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import streamlit as st
import pandas as pd
from transformers import BartForConditionalGeneration, TapexTokenizer, T5ForConditionalGeneration, T5Tokenizer
import datetime
import sentencepiece as spm
# File upload interface
uploaded_file = st.file_uploader("Upload a CSV or XLSX file", type=['csv', 'xlsx'])
if uploaded_file:
# Load the file into a DataFrame
if uploaded_file.name.endswith('.csv'):
df = pd.read_csv(uploaded_file, quotechar='"', encoding='utf-8')
elif uploaded_file.name.endswith('.xlsx'):
df = pd.read_excel(uploaded_file)
df.rename(columns={"ds": "datetime", "real": "monetary value", "Explicação": "explanation"}, inplace=True)
df.sort_values(by=['datetime', 'monetary value'], ascending=False, inplace=True)
df = df[df['monetary value'] >= 10000000.]
df['monetary value'] = df['monetary value'].apply(lambda x: f"{x:.2f}")
df = df.fillna('').astype(str)
table_data = df
# Display the uploaded table
st.dataframe(table_data.head())
# 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):
# Traduz a pergunta para o inglês
question_en = translate(user_question, pt_en_translator, tokenizer, source_lang="pt", target_lang="en")
print(question_en)
# Gera a resposta em inglês
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]
print(response_en)
# Traduz a resposta para o português
response_pt = translate(response_en, en_pt_translator, tokenizer, source_lang="en", target_lang="pt")
return response_pt
# Streamlit interface
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)
# 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)
else:
st.warning("Carregue um arquivo CSV ou XLSX para começar.")
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