app.py

import pandas as pd
from io import BytesIO
import streamlit as st
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.statespace.sarimax import SARIMAX
from itertools import product
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

# Function to run SARIMAX Model
def run_sarimax(city_data, order, seasonal_order):
    try:
        model = SARIMAX(city_data, order=order, seasonal_order=seasonal_order)
        model_fit = model.fit(disp=False)
        return model_fit.forecast(steps=6), model_fit.aic
    except Exception as e:
        st.error(f"An error occurred during SARIMAX 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.index = data['Accident Month Bracket']
    data = data.drop(['Accident Month Bracket'],axis=1)
    data.index = data.index.strftime('%Y-%m')
    return data

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

if 'best_params' not in st.session_state:
    st.session_state.best_params = {'trend': None, 'damped_trend': False, 'seasonal': None, 'seasonal_period': 12, 'model_type': 'ExpSmoothing'}

st.title("Exponential Smoothing and SARIMAX Forecasting")

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

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

model_type = st.selectbox('Select Model Type', ['ExpSmoothing', 'SARIMAX'])

if model_type == 'ExpSmoothing':
    trend = st.select_slider('Select Trend', options=['add', 'mul', None], value=st.session_state.best_params['trend'])
    damped_trend = st.checkbox('Damped Trend', value=st.session_state.best_params['damped_trend'])
    seasonal = st.select_slider('Select Seasonal', options=['add', 'mul', None], value=st.session_state.best_params['seasonal'])
    seasonal_period = st.slider('Seasonal Period', 1, 24, value=st.session_state.best_params['seasonal_period'])
elif model_type == 'SARIMAX':
    p = st.slider('AR Order (p)', 0, 5, 0)
    d = st.slider('Differencing (d)', 0, 2, 1)
    q = st.slider('MA Order (q)', 0, 5, 0)
    P = st.slider('Seasonal AR Order (P)', 0, 2, 0)
    D = st.slider('Seasonal Differencing (D)', 0, 2, 1)
    Q = st.slider('Seasonal MA Order (Q)', 0, 2, 0)
    S = st.slider('Seasonal Period (S)', 1, 24, 12)

city_data = data[data['City'] == selected_city]['Accident Count']

if model_type == 'ExpSmoothing':
    forecast, aic = run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period)
elif model_type == 'SARIMAX':
    order = (p, d, q)
    seasonal_order = (P, D, Q, S)
    forecast, aic = run_sarimax(city_data, order, seasonal_order)

if forecast is not None:
    st.write(f"Best Parameters with AIC: {aic}")
    st.write(f"Forecast:")
    forecast_index = pd.date_range(start=city_data.index[-1], periods=7, freq='M')[1:]
    forecast_index = forecast_index.to_period('M')
    forecast_df = pd.DataFrame(forecast, columns=['Forecast'])
    forecast_df = forecast_df.round(0)
    st.table(forecast_df)
    fig = px.line(forecast_df, x=forecast_df.index, y="Forecast")
    st.plotly_chart(fig)

# Grid Search Logic for Both Models
if st.button('Run Grid Search'):
    best_aic = float('inf')
    best_params = None

    if model_type == 'ExpSmoothing':
        for param_set in product(['add', 'mul', None], [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
    elif model_type == 'SARIMAX':
        for param_set in product(range(3), range(2), range(3), range(2), range(2), range(2), [12]):
            _, temp_aic = run_sarimax(city_data, param_set[:3], param_set[3:])
            if temp_aic and temp_aic < best_aic:
                best_aic = temp_aic
                best_params = param_set

    st.session_state.best_params = best_params
    st.write(f"Best Parameters: {best_params} with AIC: {best_aic}")

if st.button('Export to Excel'):
    df_to_export = forecast_df
    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')