Update src/streamlit_app.py

src/streamlit_app.py

@@ -163,13 +163,27 @@ df_top = df[df['crm_cd_desc'].isin(top_crimes)]
 heatmap1_data = df_top.groupby(['crm_cd_desc', 'year']).size().unstack(fill_value=0)
 
 # Create the heat map.
-plt.figure(figsize=(10, 6))
-sns.heatmap(heatmap1_data, annot=True, fmt="d", cmap="YlOrRd")
-plt.title("Top 10 Crime Types by Year")
-plt.xlabel("Year")
-plt.ylabel("Crime Type")
-plt.tight_layout()
-plt.show()
+fig, ax = plt.subplots(figsize=(10, 6))
+
+# 2. Draw into that Axes
+sns.heatmap(
+    heatmap1_data,
+    annot=True,
+    fmt="d",
+    cmap="YlOrRd",
+    ax=ax
+)
+
+# 3. Set titles/labels on the Axes
+ax.set_title("Top 10 Crime Types by Year")
+ax.set_xlabel("Year")
+ax.set_ylabel("Crime Type")
+
+# 4. Tight layout
+fig.tight_layout()
+
+# 5. Render in Streamlit
+st.pyplot(fig)
 
 ### Use this one!!!
 # Count the crime type and list out the top 10 crime type that have the most cases.
@@ -229,14 +243,6 @@ line_chart = alt.Chart(filtered_crimes).mark_line(point=True).encode(
 line_chart
 
 ### Use this one!!!
-# Load geojson file.
-gdf_counties = gpd.read_file("County_Boundary.geojson")
-    
-# Creat dropdown menu.
-year_dropdown = ipywidgets.Dropdown(
-    options= sorted(df['year'].unique()),
-    description='Year:')
-    
 # Identify top 10 crime types
 top_10_crimes = df['crm_cd_desc'].value_counts().nlargest(10).index.tolist()