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
from utils import calc_seasonality

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 = int(st.text_input("Write the number of horizons"))
     seasonality = calc_seasonality(freq)
                        
     st.write(f'Seasonality: {seasonality}')              
     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, seasonality), (seasonal_naive, seasonality)], 
                               freq=freq, 
                               n_jobs=1
                              )
          forecasts = fcst.forecast(horizons, level=(80, 95))
          st.write(forecasts)
          fig.add_scatter(x=forecasts['ds'], y=forecasts[f'auto_arima_season_length-{seasonality}_mean'], mode='lines')
          fig.update_traces(
                             name=f'auto_arima_season_length-{seasonality}_mean',
                             line=dict(color='blue', width=3),
                             showlegend=True
          )
          fig.add_scatter(x=forecasts['ds'], y=forecasts[f'seasonal_naive_season_length-{seasonality}'], mode='lines')
          fig.update_traces(
                             name=f'seasonal_naive_season_length-{seasonality}',
                             line=dict(color='blue', width=3),
                             showlegend=True
          )
          st.plotly_chart(fig)