Update src/streamlit_app.py

src/streamlit_app.py

@@ -249,10 +249,6 @@ line_chart
 
 st.markdown(""" This plot is a line chart visualizing the annual number of incidents for the top 5 most frequent crime types over a five-year period, from 2020 to 2024. Each line represents a distinct crime type, allowing for easy comparison of trends across different categories. The x-axis represents the year, the y-axis indicates the number of incidents, and a legend identifies the color corresponding to each specific crime type: Battery - Simple Assault, Burglary From Vehicle, Theft of Identity, Vandalism - Felony , and Vehicle - Stolen. The plot highlights the fluctuations and overall trajectories of these major crime categories across the years.""")
 
-### Use this one!!!
-# Load data
-with open("County_Boundary.geojson", "r", encoding="utf-8") as f:
-    geojson_data = json.load(f)
     
 # Identify top 10 crime types
 top_10_crimes = df['crm_cd_desc'].value_counts().nlargest(10).index.tolist()
@@ -272,8 +268,6 @@ df_filtered = df[(df['year'] == year_dropdown) & (df['crm_cd_desc'] == crime_dro
 # Create the new folium map to make the map more interactive.
 new_map = folium.Map(location=[df_filtered['lat'].mean(), df_filtered['lon'].mean()], zoom_start=10)
 
-GEOJSON_PATH = Path(__file__).parent / "County_Boundary.geojson"
-
 # Add county boundary
 folium.GeoJson(geojson_data, name="County Boundaries").add_to(new_map)