Adriano
889903fdae
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>
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
from __future__ import annotations
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import json
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from pathlib import Path
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from typing import Any
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import pandas as pd # type: ignore[import-untyped]
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from ..persistence.repository import Repository
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def get_repo(db_path: str | Path) -> Repository:
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return Repository(db_path=db_path)
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def list_runs_df(repo: Repository) -> pd.DataFrame:
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return pd.DataFrame(repo.list_runs())
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def get_run_overview(repo: Repository, run_id: str) -> dict[str, Any]:
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run = repo.get_run(run_id)
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return {
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"name": run["name"],
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"started_at": run["started_at"],
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"completed_at": run["completed_at"],
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"status": run["status"],
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"total_cost_usd": run["total_cost_usd"],
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"config": json.loads(run["config_json"]),
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}
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def generations_df(repo: Repository, run_id: str) -> pd.DataFrame:
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return pd.DataFrame(repo.list_generations(run_id))
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def evaluations_df(repo: Repository, run_id: str) -> pd.DataFrame:
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return pd.DataFrame(repo.list_evaluations(run_id))
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def genomes_df(
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repo: Repository, run_id: str, generation_idx: int | None = None
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) -> pd.DataFrame:
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rows = repo.list_genomes(run_id, generation_idx)
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flat: list[dict[str, Any]] = []
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for r in rows:
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payload = json.loads(r["payload_json"])
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flat.append(
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{
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"id": r["id"],
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"generation_idx": r["generation_idx"],
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**payload,
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}
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)
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return pd.DataFrame(flat)
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