_model,
    prepare_model_for_kbit_training,
)
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)
import pickle

USER_ICON = "images/user-icon.png"
AI_ICON = "images/ai-icon.png"
MAX_HISTORY_LENGTH = 5

if 'user_id' in st.session_state:
    user_id = st.session_state['user_id']
else:
    user_id = str(uuid.uuid4())
    st.session_state['user_id'] = user_id

if 'chat_history' not in st.session_state:
    st.session_state['chat_history'] = []

if "chats" not in st.session_state:
    st.session_state.chats = [
        {
            'id': 0,
            'question': '',
            'answer': ''
        }
    ]

if "questions" not in st.session_state:
    st.session_state.questions = []

if "answers" not in st.session_state:
    st.session_state.answers = []

if "input" not in st.session_state:
    st.session_state.input = ""

st.markdown("""
        <style>
               .block-container {
                    padding-top: 32px;
                    padding-bottom: 32px;
                    padding-left: 0;
                    padding-right: 0;
                }
                .element-container img {
                    background-color: #000000;
                }

                .main-header {
                    font-size: 24px;
                }
        </style>
        """, unsafe_allow_html=True)

# Load the model and tokenizer from Hugging Face Hub
model_name = "tiiuae/falcon-7b-instruct" 
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load the dataset
dataset = load_dataset("nisaar/Lawyer_GPT_India")  

def write_top_bar():
    col1, col2, col3 = st.columns([1,10,2])
    with col1:
        st.image(AI_ICON, use_column_width='always')
    with col2:
        header = "Cogwise Intelligent Assistant"
        st.write(f"<h3 class='main-header'>{header}</h3>", unsafe_allow_html=True)
    with col3:
        clear = st.button("Clear Chat")
    return clear

clear = write_top_bar()

if clear:
    st.session_state.questions = []
    st.session_state.answers = []
    st.session_state.input = ""
    st.session_state["chat_history"] = []

def handle_input():
    input = st.session_state.input
    question_with_id = {
        'question': input,
        'id': len(st.session_state.questions)
    }
    st.session_state.questions.append(question_with_id)

    chat_history = st.session_state["chat_history"]
    if len(chat_history) == MAX_HISTORY_LENGTH:
        chat_history = chat_history[:-1]

    # Find the most similar example in the dataset
    closest_example = find_closest_example(input, dataset)  # Implement your own logic to find the closest example

    # Generate response using the model
    inputs = tokenizer.encode(closest_example, return_tensors="pt")
    outputs = model.generate(inputs)
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)

    chat_history.append((input, answer))

    st.session_state.answers.append({
        'answer': answer,
        'id': len(st.session_state.questions)
    })
    st.session_state.input = ""

def find_closest_example(input, dataset):
    # Implement your own logic to find the closest example in the dataset based on the user input
    # You can use techniques like cosine similarity, semantic similarity, or any other approach that fits your dataset and requirements
    # Return the closest example as a string
    pass

def write_user_message(md):
    col1, col2 = st.columns([1,12])

    with col1:
        st.image(USER_ICON, use_column_width='always')
    with col2:
        st.warning(md['question'])

def render_answer(answer):
    col1, col2 = st.columns([1,12])
    with col1:
        st.image(AI_ICON, use_column_width='always')
    with col2:
        st.info(answer)

def write_chat_message(md, q):
    chat = st.container()
    with chat:
        render_answer(md['answer'])

with st.container():
    for (q, a) in zip(st.session_state.questions, st.session_state.answers):
        write_user_message(q)
        write_chat_message(a, q)

st.markdown('---')
input = st.text_input("You are talking to an AI, ask any question.", key="input", on_change=handle_input)