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
# 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"""
"""
# Aplicar o markdown combinado no Streamlit
st.markdown(html_content, unsafe_allow_html=True)
# File upload interface
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:
with st.spinner('Aplicando modelo de série temporal...'):
# 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)
# Store the result in session state
all_anomalies.rename(columns={"ds": "datetime", "real": "monetary value", "Group": "group"}, inplace=True)
all_anomalies = all_anomalies[all_anomalies['monetary value'].astype('float') >= 10,000,000.00]
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)
st.session_state['all_anomalies'] = all_anomalies
# 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(st.session_state['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, st.session_state['all_anomalies'])
st.session_state['history'].append(('🤖', bot_response))
st.markdown(f"**🤖 {bot_response}**
", 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"**🤖 {message}**
", unsafe_allow_html=True)
else:
st.warning("Por favor, carregue um arquivo CSV ou XLSX para começar.")