import streamlit as st
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
model = AutoModelForCausalLM.from_pretrained("distilgpt2", torch_dtype=torch.float16)
model = model.to('cuda') if torch.cuda.is_available() else model.to('cpu')

# Set the padding token to the end-of-sequence token
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

df = pd.read_csv('anomalies.csv')

# Função para gerar resposta
def response(question):
    prompt = f"Considerando os dados: {df.to_string(index=False)}, onde 'ds' está em formato DateTime, 'real' é o valor da despesa e 'group' é o grupo da despesa. Pergunta: {question}"
    inputs = tokenizer(prompt, return_tensors='pt', padding='max_length', truncation=True, max_length=256)
    attention_mask = inputs['attention_mask']
    input_ids = inputs['input_ids']
    
    generated_ids = model.generate(
        input_ids,
        attention_mask=attention_mask,
        max_length=len(input_ids[0]) + 50,  # Reduce max_length to speed up response
        temperature=0.7,
        top_p=0.9,
        no_repeat_ngram_size=2,
        num_beams=3,  # Adding beams for more reliable generation
    )
    
    generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
    final_response = generated_text.split("Resposta:")[-1].split(".")[0] + "."
    
    return final_response

# Interface Streamlit
st.markdown("""
<div style='display: flex; align-items: center;'>
    <div style='width: 40px; height: 40px; background-color: green; border-radius: 50%; margin-right: 5px;'></div>
    <div style='width: 40px; height: 40px; background-color: red; border-radius: 50%; margin-right: 5px;'></div>
    <div style='width: 40px; height: 40px; background-color: yellow; border-radius: 50%; margin-right: 5px;'></div>
    <span style='font-size: 40px; font-weight: bold;'>Chatbot do Tesouro RS</span>
</div>
""", unsafe_allow_html=True)

# Histórico de conversas
if 'history' not in st.session_state:
    st.session_state['history'] = []

# Caixa de entrada para a pergunta
user_question = st.text_input("Escreva sua questão aqui:", "")

if user_question:
    # Adiciona emoji de pessoa quando a pergunta está sendo digitada
    st.session_state['history'].append(('👤', user_question))
    st.markdown(f"**👤 {user_question}**")
    
    # Gera a resposta
    bot_response = response(user_question)
    
    # Adiciona emoji de robô quando a resposta está sendo gerada e alinha à direita
    st.session_state['history'].append(('🤖', bot_response))
    st.markdown(f"<div style='text-align: right'>**🤖 {bot_response}**</div>", unsafe_allow_html=True)

# Botão para limpar o histórico
if st.button("Limpar"):
    st.session_state['history'] = []

# Exibe o histórico de conversas
for sender, message in st.session_state['history']:
    if sender == '👤':
        st.markdown(f"**👤 {message}**")
    elif sender == '🤖':
        st.markdown(f"<div style='text-align: right'>**🤖 {message}**</div>", unsafe_allow_html=True)