Update src/streamlit_app.py

src/streamlit_app.py

@@ -163,7 +163,6 @@ st.markdown(""" The donut chart shows the share of the ten most frequent crime c
 
 
 # -------------------------------- Plot 2: Heat Map --------------------------------
-
 # Count the crime type and list out the top 10 crime type that have the most cases.
 top_crimes = df['crm_cd_desc'].value_counts().nlargest(10).index
 df_top = df[df['crm_cd_desc'].isin(top_crimes)]
@@ -201,8 +200,8 @@ This heatmap shows the frequency of the top 10 crimes from 2020 to 2025. The x a
 
 
 # -------------------------------- Plot 3: Line Chart --------------------------------
-# Filter out the year 2025.
-df = df[df['year'] != 2025] # 2025 is not end, so the trend can't be see
+# Filter out the year 2025 since it is not the end, so that the trend can't be see.
+df = df[df['year'] != 2025]
 
 # Group the each crime type by year.
 yearly_crime_counts = (
@@ -235,7 +234,7 @@ st.markdown(""" This plot is a line chart visualizing the annual number of incid
 
 
 # -------------------------------- Plot 4: Map --------------------------------
-# Load data
+# Load the data.
 with open(GEOJSON_PATH, "r", encoding="utf-8") as f:
     geojson_data = json.load(f)
     
@@ -302,13 +301,6 @@ This visualization uses Folium to build an interactive map of crime distribution
 """)
 
 # -------------------------------- Plot 4: Stacked Bar Chart --------------------------------
-# # Filter out the year 2025.
-# df = df[df['year'] != 2025]
-
-# # Count the crime type and list out the top 10 crime type that have the most cases.
-# top_crimes = df['crm_cd_desc'].value_counts().nlargest(10).index
-# df_top = df[df['crm_cd_desc'].isin(top_crimes)]
-
 # Group by crime type and year.
 stacked_year_df = df_top.groupby(['year', 'crm_cd_desc']).size().reset_index(name='count')
 
@@ -335,9 +327,6 @@ By observing the plot, we can find out that 2022 had the most crimes, the year h
 """)
 
 # -------------------------------- Plot 5: Bar Chart --------------------------------
-# top_crimes = df['crm_cd_desc'].value_counts().nlargest(10).index
-# df_top = df[df['crm_cd_desc'].isin(top_crimes)]
-
 # Group by crime type and year.
 heatmap1_df = df_top.groupby(['crm_cd_desc', 'year']).size().reset_index(name='count')