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import streamlit as st
import pandas as pd
import torch
from transformers import TapexTokenizer, BartForConditionalGeneration
import datetime
# Load the data
df = pd.read_excel('discrepantes.xlsx')
df.fillna(0, inplace=True)
table_data = df.astype(str)
print(table_data.head())
# Function to generate a response using the TAPEX model
def response(user_question, table_data):
a = datetime.datetime.now()
model_name = "microsoft/tapex-large-finetuned-wtq"
model = BartForConditionalGeneration.from_pretrained(model_name)
tokenizer = TapexTokenizer.from_pretrained(model_name)
queries = [user_question]
encoding = tokenizer(table=table_data, query=queries, padding=True, return_tensors="pt", truncation=True)
# Experiment with generation parameters
outputs = model.generate(
**encoding,
#num_beams=5, # Beam search to generate more diverse responses
#top_k=50, # Top-k sampling for diversity
#top_p=0.95, # Nucleus sampling
#temperature=0.7, # Temperature scaling (if supported by the model)
#max_length=50, # Limit the length of the generated response
#early_stopping=True # Stop generation when an end token is generated
)
ans = tokenizer.batch_decode(outputs, skip_special_tokens=True)
query_result = {
"Resposta": ans[0]
}
b = datetime.datetime.now()
print(b - a)
return query_result
# 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)["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) |