from __future__ import annotations

import streamlit as st

from multi_swarm.dashboard.data import (
    evaluations_df,
    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("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"])