feat(dashboard): streamlit skeleton + Overview page + data layer

Aggiunge scheletro multipage Streamlit per Phase 1:
- modulo data.py con helper (list_runs_df, get_run_overview,
  generations_df, evaluations_df, genomes_df) sopra Repository.
- streamlit_app.py entry point con DB_PATH da env.
- pages/01_overview.py per elenco run + metriche + config JSON.
- smoke test import di multi_swarm.dashboard.data.

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

src/multi_swarm/dashboard/data.py

@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+import pandas as pd  # type: ignore[import-untyped]
+
+from ..persistence.repository import Repository
+
+
+def get_repo(db_path: str | Path) -> Repository:
+    return Repository(db_path=db_path)
+
+
+def list_runs_df(repo: Repository) -> pd.DataFrame:
+    return pd.DataFrame(repo.list_runs())
+
+
+def get_run_overview(repo: Repository, run_id: str) -> dict[str, Any]:
+    run = repo.get_run(run_id)
+    return {
+        "name": run["name"],
+        "started_at": run["started_at"],
+        "completed_at": run["completed_at"],
+        "status": run["status"],
+        "total_cost_usd": run["total_cost_usd"],
+        "config": json.loads(run["config_json"]),
+    }
+
+
+def generations_df(repo: Repository, run_id: str) -> pd.DataFrame:
+    return pd.DataFrame(repo.list_generations(run_id))
+
+
+def evaluations_df(repo: Repository, run_id: str) -> pd.DataFrame:
+    return pd.DataFrame(repo.list_evaluations(run_id))
+
+
+def genomes_df(
+    repo: Repository, run_id: str, generation_idx: int | None = None
+) -> pd.DataFrame:
+    rows = repo.list_genomes(run_id, generation_idx)
+    flat: list[dict[str, Any]] = []
+    for r in rows:
+        payload = json.loads(r["payload_json"])
+        flat.append(
+            {
+                "id": r["id"],
+                "generation_idx": r["generation_idx"],
+                **payload,
+            }
+        )
+    return pd.DataFrame(flat)

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

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+from __future__ import annotations
+
+import streamlit as st
+
+from multi_swarm.dashboard.data import get_repo, get_run_overview, list_runs_df
+
+st.title("Overview")
+
+db_path = st.session_state.get("db_path", "./runs.db")
+repo = get_repo(db_path)
+
+runs = list_runs_df(repo)
+if runs.empty:
+    st.info("Nessuna run nel database. Esegui `scripts/run_phase1.py` per generarne una.")
+    st.stop()
+
+st.subheader("Tutte le run")
+st.dataframe(runs[["id", "name", "started_at", "completed_at", "status", "total_cost_usd"]])
+
+selected = st.selectbox("Seleziona run per dettaglio", runs["id"].tolist())
+overview = get_run_overview(repo, selected)
+
+col1, col2, col3, col4 = st.columns(4)
+col1.metric("Status", overview["status"])
+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("Config")
+st.json(overview["config"])

src/multi_swarm/dashboard/streamlit_app.py

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+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import streamlit as st
+
+st.set_page_config(page_title="Multi-Swarm Phase 1", layout="wide")
+st.title("Multi-Swarm Coevolutivo — Phase 1 dashboard")
+st.markdown(
+    """
+Naviga le pagine nel menu a sinistra:
+- **Overview**: ultima run e stato globale.
+- **GA Convergence**: fitness per generazione.
+- **Genomes**: top-K genomi e ispezione qualitativa.
+"""
+)
+
+db_path = os.environ.get("DB_PATH", "./runs.db")
+st.session_state["db_path"] = db_path
+st.caption(f"DB path: `{Path(db_path).resolve()}`")

tests/integration/test_streamlit_smoke.py

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+import importlib
+
+
+def test_streamlit_app_imports():
+    importlib.import_module("multi_swarm.dashboard.data")
+
+
+def test_dashboard_data_helpers_signatures():
+    from multi_swarm.dashboard import data
+
+    assert hasattr(data, "list_runs_df")
+    assert hasattr(data, "generations_df")
+    assert hasattr(data, "evaluations_df")
+    assert hasattr(data, "genomes_df")