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

# Initialize session state for best parameters
if 'best_params' not in st.session_state:
    st.session_state.best_params = {'trend': None, 'damped_trend': False, 'seasonal': None, 'seasonal_period': 12}

st.title("Exponential Smoothing Forecasting")

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

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

# Sliders for parameter adjustment, using session state values as defaults
trend = st.select_slider('Select Trend', options=['add', 'mul', None], value=st.session_state.best_params['trend'])
damped_trend = False#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'])

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, 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 button
if st.button('Run Grid Search'):
    best_aic = float('inf')
    best_params = None
    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

    # Updating session state with the best parameters
    st.session_state.best_params = {
        'trend': best_params[0],
        'damped_trend': best_params[1],
        'seasonal': best_params[2],
        'seasonal_period': best_params[3]
    }
    st.write(f"Best Parameters: {best_params} with AIC: {best_aic}")

    
# Export to Excel button
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')