# 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 # 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}") # # 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') 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') # import streamlit as st # import numpy as np # import matplotlib.pyplot as plt # # Sample data for multiple plots # data = [(np.linspace(0, 10, 10), np.sin(np.linspace(0, 10, 10))) for _ in range(3)] # # Initialize session state # if 'current_plot_index' not in st.session_state: # st.session_state['current_plot_index'] = 0 # def update_plot(index, x, y): # plt.figure() # plt.plot(x, y, marker='o') # plt.title(f"Plot {index+1}") # st.pyplot(plt) # def next_plot(): # st.session_state['current_plot_index'] = (st.session_state['current_plot_index'] + 1) % len(data) # # Display the plot # index = st.session_state['current_plot_index'] # x, y = data[index] # update_plot(index, x, y) # # Select and update point # point_index = st.number_input('Point Index', min_value=0, max_value=len(x)-1, step=1) # new_value = st.number_input('New Y Value', value=y[point_index]) # if st.button('Update Point'): # y[point_index] = new_value # update_plot(index, x, y) # # Next plot button # if st.button('Next Plot'): # next_plot() # 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)