import streamlit as st
import pandas as pd
from transformers import BartForConditionalGeneration, TapexTokenizer, T5ForConditionalGeneration, T5Tokenizer
import datetime
import sentencepiece as spm

# Load CSV file
df = pd.read_csv("anomalies.csv", quotechar='"')
df.rename(columns={"ds": "Ano e mês", "real": "Valor Monetário", "Group": "Grupo"}, inplace=True)
df.sort_values(by=['Ano e mês', 'Valor Monetário'], ascending=False, inplace=True)
df = df[df['Valor Monetário'] >= 1000000.]
df['Valor Monetário'] = df['Valor Monetário'].apply(lambda x: f"{x:.2f}")
df = df.fillna('').astype(str)
table_data = df

# Load translation models
pt_en_translator = T5ForConditionalGeneration.from_pretrained("unicamp-dl/translation-pt-en-t5")
en_pt_translator = T5ForConditionalGeneration.from_pretrained("unicamp-dl/translation-en-pt-t5")
tokenizer = T5Tokenizer.from_pretrained("unicamp-dl/translation-pt-en-t5")

# Load TAPEX model
tapex_model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
tapex_tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")

def translate(text, model, tokenizer, source_lang="pt", target_lang="en"):
    input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True)
    outputs = model.generate(input_ids)
    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return translated_text

def response(user_question, table_data):
    # Translate question to English
    question_en = translate(user_question, pt_en_translator, tokenizer, source_lang="pt", target_lang="en")

    # Generate response in English
    encoding = tapex_tokenizer(table=table_data, query=[question_en], padding=True, return_tensors="pt", truncation=True)
    outputs = tapex_model.generate(**encoding)
    response_en = tapex_tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]

    # Translate response to Portuguese
    response_pt = translate(response_en, en_pt_translator, tokenizer, source_lang="en", target_lang="pt")
    return response_pt

# Streamlit interface

st.dataframe(table_data.head())

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)

# Chat history
if 'history' not in st.session_state:
    st.session_state['history'] = []

# Input box for user question
user_question = st.text_input("Escreva sua questão aqui:", "")

if user_question:
    # Add human emoji when user asks a question
    st.session_state['history'].append(('👤', user_question))
    st.markdown(f"**👤 {user_question}**")
    
    # Generate the response
    bot_response = response(user_question, table_data)["Resposta"]
    
    # Add robot emoji when generating response and align to the right
    st.session_state['history'].append(('🤖', bot_response))
    st.markdown(f"<div style='text-align: right'>**🤖 {bot_response}**</div>", unsafe_allow_html=True)

# Clear history button
if st.button("Limpar"):
    st.session_state['history'] = []

# Display chat history
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)