app.py

import streamlit as st
import numpy as np
import pandas as pd
import plotly.express as px
from statsforecast import StatsForecast
from statsforecast.models import seasonal_naive, auto_arima

st.title('Forecasting time series')

uploaded_file = st.file_uploader("Choose a CSV file", accept_multiple_files=False)
if uploaded_file is not None:
     dataframe = pd.read_csv(uploaded_file)
     st.write(dataframe)
     series = st.text_input("Write the name of the variable you want to forecast")
     date = st.text_input("Write the first date")
     freq = st.text_input("Write the frequency")
     horizons = st.text_input("Write the number of horizons")
     if series is not None:
          series_train = pd.DataFrame(
                        {
                            'ds': pd.date_range(start=date, periods=dataframe.shape[0], freq=freq),
                            'y': dataframe[series].values
                        },
                        index=pd.Index([0] * dataframe.shape[0], name='unique_id')
          )
              
          fig = px.line(series_train, x='ds', y='y', title=f'{series}')
          st.plotly_chart(fig)
          
          fcst = StatsForecast(
                                  series_train, 
                                  models=[(auto_arima, 12), (seasonal_naive, 12)], 
                                  freq=freq, 
                                  n_jobs=1
                              )
          forecasts = fcst.forecast(horizons, level=(80, 95))