Code Editor for app.py

import streamlit as st
import pandas as pd
import plotly.express as px

st.title("Austin Traffic Incidents")

# Load data
@st.cache_data
def load_data():
    # NOTE: Using corsproxy.io because we're in a WASM environment. If running locally,
    # you can remove the corsproxy.io prefix.
    url = "https://corsproxy.io/?https://data.austintexas.gov/resource/dx9v-zd7x.csv"
    df = pd.read_csv(url)
    df['published_date'] = pd.to_datetime(df['published_date'])
    return df

# Show loading message
with st.spinner('Loading traffic data...'):
    df = load_data()

# Sidebar filters
st.sidebar.header("Filters")

# Date filter
date_range = st.sidebar.date_input(
    "Select Date Range",
    value=(df['published_date'].min().date(), df['published_date'].max().date()),
    min_value=df['published_date'].min().date(),
    max_value=df['published_date'].max().date()
)

# Issue type filter
issue_types = sorted(df['issue_reported'].unique())
selected_types = st.sidebar.multiselect(
    "Select Incident Types",
    issue_types,
    default=issue_types[:5]
)

# Filter data based on selections
filtered_df = df[
    (df['published_date'].dt.date >= date_range[0]) &
    (df['published_date'].dt.date <= date_range[1]) &
    (df['issue_reported'].isin(selected_types))
]

# Main content
col1, col2 = st.columns([2, 1])

with col1:
    st.subheader("Incident Map")
    fig = px.scatter_mapbox(
        filtered_df,
        lat='latitude',
        lon='longitude',
        color='issue_reported',
        hover_data=['address', 'issue_reported', 'published_date'],
        zoom=10,
        height=500
    )
    fig.update_layout(
        mapbox_style="open-street-map",
        margin={"r":0,"t":0,"l":0,"b":0}
    )
    st.plotly_chart(fig, use_container_width=True)

with col2:
    st.subheader("Incidents by Type")
    type_counts = filtered_df['issue_reported'].value_counts()
    fig_bar = px.bar(
        x=type_counts.values,
        y=type_counts.index,
        orientation='h',
        title="Number of Incidents by Type"
    )
    st.plotly_chart(fig_bar, use_container_width=True)

# Show raw data
if st.checkbox("Show Raw Data"):
    st.subheader("Raw Data")
    st.dataframe(filtered_df)