app.py

import streamlit as st
import pandas as pd
import torch
from transformers import pipeline
import datetime
from rapidfuzz import process, fuzz

# Load the CSV file
df = pd.read_csv("anomalies.csv", quotechar='"')

# Convert 'real' column to standard float format and then to strings
df['real'] = df['real'].apply(lambda x: f"{x:.2f}")

# Fill NaN values and convert all columns to strings
df = df.fillna('').astype(str)

# Function to filter the DataFrame using RapidFuzz
def filter_dataframe(df, date_str, group_keyword, threshold=80):
    # Apply fuzzy matching on the 'ds' (date) and 'Group' columns
    date_matches = process.extract(date_str, df['ds'], scorer=fuzz.token_sort_ratio, limit=None)
    group_matches = process.extract(group_keyword, df['Group'], scorer=fuzz.token_sort_ratio, limit=None)

    # Get the indices that match both criteria
    date_indices = {match[2] for match in date_matches if match[1] >= threshold}
    group_indices = {match[2] for match in group_matches if match[1] >= threshold}
    common_indices = list(date_indices & group_indices)

    return df.iloc[common_indices]

# Function to generate a response using the TAPAS model
def response(user_question, df):
    a = datetime.datetime.now()

    # Extract date and group keywords from the user question
    date_str = "December 2022"  # Example; you'd extract this from the user question dynamically
    group_keyword = "IPVA"

    # Filter the DataFrame by date and group
    subset_df = filter_dataframe(df, date_str, group_keyword)

    # Check if the DataFrame is empty
    if subset_df.empty:
        return {"Resposta": "Desculpe, não há dados disponíveis para responder à sua pergunta."}

    # Initialize the TAPAS model
    tqa = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq", 
                   tokenizer_kwargs={"clean_up_tokenization_spaces": False})

    # Debugging information
    print("Filtered DataFrame shape:", subset_df.shape)
    print("Filtered DataFrame head:\n", subset_df.head())
    print("User question:", user_question)

    # Query the TAPAS model
    try:
        answer = tqa(table=subset_df, query=user_question)['answer']
    except ValueError as e:
        print(f"Error: {e}")
        answer = "Desculpe, ocorreu um erro ao processar sua pergunta."

    query_result = {
        "Resposta": answer
    }

    b = datetime.datetime.now()
    print("Time taken:", 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, df)["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)