Update app.py

app.py

@@ -1,9 +1,8 @@
+import streamlit as st
 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
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
 import plotly.express as px
 
 # Function to run the Exponential Smoothing Model
@@ -16,16 +15,6 @@ def run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period)
         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')
@@ -35,6 +24,7 @@ def create_data():
     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')
@@ -43,74 +33,62 @@ 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, 'model_type': 'ExpSmoothing'}
+    st.session_state.best_params = {'trend': None, 'damped_trend': False, 'seasonal': None, 'seasonal_period': 12}
 
-st.title("Exponential Smoothing and SARIMAX Forecasting")
+st.title("Exponential Smoothing Forecasting")
 
+# Data preparation
 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')
+# Creating tabs for each city
+tab_dict = {city: st.tab(city) for city in unique_cities}
+for city, tab in tab_dict.items():
+    with tab:
+        # 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
+        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'] == 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(f'Run Grid Search for {city}'):
+            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 for {city}: {best_params} with AIC: {best_aic}")
+
+        
+        # Export to Excel button
+        if st.button(f'Export {city} 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=f'{city}_forecast.xlsx', mime='application/vnd.ms-excel')