app.py

import streamlit as st
import pandas as pd
from statsmodels.tsa.holtwinters import ExponentialSmoothing
import numpy as np
from itertools import product
from io import BytesIO
import plotly.express as px

# Function to run the Exponential Smoothing Model
def run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period):
    try:
        model = ExponentialSmoothing(city_data, trend=trend, damped_trend=damped_trend, seasonal=seasonal, seasonal_periods=seasonal_period)
        model_fit = model.fit(optimized=True)
        return model_fit.forecast(steps=6), model_fit.aic
    except Exception as e:
        st.error(f"An error occurred during model fitting: {e}")
        return None, None

def create_data():
  data = pd.read_csv('accident_count.csv', parse_dates=True, index_col=0)
  data.index = pd.to_datetime(data.index, format='%Y%m')
  data = data.groupby('City').resample('M').sum().reset_index()
  #data = data[data['City'] == 'ARAR']
  data.index = data['Accident Month Bracket']
  data = data.drop(['Accident Month Bracket'],axis=1)
  data.index = data.index.strftime('%Y-%m')
  return data
    
# Function to convert DataFrame to Excel
def to_excel(df):
    output = BytesIO()
    writer = pd.ExcelWriter(output, engine='xlsxwriter')
    df.to_excel(writer, sheet_name='Sheet1')
    writer.save()
    processed_data = output.getvalue()
    return processed_data


st.title("Exponential Smoothing Forecasting")
# Grid search button
if st.button('Run Grid Search'):
    best_aic = float('inf')
    best_params = None
    for param_set in product(['add', 'mul', None], [True, False], ['add', 'mul', None], [12]):
        _, temp_aic = run_exp_smoothing(city_data, *param_set)
        if temp_aic and temp_aic < best_aic:
            best_aic = temp_aic
            best_params = param_set
    st.write(f"Best Parameters: {best_params} with AIC: {best_aic}")
# Upload Data Section
# uploaded_file = st.file_uploader("Choose a file")
# if uploaded_file is not None:

data = create_data()
unique_cities = data['City'].unique()

# Select a city
selected_city = st.selectbox('Select a City', unique_cities)

# Sliders for parameter adjustment
trend = st.select_slider('Select Trend', options=['add', 'mul', None])
damped_trend = st.checkbox('Damped Trend')
seasonal = st.select_slider('Select Seasonal', options=['add', 'mul', None])
seasonal_period = st.slider('Seasonal Period', 1, 24, 12)

# ... [previous code remains the same]

# Display forecast with current parameters
city_data = data[data['City'] == selected_city]['Accident Count']
forecast, aic = run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period)

if forecast is not None:
    st.write(f"Best Parameters with AIC: {aic}")
    st.write(f"Trend: {trend}, Damped Trend: {damped_trend}, Seasonal: {seasonal}, Seasonal Period: {seasonal_period}")
    forecast_index = pd.date_range(start=city_data.index[-1], periods=7, freq='M')[1:]
    forecast_index = forecast_index.to_period('M')  # Convert to period index with monthly frequency
    # forecast_df = pd.DataFrame(forecast, index=forecast_index, columns=['Forecast'])
    forecast_df = pd.DataFrame(forecast,columns=['Forecast'])
    # Ensure the index is correctly formatted as 'YYYY-MM'
    # forecast_df.index = forecast_df.index.strftime('%Y-%m')
    st.table(forecast_df)
    fig = px.line(forecast_df, x=forecast_df.index, y="Forecast")
    st.plotly_chart(fig)


# Export to Excel button
# if st.button('Export to Excel'):
#     df_to_export = pd.DataFrame(forecast)
#     excel_data = to_excel(df_to_export)
#     st.download_button(label='📥 Download Excel', data=excel_data, file_name='forecast.xlsx', mime='application/vnd.ms-excel')