app.py

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

st.markdown("""
<div style='display: flex; align-items: center;'>
    <div style='width: 20px; height: 20px; background-color: green; border-radius: 50%; margin-right: 2px;'></div>
    <div style='width: 20px; height: 20px; background-color: red; border-radius: 50%; margin-right: 2px;'></div>
    <div style='width: 20px; height: 20px; background-color: yellow; border-radius: 50%; margin-right: 10px;'></div>
    <span style='font-size: 40px; font-weight: bold;'>Chatbot do Tesouro RS</span>
</div>
""", unsafe_allow_html=True)

# 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)

    # Data preprocessing for Prophet
    new_df = df.iloc[2:, 9:-1].fillna(0)
    new_df.columns = df.iloc[1, 9:-1]
    new_df.columns = new_df.columns.str.replace(r" \(\d+\)", "", regex=True)

    month_dict = {
        'Jan': '01', 'Fev': '02', 'Mar': '03', 'Abr': '04',
        'Mai': '05', 'Jun': '06', 'Jul': '07', 'Ago': '08',
        'Set': '09', 'Out': '10', 'Nov': '11', 'Dez': '12'
    }

    def convert_column_name(column_name):
        if column_name == 'Rótulos de Linha':
            return column_name
        parts = column_name.split('/')
        month = parts[0].strip()
        year = parts[1].strip()
        year = ''.join(filter(str.isdigit, year))
        month_number = month_dict.get(month, '00')
        return f"{month_number}/{year}"

    new_df.columns = [convert_column_name(col) for col in new_df.columns]
    new_df.columns = pd.to_datetime(new_df.columns, errors='coerce')
    new_df.rename(columns={new_df.columns[0]: 'Rotulo'}, inplace=True)
    df_clean = new_df.copy()

    # Create an empty DataFrame to store all anomalies
    all_anomalies = pd.DataFrame()

    # Process each row in the DataFrame
    for index, row in df_clean.iterrows():
        data = pd.DataFrame({
            'ds': [col for col in df_clean.columns if isinstance(col, pd.Timestamp)],
            'y': row[[isinstance(col, pd.Timestamp) for col in df_clean.columns]].values
        })

        data = data[data['y'] > 0].reset_index(drop=True)
        if data.empty or len(data) < 2:
            print(f"Skipping group {row['Rotulo']} because there are less than 2 non-zero observations.")
            continue

        try:
            model = Prophet(interval_width=0.95)
            model.fit(data)
        except ValueError as e:
            print(f"Skipping group {row['Rotulo']} due to error: {e}")
            continue

        future = model.make_future_dataframe(periods=12, freq='M')
        forecast = model.predict(future)

        num_real = len(data)
        num_forecast = len(forecast)
        real_values = list(data['y']) + [None] * (num_forecast - num_real)
        forecast['real'] = real_values
        anomalies = forecast[(forecast['real'] < forecast['yhat_lower']) | (forecast['real'] > forecast['yhat_upper'])]

        anomalies['Group'] = row['Rotulo']
        all_anomalies = pd.concat([all_anomalies, anomalies[['ds', 'real', 'Group']]], ignore_index=True)

    # Preparing anomalies DataFrame for TAPEX model
    all_anomalies.rename(columns={"ds": "datetime", "real": "monetary value", "Group": "explanation"}, inplace=True)
    all_anomalies['monetary value'] = all_anomalies['monetary value'].apply(lambda x: f"{x:.2f}")
    all_anomalies = all_anomalies.fillna('').astype(str)

    # 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):
        question_en = translate(user_question, pt_en_translator, tokenizer, source_lang="pt", target_lang="en")
        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]
        response_pt = translate(response_en, en_pt_translator, tokenizer, source_lang="en", target_lang="pt")
        return response_pt

    # Streamlit interface

    st.dataframe(all_anomalies.head())

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

    user_question = st.text_input("Escreva sua questão aqui:", "")

    if user_question:
        st.session_state['history'].append(('👤', user_question))
        st.markdown(f"**👤 {user_question}**")
        
        bot_response = response(user_question, all_anomalies)
        
        st.session_state['history'].append(('🤖', bot_response))
        st.markdown(f"<div style='text-align: right'>**🤖 {bot_response}**</div>", unsafe_allow_html=True)

    if st.button("Limpar"):
        st.session_state['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("Please upload a CSV or XLSX file to start.")