Update app.py

app.py

@@ -66,14 +66,23 @@ def generate_visuals(filtered_data):
     plt.ylabel("Count")
     plt.tight_layout()
     country_chart = gr.Plot(plt.gcf())
+    
+    # Salary by Gender Histogram
+    plt.figure(figsize=(12, 8))
+    sns.histplot(data=filtered_data, x='wage_amt', hue='gender', multiple="stack", kde=True)
+    plt.title("Salary Distribution by Gender")
+    plt.xlabel("Salary (USD)")
+    plt.ylabel("Frequency")
+    plt.tight_layout()
+    salary_gender_hist = gr.Plot(plt.gcf())
 
-    return gender_chart, country_chart
+    return gender_chart, country_chart, salary_gender_hist
 
 # Gradio Interface
 def app(fiscal_year, employer, job_title, country_of_birth, country_of_nationality, min_salary, max_salary, worksite_city, worksite_state):
     filtered = filter_data(fiscal_year, employer, job_title, country_of_birth, country_of_nationality, min_salary, max_salary, worksite_city, worksite_state)
-    gender_chart, country_chart = generate_visuals(filtered)
-    return filtered, gender_chart, country_chart
+    gender_chart, country_chart, salary_gender_hist = generate_visuals(filtered)
+    return filtered, gender_chart, country_chart, salary_gender_hist
 
 # Dropdown options
 fiscal_years = ["All"] + sorted(data['fiscal_year'].dropna().unique().astype(str).tolist())
@@ -109,15 +118,16 @@ with gr.Blocks() as demo:
     with gr.Row():
         apply_filters = gr.Button("Apply Filters")
     
-    
+    # Output components
     output_table = gr.Dataframe(label="Filtered Data")
     gender_chart = gr.Plot()
     country_chart = gr.Plot()
+    salary_gender_hist = gr.Plot()
 
     apply_filters.click(
         app,
         inputs=[fiscal_year, employer, job_title, country_of_birth, country_of_nationality, min_salary, max_salary, worksite_city, worksite_state],
-        outputs=[output_table, gender_chart, country_chart]
+        outputs=[output_table, gender_chart, country_chart, salary_gender_hist]
     )
 
 demo.launch()