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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')
# 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:
# Accident count over days
accident_counts = filtered_entries['Accident Date'].value_counts().sort_index()
accident_counts = accident_counts.reset_index()
accident_counts.columns = ['Accident Date', 'Accident Count']
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'})
st.plotly_chart(fig1)
# Bar chart showing number of people killed each day
# Grouping by 'Publish Date' and summing 'Killed' column
killed_per_day = filtered_entries.groupby('Accident Date')['Killed'].sum().reset_index()
killed_per_day.columns = ['Accident Date', 'Total Killed']
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='Blues')
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'})
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)
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