app.py

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

# Caminho para o arquivo CSS, ajuste conforme a estrutura do seu projeto
css_file = "style.css"

# Abrindo e lendo o arquivo CSS
with open(css_file, "r") as css:
    css_style = css.read()

# Markdown combinado com a importação da fonte e o HTML
html_content = f"""
<style>
{css_style}
@import url('https://fonts.googleapis.com/css2?family=Kanit:wght@700&display=swap');
</style>
<div style='display: flex; flex-direction: column; align-items: flex-start;'>
    <div style='display: flex; align-items: center;'>
        <div style='width: 20px; height: 40px; background-color: green; margin-right: 1px;'></div>
        <div style='width: 20px; height: 40px; background-color: red; margin-right: 1px;'></div>
        <div style='width: 20px; height: 40px; background-color: yellow; margin-right: 20px;'></div>
        <span style='font-size: 50px; font-weight: normal; font-family: "Kanit", sans-serif;'>NOSTRADAMUS</span>
    </div>
    <div style='text-align: left; width: 100%;'>
        <span style='font-size: 20px; font-weight: normal; color: #333; font-family: "Kanit", sans-serif'>
        Meta Prophet + Microsoft TAPEX</span>
    </div>
</div>
"""

# Aplicar o markdown combinado no Streamlit
st.markdown(html_content, unsafe_allow_html=True)

# Inicialização de variáveis de estado
if 'all_anomalies' not in st.session_state:
    st.session_state['all_anomalies'] = pd.DataFrame()
if 'history' not in st.session_state:
    st.session_state['history'] = []

# Carregar os modelos de tradução e TAPEX
pt_en_translator = T5ForConditionalGeneration.from_pretrained("unicamp-dl/translation-pt-en-t5")
en_pt_translator = T5ForConditionalGeneration.from_pretrained("unicamp-dl/translation-en-pt-t5")
tapex_model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
tapex_tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
tokenizer = T5Tokenizer.from_pretrained("unicamp-dl/translation-pt-en-t5")

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

def load_data(uploaded_file):
    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)
    return df

def preprocess_data(df):
    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()
    return df_clean

def apply_prophet(df_clean):
    # Criar um DataFrame vazio para armazenar todas as anomalias
    all_anomalies = pd.DataFrame()

    # Processar cada linha no 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"Pulando grupo {row['Rotulo']} porque há menos de 2 observações não nulas.")
            continue

        try:
            model = Prophet(interval_width=0.95)
            model.fit(data)
        except ValueError as e:
            print(f"Pulando grupo {row['Rotulo']} devido a erro: {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)

    # Renomear colunas e aplicar filtros
    all_anomalies.rename(columns={"ds": "datetime", "real": "monetary value", "Group": "group"}, inplace=True)
    all_anomalies = all_anomalies[all_anomalies['monetary value'].astype(float) >= 10000000.00]
    all_anomalies['monetary value'] = all_anomalies['monetary value'].apply(lambda x: f"{x:.2f}")
    all_anomalies.sort_values(by=['monetary value'], ascending=False, inplace=True)
    all_anomalies = all_anomalies.fillna('').astype(str)

    return all_anomalies

# Interface para carregar arquivo
uploaded_file = st.file_uploader("Carregue um arquivo CSV ou XLSX", type=['csv', 'xlsx'])
if uploaded_file:
    if 'all_anomalies' not in st.session_state:
        df = load_data(uploaded_file)
        df = preprocess_data(df)
        with st.spinner('Aplicando modelo de série temporal...'):
            all_anomalies = apply_prophet(df)
            st.session_state['all_anomalies'] = all_anomalies
        st.session_state['all_anomalies'] = all_anomalies

# Interface para perguntas do usuário
user_question = st.text_input("Escreva sua questão aqui:", "")
if user_question:
    bot_response = response(user_question, st.session_state['all_anomalies'])
    st.session_state['history'].append(('👤', user_question))
    st.session_state['history'].append(('🤖', bot_response))

# Mostrar histórico de conversa
for sender, message in st.session_state['history']:
    if sender == '👤':
        st.markdown(f"**👤 {message}**")
    elif sender == '🤖':
        st.markdown(f"**🤖 {message}**", unsafe_allow_html=True)

# Botão para limpar histórico
if st.button("Limpar histórico"):
    st.session_state['history'] = []