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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| from datetime import datetime | |
| excel_file_name = 'updated_dataset.csv' | |
| # Streamlit title | |
| st.title("Bangladesh Accident Monitoring System (BAMS)") | |
| # Display a note to the user | |
| st.write("Please Note, First Date must be smaller than Last date. Example: First Date = 25-08-2024 and Last Date = 28-08-2024") | |
| # Get today's date | |
| today = datetime.strptime(datetime.today().strftime('%d-%m-%Y'), '%d-%m-%Y') | |
| # Input fields for date range | |
| start = st.date_input("Enter first date", max_value=today, format="DD-MM-YYYY") | |
| start_string = start.strftime('%d-%m-%Y') | |
| end = st.date_input("Enter last date", max_value=today, format="DD-MM-YYYY") | |
| end_string = end.strftime('%d-%m-%Y') | |
| # Button to generate dataset based on date range | |
| if st.button("Generate Dataset"): | |
| # Read the selected excel file | |
| df3 = pd.read_csv(excel_file_name) | |
| # Convert 'Publish Date' column to datetime with 'day-month-year' format | |
| df3['Publish Date'] = pd.to_datetime(df3['Publish Date'], format='%d-%m-%Y') | |
| # Fixing date formats | |
| for i in range(len(df3)): | |
| if '/' in df3['Accident Date'][i]: | |
| day=int(df3['Accident Date'][i].split('/')[0]) | |
| mon=int(df3['Accident Date'][i].split('/')[1]) | |
| yr=int(df3['Accident Date'][i].split('/')[2]) | |
| df3['Publish Date'][i]=f"{day}-{mon}-{yr}" | |
| print(df3.tail()) | |
| # Convert user input dates to datetime | |
| start_date = pd.to_datetime(start_string, format='%d-%m-%Y') | |
| end_date = pd.to_datetime(end_string, format='%d-%m-%Y') | |
| # Filter rows based on the specified date range | |
| filtered_entries = df3[(df3['Publish Date'] >= start_date) & (df3['Publish Date'] <= end_date)] | |
| filtered_entries.reset_index(inplace=True, drop=True) | |
| # Display the filtered data | |
| st.dataframe(filtered_entries) | |
| # Create a bar chart for accident count over days | |
| if not filtered_entries.empty: | |
| # Create a bar chart for accident count over days | |
| if not filtered_entries.empty: | |
| import plotly.express as px | |
| # Convert 'Accident Date' to datetime format | |
| filtered_entries['Accident Date'] = pd.to_datetime(filtered_entries['Accident Date'], format='%d-%m-%Y') | |
| # Count accidents per date and sort by date | |
| accident_counts = filtered_entries['Accident Date'].value_counts().sort_index() | |
| # Reset the index and rename columns | |
| accident_counts = accident_counts.reset_index() | |
| accident_counts.columns = ['Accident Date', 'Accident Count'] | |
| # Convert 'Accident Date' back to string format | |
| accident_counts['Accident Date'] = accident_counts['Accident Date'].dt.strftime('%d-%m-%Y') | |
| filtered_entries['Accident Date'] = accident_counts['Accident Date'] | |
| fig1 = px.bar(accident_counts, | |
| x='Accident Date', | |
| y='Accident Count', | |
| title="Accident Count Over Days", | |
| labels={'Accident Date': 'Date', 'Accident Count': 'Number of Accidents'}, | |
| color='Accident Count', | |
| color_continuous_scale='Viridis') | |
| st.plotly_chart(fig1) | |
| # Convert 'Accident Date' to datetime format | |
| filtered_entries['Accident Date'] = pd.to_datetime(filtered_entries['Accident Date'], format='%d-%m-%Y') | |
| # Group by 'Accident Date' and sum the 'Killed' column | |
| killed_per_day = filtered_entries.groupby('Accident Date')['Killed'].sum().reset_index() | |
| killed_per_day.columns = ['Accident Date', 'Total Killed'] | |
| # Sort the dates in ascending order | |
| killed_per_day = killed_per_day.sort_values(by='Accident Date') | |
| # Convert 'Accident Date' back to string format | |
| killed_per_day['Accident Date'] = killed_per_day['Accident Date'].dt.strftime('%d-%m-%Y') | |
| fig2 = px.bar(killed_per_day, | |
| x='Accident Date', | |
| y='Total Killed', | |
| title="Number of People Killed Each Day", | |
| labels={'Accident Date': 'Date', 'Total Killed': 'Number of People Killed'}, | |
| color='Total Killed', | |
| color_continuous_scale='Reds') | |
| st.plotly_chart(fig2) | |
| # Bar chart showing the number of accidents in each district | |
| district_accidents = filtered_entries['District'].value_counts().reset_index() | |
| district_accidents.columns = ['District', 'Number of Accidents'] | |
| fig3 = px.bar(district_accidents, | |
| x='District', | |
| y='Number of Accidents', | |
| title="Accidents in Each District", | |
| labels={'Number of Accidents': 'Number of Accidents', 'District': 'District'}, | |
| color='Number of Accidents', | |
| color_continuous_scale='Cividis') | |
| st.plotly_chart(fig3) | |
| ### Pie Chart Code ### | |
| yes_count=0 | |
| no_count=0 | |
| not_available_count=0 | |
| for i in range(len(filtered_entries)): | |
| if ('Yes' in filtered_entries['Pedestrian_Involved'][i] or 'yes' in filtered_entries['Pedestrian_Involved'][i]): yes_count+=1 | |
| if ('No' in filtered_entries['Pedestrian_Involved'][i] or 'no' in filtered_entries['Pedestrian_Involved'][i]): no_count+=1 | |
| if ('Not Available' in filtered_entries['Pedestrian_Involved'][i]): not_available_count+=1 | |
| Pedestrian_Involved_list = ['Yes', 'No', 'Not Available'] | |
| Count_list = [yes_count, no_count, not_available_count] | |
| # dictionary of lists | |
| dict = {'Pedestrian Involved': Pedestrian_Involved_list, 'Count':Count_list} | |
| pedestrian_involvement = pd.DataFrame(dict) | |
| # Pie chart showing the percentage of accidents involving pedestrians vs. those that don't | |
| # pedestrian_involvement = filtered_entries['Pedestrian_Involved'].value_counts().reset_index() | |
| # pedestrian_involvement.columns = ['Pedestrian Involved', 'Count'] | |
| fig4 = px.pie(pedestrian_involvement, | |
| names='Pedestrian Involved', | |
| values='Count', | |
| title="Accidents Involving Pedestrians", | |
| labels={'Pedestrian Involved': 'Pedestrian Involved'}, | |
| color_discrete_sequence=['Green', 'Red', 'Blue']) | |
| st.plotly_chart(fig4) | |
| else: | |
| st.write("No data available for the selected date range.") | |
| # Display selected start and end dates | |
| st.write("Start date is:", start) | |
| st.write("End date is:", end) | |