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
-
-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
-
-        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)
+# 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")
+
+# # 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)
         
-        # 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')
 
 
 
@@ -133,6 +133,33 @@ for tab, city in zip(tabs, unique_cities):
 
 
 
+import streamlit as st
+
+def deletefile(filename, dfname):
+    # Your delete file logic here
+    pass
+
+def spawnbutton(filename, dfname):
+    # Check if the button has already been clicked
+    if st.session_state.get(f"{filename}_clicked", False):
+        # Button logic after being clicked (if any)
+        pass
+    else:
+        # Show the button if it hasn't been clicked yet
+        if st.button(f"Delete file ({st.session_state[filename].name})", use_container_width=True, key=f'{filename}_deleter'):
+            deletefile(filename, dfname)
+            statecheck(dfname)
+            statecheck(filename)
+            # Set the state to indicate the button has been clicked
+            st.session_state[f"{filename}_clicked"] = True
+
+# Example usage
+if 'example_file' not in st.session_state:
+    st.session_state['example_file'] = "file.txt"
+if 'example_df' not in st.session_state:
+    st.session_state['example_df'] = "dataframe"
+
+spawnbutton('example_file', 'example_df')