add

app.py

@@ -304,9 +304,9 @@ def create_monthly_metrics_plot(type="issue", top_n=5):
         print_msg = "discussion"
     elif type == "wanted":
         metrics_key = 'wanted_issue_monthly_metrics'
-        total_field = 'resolved_wanted'  # Only resolved_wanted is available now
+        total_field = 'total_wanted'
         no_data_msg = "No wanted issue data available for visualization"
-        total_label = "Resolved Wanted Issues"
+        total_label = "Total Wanted Issues"
         print_msg = "wanted issue"
 
     # Load from saved dataset
@@ -390,45 +390,41 @@ def create_monthly_metrics_plot(type="issue", top_n=5):
         color = agent_colors[agent_name]
         agent_data = data[agent_name]
 
-        # Add line trace for resolved rate (left y-axis) - only if resolved_rates exists
-        resolved_rates = agent_data.get('resolved_rates')
-        if resolved_rates:
-            # Filter out None values for plotting
-            x_resolved = [month for month, rate in zip(months, resolved_rates) if rate is not None]
-            y_resolved = [rate for rate in resolved_rates if rate is not None]
-
-            if x_resolved and y_resolved:  # Only add trace if there's data
-                fig.add_trace(
-                    go.Scatter(
-                        x=x_resolved,
-                        y=y_resolved,
-                        name=agent_name,
-                        mode='lines+markers',
-                        line=dict(color=color, width=2),
-                        marker=dict(size=8),
-                        legendgroup=agent_name,
-                        showlegend=(top_n is not None and top_n <= 10),  # Show legend for top N assistants
-                        hovertemplate='<b>Assistant: %{fullData.name}</b><br>' +
-                                     'Month: %{x}<br>' +
-                                     'Resolved Rate: %{y:.2f}%<br>' +
-                                     '<extra></extra>'
-                    ),
-                    secondary_y=False
-                )
+        # Add line trace for resolved rate (left y-axis)
+        resolved_rates = agent_data['resolved_rates']
+        # Filter out None values for plotting
+        x_resolved = [month for month, rate in zip(months, resolved_rates) if rate is not None]
+        y_resolved = [rate for rate in resolved_rates if rate is not None]
+
+        if x_resolved and y_resolved:  # Only add trace if there's data
+            fig.add_trace(
+                go.Scatter(
+                    x=x_resolved,
+                    y=y_resolved,
+                    name=agent_name,
+                    mode='lines+markers',
+                    line=dict(color=color, width=2),
+                    marker=dict(size=8),
+                    legendgroup=agent_name,
+                    showlegend=(top_n is not None and top_n <= 10),  # Show legend for top N assistants
+                    hovertemplate='<b>Assistant: %{fullData.name}</b><br>' +
+                                 'Month: %{x}<br>' +
+                                 'Resolved Rate: %{y:.2f}%<br>' +
+                                 '<extra></extra>'
+                ),
+                secondary_y=False
+            )
 
         # Add bar trace for total count (right y-axis)
         # Only show bars for months where assistant has data
         x_bars = []
         y_bars = []
-        total_data = agent_data.get(total_field, [])
-        for month, count in zip(months, total_data):
+        for month, count in zip(months, agent_data[total_field]):
             if count > 0:  # Only include months with data
                 x_bars.append(month)
                 y_bars.append(count)
 
         if x_bars and y_bars:  # Only add trace if there's data
-            # For wanted type without resolved_rates, show legend on bar chart
-            show_bar_legend = (top_n is not None and top_n <= 10) and not resolved_rates
             fig.add_trace(
                 go.Bar(
                     x=x_bars,
@@ -436,7 +432,7 @@ def create_monthly_metrics_plot(type="issue", top_n=5):
                     name=agent_name,
                     marker=dict(color=color, opacity=0.6),
                     legendgroup=agent_name,
-                    showlegend=show_bar_legend,  # Show legend if no line chart was added
+                    showlegend=False,  # Hide duplicate legend entry (already shown in Scatter)
                     hovertemplate=f'<b>Assistant: %{{fullData.name}}</b><br>' +
                                  f'Month: %{{x}}<br>' +
                                  f'{total_label}: %{{y}}<br>' +
@@ -448,25 +444,15 @@ def create_monthly_metrics_plot(type="issue", top_n=5):
 
     # Update axes labels
     fig.update_xaxes(title_text=None)
-
-    # For "wanted" type, there's no resolved_rates, so hide the left y-axis
-    if type == "wanted":
-        fig.update_yaxes(
-            title_text=None,
-            showticklabels=False,
-            showgrid=False,
-            secondary_y=False
-        )
-    else:
-        fig.update_yaxes(
-            title_text="<b>Resolved Rate (%)</b>",
-            range=[0, 100],
-            secondary_y=False,
-            showticklabels=True,
-            tickmode='linear',
-            dtick=10,
-            showgrid=True
-        )
+    fig.update_yaxes(
+        title_text="<b>Resolved Rate (%)</b>",
+        range=[0, 100],
+        secondary_y=False,
+        showticklabels=True,
+        tickmode='linear',
+        dtick=10,
+        showgrid=True
+    )
     fig.update_yaxes(title_text=f"<b>{total_label}</b>", secondary_y=True)
 
     # Update layout
@@ -726,7 +712,6 @@ with gr.Blocks(title="SWE Assistant Issue & Discussion Leaderboard", theme=gr.th
                 value=pd.DataFrame(columns=[col[0] for col in LEADERBOARD_COLUMNS]),  # Empty initially
                 datatype=LEADERBOARD_COLUMNS,
                 search_columns=["Assistant", "Website"],
-                height=400,  # Minimum height to prevent rows from being obscured by filter controls
                 filter_columns=[
                     ColumnFilter(
                         "Issue Resolved Rate (%)",
@@ -776,7 +761,7 @@ with gr.Blocks(title="SWE Assistant Issue & Discussion Leaderboard", theme=gr.th
                     discussion_metrics_plot = gr.Plot()
 
                 with gr.Column():
-                    gr.Markdown("*Resolved wanted issues over time*")
+                    gr.Markdown("*Wanted issue volume and resolved rate over time*")
                     wanted_metrics_plot = gr.Plot()
 
             # Load monthly metrics when app starts

msr.py

@@ -1050,20 +1050,25 @@ def calculate_monthly_metrics_by_discussions(all_discussions_dict, assistants):
     }
 
 
-def calculate_monthly_metrics_by_wanted_issues(wanted_resolved_dict, assistants):
-    """Calculate monthly metrics for resolved wanted issues for all assistants for visualization.
+def calculate_monthly_metrics_by_wanted_issues(wanted_resolved_dict, wanted_in_progress_dict, assistants):
+    """Calculate monthly metrics for wanted issues for all assistants for visualization.
+
+    Resolved wanted issues are grouped by their closing date.
+    In-progress wanted issues are counted in the current month (representing ongoing work).
+    Only months within LEADERBOARD_TIME_FRAME_DAYS are included.
     """
     identifier_to_name = {assistant.get('github_identifier'): assistant.get('name') for assistant in assistants if assistant.get('github_identifier')}
 
-    if not wanted_resolved_dict:
+    if not wanted_resolved_dict and not wanted_in_progress_dict:
         return {'assistants': [], 'months': [], 'data': {}}
 
     # Calculate the valid date range for filtering
     current_time = datetime.now(timezone.utc)
     start_date = current_time - timedelta(days=LEADERBOARD_TIME_FRAME_DAYS)
     start_month_key = f"{start_date.year}-{start_date.month:02d}"
+    current_month_key = f"{current_time.year}-{current_time.month:02d}"
 
-    agent_month_data = defaultdict(lambda: defaultdict(list))
+    agent_month_data = defaultdict(lambda: defaultdict(lambda: {'resolved': 0, 'in_progress': 0}))
 
     # Process resolved wanted issues - group by closed_at date
     for agent_identifier, metadata_list in (wanted_resolved_dict or {}).items():
@@ -1083,11 +1088,17 @@ def calculate_monthly_metrics_by_wanted_issues(wanted_resolved_dict, assistants)
                     continue
 
                 month_key = f"{dt.year}-{dt.month:02d}"
-                agent_month_data[agent_name][month_key].append(issue_meta)
+                agent_month_data[agent_name][month_key]['resolved'] += 1
             except Exception as e:
                 print(f"Warning: Could not parse wanted issue date '{closed_at}': {e}")
                 continue
 
+    # Process in-progress wanted issues - count in current month (ongoing work)
+    for agent_identifier, metadata_list in (wanted_in_progress_dict or {}).items():
+        agent_name = identifier_to_name.get(agent_identifier, agent_identifier)
+        # All in-progress issues count towards the current month
+        agent_month_data[agent_name][current_month_key]['in_progress'] += len(metadata_list)
+
     all_months = set()
     for agent_data in agent_month_data.values():
         all_months.update(agent_data.keys())
@@ -1097,13 +1108,26 @@ def calculate_monthly_metrics_by_wanted_issues(wanted_resolved_dict, assistants)
 
     result_data = {}
     for agent_name, month_dict in agent_month_data.items():
+        resolved_rates = []
+        total_wanted_list = []
         resolved_wanted_list = []
 
         for month in months:
-            resolved_count = len(month_dict.get(month, []))
+            month_data = month_dict.get(month, {'resolved': 0, 'in_progress': 0})
+            resolved_count = month_data['resolved']
+            in_progress_count = month_data['in_progress']
+            total_count = resolved_count + in_progress_count
+
+            # Resolved rate = resolved / total * 100
+            resolved_rate = (resolved_count / total_count * 100) if total_count > 0 else None
+
+            resolved_rates.append(resolved_rate)
+            total_wanted_list.append(total_count)
             resolved_wanted_list.append(resolved_count)
 
         result_data[agent_name] = {
+            'resolved_rates': resolved_rates,
+            'total_wanted': total_wanted_list,
             'resolved_wanted': resolved_wanted_list
         }
 
@@ -1288,7 +1312,7 @@ def mine_all_agents():
             agent_discussions, assistants
         )
         wanted_issue_monthly_metrics = calculate_monthly_metrics_by_wanted_issues(
-            wanted_resolved, assistants
+            wanted_resolved, wanted_in_progress, assistants
         )
         save_leaderboard_data_to_hf(
             leaderboard_dict, issue_monthly_metrics, wanted_open, discussion_monthly_metrics, wanted_issue_monthly_metrics