Update app.py

app.py

@@ -19,7 +19,7 @@ def wait_for_element(driver, locator):
     return WebDriverWait(driver, 10).until(EC.element_to_be_clickable(locator))
 
 def select_date_month_day(driver, date_str, date_input_id):
-    date_to_select = datetime.strptime(date_str, '%Y#-%m-%d')
+    date_to_select = datetime.strptime(date_str, '%Y-%m-%d')
     date_input = WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.ID, date_input_id)))
     date_input.click()
     month_select = WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CLASS_NAME, 'ui-datepicker-month')))
@@ -287,7 +287,7 @@ def scrape_data(url, user_id, password):
 
 
 # --- Plotting Functions ---
-def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_by_year, year, show_mc_pct=True):
+def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_by_year, year, show_mc_pct=True, mc_sort_order="desc"):
     if not patient_data_by_year or not claim_data_by_year or not mc_data_by_year:
         return None, None
     
@@ -300,12 +300,13 @@ def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_
         return None, None
     
     # Professional styling
-    sns.set(style="whitegrid", palette="deep")
+    sns.set(style="whitegrid", palette="muted")
     plt.rcParams.update({
-        'font.family': 'Arial', 'font.size': 12, 'axes.titlesize': 14, 
-        'axes.labelsize': 12, 'xtick.labelsize': 10, 'ytick.labelsize': 10,
-        'axes.titleweight': 'bold', 'axes.linewidth': 1.2, 'grid.linestyle': '--', 
-        'grid.alpha': 0.7, 'figure.facecolor': '#f9f9f9', 'axes.facecolor': '#ffffff'
+        'font.family': 'Helvetica', 'font.size': 12, 'axes.titlesize': 16, 
+        'axes.labelsize': 14, 'xtick.labelsize': 11, 'ytick.labelsize': 11,
+        'axes.titleweight': 'bold', 'axes.linewidth': 1.5, 'grid.linestyle': ':', 
+        'grid.alpha': 0.5, 'figure.facecolor': '#f5f6f5', 'axes.facecolor': '#ffffff',
+        'axes.edgecolor': '#333333', 'axes.labelcolor': '#333333', 'text.color': '#333333'
     })
     
     provider_charts = []
@@ -315,12 +316,12 @@ def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_
     # 1. Total Visits by Providers (Horizontal Bar)
     plt.figure(figsize=(12, 6))
     top_prov_visits = mc_df.sort_values('No. of Visit', ascending=False).head(10)
-    sns.barplot(data=top_prov_visits, x='No. of Visit', y='Provider', hue='Provider', palette='Blues_d', legend=False)
-    plt.title(f'Top 10 Providers by Total Visits ({year})')
-    plt.xlabel('Total Visits')
-    plt.ylabel('Provider')
+    ax = sns.barplot(data=top_prov_visits, x='No. of Visit', y='Provider', hue='Provider', palette='Blues_r', legend=False, edgecolor='black', linewidth=0.5)
+    ax.set_title(f'Top 10 Providers by Total Visits ({year})', pad=15)
+    ax.set_xlabel('Total Visits')
+    ax.set_ylabel('Provider')
     for i, v in enumerate(top_prov_visits['No. of Visit']):
-        plt.text(v + 0.5, i, f'{int(v)}', va='center', fontsize=10, color='black')
+        ax.text(v + 0.5, i, f'{int(v)}', va='center', fontsize=10, color='#333333')
     plt.tight_layout()
     provider_charts.append(plt.gcf())
     plt.close()
@@ -328,30 +329,32 @@ def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_
     # 2. Total MC by Providers (Horizontal Bar)
     plt.figure(figsize=(12, 6))
     top_prov_mc = mc_df.sort_values('Total MC Given', ascending=False).head(10)
-    sns.barplot(data=top_prov_mc, x='Total MC Given', y='Provider', hue='Provider', palette='Greens_d', legend=False)
-    plt.title(f'Top 10 Providers by Total MC Given ({year})')
-    plt.xlabel('Total MC (Days)')
-    plt.ylabel('Provider')
+    ax = sns.barplot(data=top_prov_mc, x='Total MC Given', y='Provider', hue='Provider', palette='Greens_r', legend=False, edgecolor='black', linewidth=0.5)
+    ax.set_title(f'Top 10 Providers by Total MC Given ({year})', pad=15)
+    ax.set_xlabel('Total MC (Days)')
+    ax.set_ylabel('Provider')
     for i, v in enumerate(top_prov_mc['Total MC Given']):
-        plt.text(v + 0.5, i, f'{int(v)}', va='center', fontsize=10, color='black')
+        ax.text(v + 0.5, i, f'{int(v)}', va='center', fontsize=10, color='#333333')
     plt.tight_layout()
     provider_charts.append(plt.gcf())
     plt.close()
 
-    # 3. % MC Given by Providers (Bar) - Based on Top 10 by Visits and MC
+    # 3. % MC Given by Providers (Larger Bar, Top 20, Sortable)
     if show_mc_pct:
-        plt.figure(figsize=(12, 6))
+        plt.figure(figsize=(18, 9))  # Larger size for professionalism and readability
         top_visits_provs = set(mc_df.sort_values('No. of Visit', ascending=False).head(10)['Provider'])
         top_mc_provs = set(mc_df.sort_values('Total MC Given', ascending=False).head(10)['Provider'])
         top_provs = top_visits_provs.union(top_mc_provs)
-        top_prov_mc_pct = mc_df[mc_df['Provider'].isin(top_provs)].sort_values('% MC Given', ascending=False)
-        sns.barplot(data=top_prov_mc_pct, x='Provider', y='% MC Given', hue='Provider', palette='Purples_d', legend=False)
-        plt.title(f'% MC Given for Top Providers ({year})')
-        plt.ylabel('% MC Given')
-        plt.xlabel('Provider')
-        plt.xticks(rotation=45, ha='right')
+        top_prov_mc_pct = mc_df[mc_df['Provider'].isin(top_provs)].sort_values(
+            '% MC Given', ascending=(mc_sort_order == "asc")).head(20)
+        ax = sns.barplot(data=top_prov_mc_pct, x='Provider', y='% MC Given', hue='Provider', 
+                         palette='Purples_r', legend=False, edgecolor='black', linewidth=0.5)
+        ax.set_title(f'Top 20 Providers by % MC Given ({year}) - Sorted {"Ascending" if mc_sort_order == "asc" else "Descending"}', pad=15)
+        ax.set_ylabel('% MC Given', fontsize=14)
+        ax.set_xlabel('Provider', fontsize=14)
+        plt.xticks(rotation=45, ha='right', fontsize=11)
         for i, v in enumerate(top_prov_mc_pct['% MC Given']):
-            plt.text(i, v + 1, f'{v:.1f}%', ha='center', fontsize=10, color='black')
+            ax.text(i, v + 1, f'{v:.1f}%', ha='center', fontsize=10, color='#333333')
         plt.tight_layout()
         provider_charts.append(plt.gcf())
     else:
@@ -361,12 +364,13 @@ def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_
     # 4. Total Claim by Providers (Horizontal Bar)
     plt.figure(figsize=(12, 6))
     top_prov_claim = claim_df.sort_values('Total Claim', ascending=False).head(10)
-    sns.barplot(data=top_prov_claim, x='Total Claim', y='Provider Name', hue='Provider Name', palette='Oranges_d', legend=False)
-    plt.title(f'Top 10 Providers by Total Claim ({year})')
-    plt.xlabel('Total Claim ($)')
-    plt.ylabel('Provider')
+    ax = sns.barplot(data=top_prov_claim, x='Total Claim', y='Provider Name', hue='Provider Name', 
+                     palette='Oranges_r', legend=False, edgecolor='black', linewidth=0.5)
+    ax.set_title(f'Top 10 Providers by Total Claim ({year})', pad=15)
+    ax.set_xlabel('Total Claim ($)')
+    ax.set_ylabel('Provider')
     for i, v in enumerate(top_prov_claim['Total Claim']):
-        plt.text(v + 0.5, i, f'{v:,.2f}', va='center', fontsize=10, color='black')
+        ax.text(v + 0.5, i, f'{v:,.2f}', va='center', fontsize=10, color='#333333')
     plt.tight_layout()
     provider_charts.append(plt.gcf())
     plt.close()
@@ -374,37 +378,29 @@ def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_
     # 5. Average Claim per Visit by Providers (Bar)
     plt.figure(figsize=(12, 6))
     top_prov_avg_claim = claim_df.sort_values('Avg Claim per Visit', ascending=False).head(10)
-    sns.barplot(data=top_prov_avg_claim, x='Provider Name', y='Avg Claim per Visit', hue='Provider Name', palette='Reds_d', legend=False)
-    plt.title(f'Top 10 Providers by Avg Claim per Visit ({year})')
-    plt.ylabel('Avg Claim per Visit ($)')
-    plt.xlabel('Provider')
+    ax = sns.barplot(data=top_prov_avg_claim, x='Provider Name', y='Avg Claim per Visit', hue='Provider Name', 
+                     palette='Reds_r', legend=False, edgecolor='black', linewidth=0.5)
+    ax.set_title(f'Top 10 Providers by Avg Claim per Visit ({year})', pad=15)
+    ax.set_ylabel('Avg Claim per Visit ($)')
+    ax.set_xlabel('Provider')
     plt.xticks(rotation=45, ha='right')
     for i, v in enumerate(top_prov_avg_claim['Avg Claim per Visit']):
-        plt.text(i, v + 0.5, f'{v:.2f}', ha='center', fontsize=10, color='black')
-    plt.tight_layout()
-    provider_charts.append(plt.gcf())
-    plt.close()
-
-    # 6. Insight: MC vs Claim Correlation (Scatter)
-    plt.figure(figsize=(12, 6))
-    sns.scatterplot(data=claim_df, x='Total MC (Days)', y='Total Claim', size='No of Visits', hue='Provider Name', palette='viridis', legend=False)
-    plt.title(f'MC vs Claim Correlation by Provider ({year})')
-    plt.xlabel('Total MC (Days)')
-    plt.ylabel('Total Claim ($)')
+        ax.text(i, v + 0.5, f'{v:.2f}', ha='center', fontsize=10, color='#333333')
     plt.tight_layout()
     provider_charts.append(plt.gcf())
     plt.close()
 
-    # --- Employee Dashboard Charts (unchanged styling for brevity, apply similar updates as above if desired) ---
+    # --- Employee Dashboard Charts ---
     # 1. Total Visits by Employees
     plt.figure(figsize=(12, 6))
     top_emp_visits = patient_df.sort_values('Total Visit', ascending=False).head(10)
-    sns.barplot(data=top_emp_visits, x='Total Visit', y='Employee Name', hue='Employee Name', palette='Blues_d', legend=False)
-    plt.title(f'Top 10 Employees by Total Visits ({year})')
-    plt.xlabel('Total Visits')
-    plt.ylabel('Employee')
+    ax = sns.barplot(data=top_emp_visits, x='Total Visit', y='Employee Name', hue='Employee Name', 
+                     palette='Blues_r', legend=False, edgecolor='black', linewidth=0.5)
+    ax.set_title(f'Top 10 Employees by Total Visits ({year})', pad=15)
+    ax.set_xlabel('Total Visits')
+    ax.set_ylabel('Employee')
     for i, v in enumerate(top_emp_visits['Total Visit']):
-        plt.text(v + 0.2, i, f'{int(v)}', va='center', fontsize=10, color='black')
+        ax.text(v + 0.2, i, f'{int(v)}', va='center', fontsize=10, color='#333333')
     plt.tight_layout()
     employee_charts.append(plt.gcf())
     plt.close()
@@ -412,12 +408,13 @@ def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_
     # 2. Total Claim by Employees
     plt.figure(figsize=(12, 6))
     top_emp_claim = patient_df.sort_values('Total Claim (Combined)', ascending=False).head(10)
-    sns.barplot(data=top_emp_claim, x='Total Claim (Combined)', y='Employee Name', hue='Employee Name', palette='Oranges_d', legend=False)
-    plt.title(f'Top 10 Employees by Total Claim ({year})')
-    plt.xlabel('Total Claim ($)')
-    plt.ylabel('Employee')
+    ax = sns.barplot(data=top_emp_claim, x='Total Claim (Combined)', y='Employee Name', hue='Employee Name', 
+                     palette='Oranges_r', legend=False, edgecolor='black', linewidth=0.5)
+    ax.set_title(f'Top 10 Employees by Total Claim ({year})', pad=15)
+    ax.set_xlabel('Total Claim ($)')
+    ax.set_ylabel('Employee')
     for i, v in enumerate(top_emp_claim['Total Claim (Combined)']):
-        plt.text(v + 1, i, f'{v:,.2f}', va='center', fontsize=10, color='black')
+        ax.text(v + 1, i, f'{v:,.2f}', va='center', fontsize=10, color='#333333')
     plt.tight_layout()
     employee_charts.append(plt.gcf())
     plt.close()
@@ -425,13 +422,14 @@ def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_
     # 3. Average Claim per Visit by Employees
     plt.figure(figsize=(12, 6))
     top_emp_avg_claim = patient_df.sort_values('Avg Claim per Visit', ascending=False).head(10)
-    sns.barplot(data=top_emp_avg_claim, x='Employee Name', y='Avg Claim per Visit', hue='Employee Name', palette='Reds_d', legend=False)
-    plt.title(f'Top 10 Employees by Avg Claim per Visit ({year})')
-    plt.ylabel('Avg Claim per Visit ($)')
-    plt.xlabel('Employee')
+    ax = sns.barplot(data=top_emp_avg_claim, x='Employee Name', y='Avg Claim per Visit', hue='Employee Name', 
+                     palette='Reds_r', legend=False, edgecolor='black', linewidth=0.5)
+    ax.set_title(f'Top 10 Employees by Avg Claim per Visit ({year})', pad=15)
+    ax.set_ylabel('Avg Claim per Visit ($)')
+    ax.set_xlabel('Employee')
     plt.xticks(rotation=45, ha='right')
     for i, v in enumerate(top_emp_avg_claim['Avg Claim per Visit']):
-        plt.text(i, v + 0.5, f'{v:.2f}', ha='center', fontsize=10, color='black')
+        ax.text(i, v + 0.5, f'{v:.2f}', ha='center', fontsize=10, color='#333333')
     plt.tight_layout()
     employee_charts.append(plt.gcf())
     plt.close()
@@ -439,12 +437,13 @@ def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_
     # 4. Total MC by Employees
     plt.figure(figsize=(12, 6))
     top_emp_mc = patient_df.sort_values('Total MC (Days)', ascending=False).head(10)
-    sns.barplot(data=top_emp_mc, x='Total MC (Days)', y='Employee Name', hue='Employee Name', palette='Greens_d', legend=False)
-    plt.title(f'Top 10 Employees by Total MC ({year})')
-    plt.xlabel('Total MC (Days)')
-    plt.ylabel('Employee')
+    ax = sns.barplot(data=top_emp_mc, x='Total MC (Days)', y='Employee Name', hue='Employee Name', 
+                     palette='Greens_r', legend=False, edgecolor='black', linewidth=0.5)
+    ax.set_title(f'Top 10 Employees by Total MC ({year})', pad=15)
+    ax.set_xlabel('Total MC (Days)')
+    ax.set_ylabel('Employee')
     for i, v in enumerate(top_emp_mc['Total MC (Days)']):
-        plt.text(v + 0.2, i, f'{int(v)}', va='center', fontsize=10, color='black')
+        ax.text(v + 0.2, i, f'{int(v)}', va='center', fontsize=10, color='#333333')
     plt.tight_layout()
     employee_charts.append(plt.gcf())
     plt.close()
@@ -452,36 +451,24 @@ def generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_
     # 5. Average MC per Visit by Employees
     plt.figure(figsize=(12, 6))
     top_emp_avg_mc = patient_df.sort_values('Avg MC per Visit', ascending=False).head(10)
-    sns.barplot(data=top_emp_avg_mc, x='Employee Name', y='Avg MC per Visit', hue='Employee Name', palette='Purples_d', legend=False)
-    plt.title(f'Top 10 Employees by Avg MC per Visit ({year})')
-    plt.ylabel('Avg MC per Visit (Days)')
-    plt.xlabel('Employee')
+    ax = sns.barplot(data=top_emp_avg_mc, x='Employee Name', y='Avg MC per Visit', hue='Employee Name', 
+                     palette='Purples_r', legend=False, edgecolor='black', linewidth=0.5)
+    ax.set_title(f'Top 10 Employees by Avg MC per Visit ({year})', pad=15)
+    ax.set_ylabel('Avg MC per Visit (Days)')
+    ax.set_xlabel('Employee')
     plt.xticks(rotation=45, ha='right')
     for i, v in enumerate(top_emp_avg_mc['Avg MC per Visit']):
-        plt.text(i, v + 0.05, f'{v:.2f}', ha='center', fontsize=10, color='black')
-    plt.tight_layout()
-    employee_charts.append(plt.gcf())
-    plt.close()
-
-    # 6. Average Claim per MC by Employees
-    plt.figure(figsize=(12, 6))
-    top_emp_avg_claim_mc = patient_df.sort_values('Avg Claim per MC', ascending=False).head(10)
-    sns.barplot(data=top_emp_avg_claim_mc, x='Employee Name', y='Avg Claim per MC', hue='Employee Name', palette='YlOrBr', legend=False)
-    plt.title(f'Top 10 Employees by Avg Claim per MC ({year})')
-    plt.ylabel('Avg Claim per MC ($)')
-    plt.xlabel('Employee')
-    plt.xticks(rotation=45, ha='right')
-    for i, v in enumerate(top_emp_avg_claim_mc['Avg Claim per MC']):
-        plt.text(i, v + 0.5, f'{v:.2f}', ha='center', fontsize=10, color='black')
+        ax.text(i, v + 0.05, f'{v:.2f}', ha='center', fontsize=10, color='#333333')
     plt.tight_layout()
     employee_charts.append(plt.gcf())
     plt.close()
 
-    # 7. Division-wise Claim Distribution (Pie)
+    # 6. Division-wise Claim Distribution (Pie)
     plt.figure(figsize=(10, 6))
     division_claims = patient_df.groupby('Division/Department')['Total Claim (Combined)'].sum()
-    plt.pie(division_claims, labels=division_claims.index, autopct='%1.1f%%', colors=sns.color_palette('pastel'), startangle=90)
-    plt.title(f'Claim Distribution by Division ({year})')
+    plt.pie(division_claims, labels=division_claims.index, autopct='%1.1f%%', colors=sns.color_palette('muted'), 
+            startangle=90, textprops={'fontsize': 11, 'color': '#333333'}, wedgeprops={'edgecolor': 'black', 'linewidth': 0.5})
+    plt.title(f'Claim Distribution by Division ({year})', pad=15)
     plt.tight_layout()
     employee_charts.append(plt.gcf())
     plt.close()
@@ -500,14 +487,17 @@ def fig_to_image(fig):
     return img_array
 
 # --- Gradio Interface ---
-with gr.Blocks(title="Claims Analysis Dashboard") as demo:
+with gr.Blocks(title="Claims Analysis Dashboard", css="""
+    body { background-color: #f5f6f5; }
+    h1, h2 { color: #333333; font-family: Helvetica; }
+""") as demo:
     gr.Markdown("# Claims Analysis Dashboard (2024 - Present)")
     
     with gr.Row():
-        url_input = gr.Textbox(label="Website URL")
-        user_id_input = gr.Textbox(label="User ID")
-        password_input = gr.Textbox(label="Password", type="password")
-    scrape_btn = gr.Button("Scrape Data")
+        url_input = gr.Textbox(label="Website URL", placeholder="Enter URL here", lines=1)
+        user_id_input = gr.Textbox(label="User ID", placeholder="Enter User ID", lines=1)
+        password_input = gr.Textbox(label="Password", type="password", placeholder="Enter Password", lines=1)
+    scrape_btn = gr.Button("Submit", variant="primary")
     
     with gr.Row():
         year_dropdown = gr.Dropdown(
@@ -517,8 +507,14 @@ with gr.Blocks(title="Claims Analysis Dashboard") as demo:
             allow_custom_value=False
         )
         show_mc_pct_checkbox = gr.Checkbox(label="Show % MC Given Chart", value=True)
+        mc_sort_dropdown = gr.Dropdown(
+            label="Sort % MC Given",
+            choices=["desc", "asc"],
+            value="desc",
+            allow_custom_value=False
+        )
     
-    status_output = gr.Textbox(label="Status")
+    status_output = gr.Textbox(label="Status", lines=2, interactive=False)
     patient_state = gr.State()
     claim_state = gr.State()
     mc_state = gr.State()
@@ -530,11 +526,10 @@ with gr.Blocks(title="Claims Analysis Dashboard") as demo:
                 prov_chart1 = gr.Image(label="Total Visits by Providers", interactive=False)
                 prov_chart2 = gr.Image(label="Total MC by Providers", interactive=False)
             with gr.Row():
-                prov_chart3 = gr.Image(label="% MC Given by Providers", interactive=False, visible=True)
-                prov_chart4 = gr.Image(label="Total Claim by Providers", interactive=False)
+                prov_chart3 = gr.Image(label="% MC Given by Providers (Top 20)", interactive=False, visible=True)
             with gr.Row():
+                prov_chart4 = gr.Image(label="Total Claim by Providers", interactive=False)
                 prov_chart5 = gr.Image(label="Average Claim per Visit by Providers", interactive=False)
-                prov_chart6 = gr.Image(label="MC vs Claim Correlation", interactive=False)
 
         with gr.TabItem("Employee Insights"):
             gr.Markdown("## Employee Insights Dashboard")
@@ -546,57 +541,67 @@ with gr.Blocks(title="Claims Analysis Dashboard") as demo:
                 emp_chart4 = gr.Image(label="Total MC by Employees", interactive=False)
             with gr.Row():
                 emp_chart5 = gr.Image(label="Average MC per Visit by Employees", interactive=False)
-                emp_chart6 = gr.Image(label="Average Claim per MC by Employees", interactive=False)
-            with gr.Row():
-                emp_chart7 = gr.Image(label="Claim Distribution by Division", interactive=False)
+                emp_chart6 = gr.Image(label="Claim Distribution by Division", interactive=False)
 
-    def scrape_and_store(url, user_id, password, show_mc_pct):
+    # [Event handlers remain unchanged, just ensure inputs/outputs match the above components]
+    def scrape_and_store(url, user_id, password, show_mc_pct, mc_sort_order):
         patient_data_by_year, claim_data_by_year, mc_data_by_year, status = scrape_data(url, user_id, password)
         if patient_data_by_year is None or claim_data_by_year is None or mc_data_by_year is None:
             return status, None, None, None
         
-        provider_images, employee_images = generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_by_year, "2024", show_mc_pct)
+        provider_images, employee_images = generate_dashboard_charts(
+            patient_data_by_year, claim_data_by_year, mc_data_by_year, "2024", show_mc_pct, mc_sort_order)
         return (
             status, patient_data_by_year, claim_data_by_year, mc_data_by_year,
-            provider_images[0], provider_images[1], provider_images[2], provider_images[3], provider_images[4], provider_images[5],
-            employee_images[0], employee_images[1], employee_images[2], employee_images[3], employee_images[4], employee_images[5], employee_images[6]
+            provider_images[0], provider_images[1], provider_images[2], provider_images[3], provider_images[4],
+            employee_images[0], employee_images[1], employee_images[2], employee_images[3], employee_images[4], employee_images[5]
         )
 
-    def update_dashboard(year, patient_data_by_year, claim_data_by_year, mc_data_by_year, show_mc_pct):
+    def update_dashboard(year, patient_data_by_year, claim_data_by_year, mc_data_by_year, show_mc_pct, mc_sort_order):
         if not patient_data_by_year or not claim_data_by_year or not mc_data_by_year:
-            return [None] * 13
-        provider_images, employee_images = generate_dashboard_charts(patient_data_by_year, claim_data_by_year, mc_data_by_year, year, show_mc_pct)
+            return [None] * 11
+        provider_images, employee_images = generate_dashboard_charts(
+            patient_data_by_year, claim_data_by_year, mc_data_by_year, year, show_mc_pct, mc_sort_order)
         return (
-            provider_images[0], provider_images[1], provider_images[2], provider_images[3], provider_images[4], provider_images[5],
-            employee_images[0], employee_images[1], employee_images[2], employee_images[3], employee_images[4], employee_images[5], employee_images[6]
+            provider_images[0], provider_images[1], provider_images[2], provider_images[3], provider_images[4],
+            employee_images[0], employee_images[1], employee_images[2], employee_images[3], employee_images[4], employee_images[5]
         )
 
     scrape_btn.click(
-        fn=lambda url, user_id, password: scrape_and_store(url, user_id, password, show_mc_pct_checkbox.value),
+        fn=lambda url, user_id, password: scrape_and_store(url, user_id, password, show_mc_pct_checkbox.value, mc_sort_dropdown.value),
         inputs=[url_input, user_id_input, password_input],
         outputs=[
             status_output, patient_state, claim_state, mc_state,
-            prov_chart1, prov_chart2, prov_chart3, prov_chart4, prov_chart5, prov_chart6,
-            emp_chart1, emp_chart2, emp_chart3, emp_chart4, emp_chart5, emp_chart6, emp_chart7
+            prov_chart1, prov_chart2, prov_chart3, prov_chart4, prov_chart5,
+            emp_chart1, emp_chart2, emp_chart3, emp_chart4, emp_chart5, emp_chart6
         ]
     )
 
     year_dropdown.change(
         fn=update_dashboard,
-        inputs=[year_dropdown, patient_state, claim_state, mc_state, show_mc_pct_checkbox],
+        inputs=[year_dropdown, patient_state, claim_state, mc_state, show_mc_pct_checkbox, mc_sort_dropdown],
         outputs=[
-            prov_chart1, prov_chart2, prov_chart3, prov_chart4, prov_chart5, prov_chart6,
-            emp_chart1, emp_chart2, emp_chart3, emp_chart4, emp_chart5, emp_chart6, emp_chart7
+            prov_chart1, prov_chart2, prov_chart3, prov_chart4, prov_chart5,
+            emp_chart1, emp_chart2, emp_chart3, emp_chart4, emp_chart5, emp_chart6
         ]
     )
 
     show_mc_pct_checkbox.change(
         fn=update_dashboard,
-        inputs=[year_dropdown, patient_state, claim_state, mc_state, show_mc_pct_checkbox],
+        inputs=[year_dropdown, patient_state, claim_state, mc_state, show_mc_pct_checkbox, mc_sort_dropdown],
+        outputs=[
+            prov_chart1, prov_chart2, prov_chart3, prov_chart4, prov_chart5,
+            emp_chart1, emp_chart2, emp_chart3, emp_chart4, emp_chart5, emp_chart6
+        ]
+    )
+
+    mc_sort_dropdown.change(
+        fn=update_dashboard,
+        inputs=[year_dropdown, patient_state, claim_state, mc_state, show_mc_pct_checkbox, mc_sort_dropdown],
         outputs=[
-            prov_chart1, prov_chart2, prov_chart3, prov_chart4, prov_chart5, prov_chart6,
-            emp_chart1, emp_chart2, emp_chart3, emp_chart4, emp_chart5, emp_chart6, emp_chart7
+            prov_chart1, prov_chart2, prov_chart3, prov_chart4, prov_chart5,
+            emp_chart1, emp_chart2, emp_chart3, emp_chart4, emp_chart5, emp_chart6
         ]
     )
 
-demo.launch()
+demo.launch(share=True)