chore(dashboard): mypy ignores + parse-success metric in Overview

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

scripts/smoke_run.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from pathlib import Path
 
 import numpy as np
-import pandas as pd
+import pandas as pd  # type: ignore[import-untyped]
 
 from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
 from multi_swarm.llm.client import CompletionResult

src/multi_swarm/dashboard/pages/01_overview.py

@@ -2,7 +2,12 @@ from __future__ import annotations
 
 import streamlit as st
 
-from multi_swarm.dashboard.data import get_repo, get_run_overview, list_runs_df
+from multi_swarm.dashboard.data import (
+    evaluations_df,
+    get_repo,
+    get_run_overview,
+    list_runs_df,
+)
 
 st.title("Overview")
 
@@ -26,5 +31,17 @@ col2.metric("Cost (USD)", f"{overview['total_cost_usd']:.4f}")
 col3.metric("Started", overview["started_at"])
 col4.metric("Completed", overview["completed_at"] or "—")
 
+st.subheader("Statistiche evaluations")
+evals = evaluations_df(repo, selected)
+col5, col6, col7, col8 = st.columns(4)
+if not evals.empty:
+    parse_success = 100 * (evals["parse_error"].isna().sum() / len(evals))
+    col5.metric("Evaluations totali", len(evals))
+    col6.metric("Parse success %", f"{parse_success:.1f}%")
+    col7.metric("Top fitness", f"{evals['fitness'].max():.3f}")
+    col8.metric("Median fitness", f"{evals['fitness'].median():.3f}")
+else:
+    col5.metric("Evaluations totali", 0)
+
 st.subheader("Config")
 st.json(overview["config"])

src/multi_swarm/dashboard/pages/02_ga_convergence.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import plotly.graph_objects as go
+import plotly.graph_objects as go  # type: ignore[import-untyped]
 import streamlit as st
 
 from multi_swarm.dashboard.data import generations_df, get_repo, list_runs_df