:sparkles: Add county filter and highlight selected county in charts

This update introduces an interactive county selection filter to the app, allowing users to choose a specific county or view all data at once. The selected county is visually highlighted in the bar chart to enhance data interpretation, ensuring a more intuitive and engaging user experience.

app.py

@@ -38,19 +38,37 @@ st.subheader("Explore EV Distribution by County")
 county_counts = ev_data['County'].value_counts().reset_index()
 county_counts.columns = ['County', 'Count']
 
+# Add interactivity for county selection
+selected_county = st.selectbox("Select a County to Filter:", options=["All"] + county_counts['County'].tolist())
+if selected_county != "All":
+    ev_data_filtered = ev_data[ev_data['County'] == selected_county]
+else:
+    ev_data_filtered = ev_data
+
+# Highlight the selected bar
+county_counts['Color'] = county_counts['County'].apply(
+    lambda x: 'blue' if x == selected_county else 'green'
+)
+
+# Contextual Visualization 0: EV Bar Counts by County
 fig_county = px.bar(
     county_counts,
     x='County',
     y='Count',
-    title="Electric Vehicle Counts by County",
+    title=f"Electric Vehicle Counts by {selected_county if selected_county != 'All' else 'County'}",
     labels={"Count": "Number of EVs", "County": "County"},
+    color='County',
+    color_discrete_map={
+        selected_county: 'blue',
+        'Other': 'green'
+    },
     height=500
 )
 st.plotly_chart(fig_county, use_container_width=True)
 
 # Contextual Visualization 1: Vehicle Model Trends
 ev_data['Model Year'] = pd.to_numeric(ev_data['Model Year'], errors='coerce')
-model_year_counts = ev_data['Model Year'].value_counts().reset_index()
+model_year_counts = ev_data_filtered['Model Year'].value_counts().reset_index()
 model_year_counts.columns = ['Year', 'Count']
 model_year_counts = model_year_counts.sort_values('Year')
 
@@ -66,7 +84,7 @@ st.plotly_chart(fig_year, use_container_width=True)
 
 # Contextual Visualization 2: Map of EV Locations
 st.subheader("Geographic Distribution of EVs")
-filtered_data = ev_data.dropna(subset=['Latitude', 'Longitude'])
+filtered_data = ev_data_filtered.dropna(subset=['Latitude', 'Longitude'])
 st.pydeck_chart(pdk.Deck(
     initial_view_state=pdk.ViewState(
         latitude=filtered_data['Latitude'].mean(),