Update app.py

app.py

@@ -1,125 +1,125 @@
-# import streamlit as st
-# import pandas as pd
-# from io import BytesIO
-# from itertools import product
-# from statsmodels.tsa.statespace.sarimax import SARIMAX
-# import plotly.express as px
+import streamlit as st
+import pandas as pd
+from io import BytesIO
+from itertools import product
+from statsmodels.tsa.statespace.sarimax import SARIMAX
+import plotly.express as px
 
-# st.set_page_config(layout="wide")
+st.set_page_config(layout="wide")
 
-# # Function to run the SARIMAX Model
-# def run_sarimax(city_data, order, seasonal_order):
-#     try:
-#         # Check if the data is non-empty and in the correct format
-#         if city_data.empty:
-#             st.error(f"No data available for modeling.")
-#             return None, None
+# Function to run the SARIMAX Model
+def run_sarimax(city_data, order, seasonal_order):
+    try:
+        # Check if the data is non-empty and in the correct format
+        if city_data.empty:
+            st.error(f"No data available for modeling.")
+            return None, None
 
-#         model = SARIMAX(city_data, order=order, seasonal_order=seasonal_order, enforce_stationarity=False, enforce_invertibility=False)
-#         model_fit = model.fit(disp=False)
-#         forecast = model_fit.forecast(steps=6)
+        model = SARIMAX(city_data, order=order, seasonal_order=seasonal_order, enforce_stationarity=False, enforce_invertibility=False)
+        model_fit = model.fit(disp=False)
+        forecast = model_fit.forecast(steps=6)
         
-#         # Check if the forecast is valid
-#         if forecast is None or forecast.empty:
-#             st.error(f"Forecast failed, the model returned an empty forecast.")
-#             return None, None
+        # Check if the forecast is valid
+        if forecast is None or forecast.empty:
+            st.error(f"Forecast failed, the model returned an empty forecast.")
+            return None, None
         
-#         return forecast, model_fit.aic
-#     except Exception as e:
-#         st.error(f"An error occurred during model fitting: {e}")
-#         return None, None
-
-# def create_data():
-#     # Assuming you have a CSV file named 'accident_count.csv' with 'City' and 'Accident Count' columns
-#     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
-
-# # Initialize session state for best parameters
-# if 'best_params' not in st.session_state:
-#     st.session_state.best_params = {'order': (1, 1, 1), 'seasonal_order': (1, 1, 1, 12)}
-
-# st.title("SARIMAX Forecasting")
-
-# # Data preparation
-# data = create_data()
-# unique_cities = data['City'].unique()
-
-# # Creating tabs for each city
-# tabs = st.tabs([city for city in unique_cities])
-
-# for tab, city in zip(tabs, unique_cities):
-#     with tab:
-#         # SARIMAX specific sliders
-#         p = st.slider('AR Order (p)', 0, 5, value=st.session_state.best_params['order'][0], key=city+'p')
-#         d = st.slider('Differencing Order (d)', 0, 2, value=st.session_state.best_params['order'][1], key=city+'d')
-#         q = st.slider('MA Order (q)', 0, 5, value=st.session_state.best_params['order'][2], key=city+'q')
-#         P = st.slider('Seasonal AR Order (P)', 0, 5, value=st.session_state.best_params['seasonal_order'][0], key=city+'P')
-#         D = st.slider('Seasonal Differencing Order (D)', 0, 2, value=st.session_state.best_params['seasonal_order'][1], key=city+'D')
-#         Q = st.slider('Seasonal MA Order (Q)', 0, 5, value=st.session_state.best_params['seasonal_order'][2], key=city+'Q')
-#         S = st.slider('Seasonal Period (S)', 1, 24, value=st.session_state.best_params['seasonal_order'][3], key=city+'S')
-
-#         city_data = data[data['City'] == city]['Accident Count']
-#         forecast, aic = run_sarimax(city_data, (p, d, q), (P, D, Q, S))
-
-#         if forecast is not None:
-#             st.write(f"Best Parameters with AIC: {aic}")
-#             st.write(f"Non-Seasonal Order: {(p, d, q)}, Seasonal Order: {(P, D, Q, S)}")
-#             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=['predicted_mean'])
-#             forecast_df = forecast_df.round(0)
-#             st.table(forecast_df)
-#             fig = px.line(forecast_df, x=forecast_df.index, y="predicted_mean")
-#             st.plotly_chart(fig)
-
-#             # Grid search button
-#             if st.button(f'Run Grid Search for {city}'):
-#                 best_aic = float('inf')
-#                 best_params = None
-#                 # Define the range for each parameter
-#                 p_range = d_range = q_range = range(3)
-#                 P_range = D_range = Q_range = range(3)
-#                 S = 12  # Assuming a fixed seasonal period, adjust as needed
+        return forecast, model_fit.aic
+    except Exception as e:
+        st.error(f"An error occurred during model fitting: {e}")
+        return None, None
+
+def create_data():
+    # Assuming you have a CSV file named 'accident_count.csv' with 'City' and 'Accident Count' columns
+    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
+
+# Initialize session state for best parameters
+if 'best_params' not in st.session_state:
+    st.session_state.best_params = {'order': (1, 1, 1), 'seasonal_order': (1, 1, 1, 12)}
+
+st.title("SARIMAX Forecasting")
+
+# Data preparation
+data = create_data()
+unique_cities = data['City'].unique()
+
+# Creating tabs for each city
+tabs = st.tabs([city for city in unique_cities])
+
+for tab, city in zip(tabs, unique_cities):
+    with tab:
+        # SARIMAX specific sliders
+        p = st.slider('AR Order (p)', 0, 5, value=st.session_state.best_params['order'][0], key=city+'p')
+        d = st.slider('Differencing Order (d)', 0, 2, value=st.session_state.best_params['order'][1], key=city+'d')
+        q = st.slider('MA Order (q)', 0, 5, value=st.session_state.best_params['order'][2], key=city+'q')
+        P = st.slider('Seasonal AR Order (P)', 0, 5, value=st.session_state.best_params['seasonal_order'][0], key=city+'P')
+        D = st.slider('Seasonal Differencing Order (D)', 0, 2, value=st.session_state.best_params['seasonal_order'][1], key=city+'D')
+        Q = st.slider('Seasonal MA Order (Q)', 0, 5, value=st.session_state.best_params['seasonal_order'][2], key=city+'Q')
+        S = st.slider('Seasonal Period (S)', 1, 24, value=st.session_state.best_params['seasonal_order'][3], key=city+'S')
+
+        city_data = data[data['City'] == city]['Accident Count']
+        forecast, aic = run_sarimax(city_data, (p, d, q), (P, D, Q, S))
+
+        if forecast is not None:
+            st.write(f"Best Parameters with AIC: {aic}")
+            st.write(f"Non-Seasonal Order: {(p, d, q)}, Seasonal Order: {(P, D, Q, S)}")
+            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=['predicted_mean'])
+            forecast_df = forecast_df.round(0)
+            st.table(forecast_df)
+            fig = px.line(forecast_df, x=forecast_df.index, y="predicted_mean")
+            st.plotly_chart(fig)
+
+            # Grid search button
+            if st.button(f'Run Grid Search for {city}'):
+                best_aic = float('inf')
+                best_params = None
+                # Define the range for each parameter
+                p_range = d_range = q_range = range(3)
+                P_range = D_range = Q_range = range(3)
+                S = 12  # Assuming a fixed seasonal period, adjust as needed
             
-#                 # Perform the grid search
-#                 for params in product(p_range, d_range, q_range, P_range, D_range, Q_range):
-#                     order = params[:3]
-#                     seasonal_order = params[3:] + (S,)
-#                     try:
-#                         _, temp_aic = run_sarimax(city_data, order, seasonal_order)
-#                         if temp_aic < best_aic:
-#                             best_aic = temp_aic
-#                             best_params = (order, seasonal_order)
-#                     except Exception as e:
-#                         st.error(f"An error occurred for parameters {params}: {e}")
+                # Perform the grid search
+                for params in product(p_range, d_range, q_range, P_range, D_range, Q_range):
+                    order = params[:3]
+                    seasonal_order = params[3:] + (S,)
+                    try:
+                        _, temp_aic = run_sarimax(city_data, order, seasonal_order)
+                        if temp_aic < best_aic:
+                            best_aic = temp_aic
+                            best_params = (order, seasonal_order)
+                    except Exception as e:
+                        st.error(f"An error occurred for parameters {params}: {e}")
             
-#                 # Update the session state with the best parameters
-#                 if best_params is not None:
-#                     st.session_state.best_params = {
-#                         'order': best_params[0],
-#                         'seasonal_order': best_params[1]
-#                     }
-#                     st.write(f"Best Parameters for {city}: {best_params} with AIC: {best_aic}")
+                # Update the session state with the best parameters
+                if best_params is not None:
+                    st.session_state.best_params = {
+                        'order': best_params[0],
+                        'seasonal_order': best_params[1]
+                    }
+                    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')
+        # 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')
 
 
 
@@ -190,26 +190,26 @@
 
 
 
-import plotly.express as px
-import streamlit as st
-from streamlit_plotly_events import plotly_events
-
-# Sample data
-df = px.data.gapminder().query("country=='Canada'")
-fig = px.line(df, x="year", y="lifeExp", title='Life Expectancy in Canada Over Years')
-
-# Capture the selected points
-selected_points = plotly_events(fig, click_event=True)
-
-# Handle the click event
-if selected_points:
-    st.write("You clicked on:", selected_points)
-    point_index = selected_points[0]['pointIndex']
-    new_value = st.number_input('Enter new value for life expectancy', value=df.iloc[point_index]['lifeExp'])
-    if st.button('Update Data'):
-        df.at[point_index, 'lifeExp'] = new_value
-        fig = px.line(df, x="year", y="lifeExp", title='Life Expectancy in Canada Over Years')
-        st.plotly_chart(fig)
-else:
-    st.plotly_chart(fig)
+# import plotly.express as px
+# import streamlit as st
+# from streamlit_plotly_events import plotly_events
+
+# # Sample data
+# df = px.data.gapminder().query("country=='Canada'")
+# fig = px.line(df, x="year", y="lifeExp", title='Life Expectancy in Canada Over Years')
+
+# # Capture the selected points
+# selected_points = plotly_events(fig, click_event=True)
+
+# # Handle the click event
+# if selected_points:
+#     st.write("You clicked on:", selected_points)
+#     point_index = selected_points[0]['pointIndex']
+#     new_value = st.number_input('Enter new value for life expectancy', value=df.iloc[point_index]['lifeExp'])
+#     if st.button('Update Data'):
+#         df.at[point_index, 'lifeExp'] = new_value
+#         fig = px.line(df, x="year", y="lifeExp", title='Life Expectancy in Canada Over Years')
+#         st.plotly_chart(fig)
+# else:
+#     st.plotly_chart(fig)