import streamlit as st
import pandas as pd
import torch
from transformers import pipeline
#from transformers import TapasTokenizer, TapexTokenizer, BartForConditionalGeneration
from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering
import datetime

#df = pd.read_excel('discrepantes.xlsx', index_col='Unnamed: 0')
df = pd.read_excel('discrepantes.xlsx')
df.fillna(0, inplace=True)
table_data = df.astype(str)
print(table_data.head())

def response(user_question, table_data):
    a = datetime.datetime.now()

    #model_name = "microsoft/tapex-large-finetuned-wtq"
    model_name = ""google/tapas-base-finetuned-wtq""
    #model = BartForConditionalGeneration.from_pretrained(model_name)
    model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
    #tokenizer = TapexTokenizer.from_pretrained(model_name)
    tokenizer = AutoTokenizer.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 person emoji when typing 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)