# 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='π₯ 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) | |