Update app.py

app.py

@@ -1,9 +1,8 @@
 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
+from itertools import product
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
 import plotly.express as px
 
 # Function to run the Exponential Smoothing Model
@@ -17,15 +16,14 @@ def run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period)
         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
-    
+    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()
@@ -35,37 +33,25 @@ def to_excel(df):
     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")
-# 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 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
-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)
+# 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 = 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'])
 
-# ... [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)
 
@@ -74,17 +60,26 @@ if forecast is not None:
     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')
+    forecast_df = pd.DataFrame(forecast, columns=['Forecast'])
     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], [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
 
-# 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')
+    # 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}")