"""
Run history — saves and loads completed pipeline runs as JSON.
Each run is stored at data/output/run_history/run_YYYY-MM-DD_HH-MM-SS.json.
Keeps the full job list so results can be restored in the UI without rerunning.
"""
import os
import json
import logging
from pathlib import Path
from datetime import datetime

log = logging.getLogger("run_history")
HISTORY_DIR = Path("data/output/run_history")


def save_run(jobs: list, run_meta: dict) -> str:
    """
    Save a completed run to history.
    run_meta should include: run_id, excel_path, platforms, roles.
    Returns the history file path, or '' on failure.
    """
    HISTORY_DIR.mkdir(parents=True, exist_ok=True)
    run_id = run_meta.get("run_id") or datetime.now().strftime("%Y-%m-%d_%H-%M-%S")

    high    = sum(1 for j in jobs if j.get("relevance_score", 0) >= 8)
    med     = sum(1 for j in jobs if 6 <= j.get("relevance_score", 0) <= 7)
    resumes = sum(1 for j in jobs if j.get("resume_path"))
    pdfs    = sum(1 for j in jobs if j.get("resume_pdf_path"))

    ats_before = [j["ats_score_before"] for j in jobs
                  if j.get("ats_score_before") not in (None, "", 0)]
    ats_after  = [j["ats_score_after"]  for j in jobs
                  if j.get("ats_score_after")  not in (None, "", 0)]

    record = {
        "run_id":         run_id,
        "date":           datetime.now().strftime("%Y-%m-%d %H:%M"),
        "total_jobs":     len(jobs),
        "high_priority":  high,
        "med_priority":   med,
        "resumes":        resumes,
        "pdfs":           pdfs,
        "avg_ats_before": int(sum(ats_before) / len(ats_before)) if ats_before else 0,
        "avg_ats_after":  int(sum(ats_after)  / len(ats_after))  if ats_after  else 0,
        "platforms":      list(set(j.get("platform", "") for j in jobs if j.get("platform"))),
        "roles":          run_meta.get("roles", []),
        "excel_path":     run_meta.get("excel_path", ""),
        "jobs":           jobs,
    }

    path = HISTORY_DIR / f"run_{run_id}.json"
    try:
        with open(path, "w", encoding="utf-8") as f:
            json.dump(record, f, ensure_ascii=False, default=str)
        log.info(f"Run saved to history: {path}")
        # Best-effort: mirror to a private HF Dataset so history + resumes
        # survive HF Spaces restarts (no-ops when no HF token is configured).
        try:
            from .hf_storage import push_run, is_enabled
            if is_enabled():
                resume_files = [
                    j[k] for j in jobs for k in ("resume_path", "resume_pdf_path")
                    if j.get(k)
                ]
                # Also persist the generated report (Excel + its CSV sibling).
                xp = run_meta.get("excel_path", "")
                if xp:
                    resume_files.append(xp)
                    resume_files.append(os.path.splitext(xp)[0] + ".csv")
                push_run(str(path), resume_files)
        except Exception as _e:
            log.warning(f"HF dataset push skipped: {_e}")
        return str(path)
    except Exception as e:
        log.error(f"Failed to save run history: {e}")
        return ""


def list_runs() -> list:
    """
    List all saved runs, newest first.
    Returns list of summary dicts (no jobs key — kept light for UI listing).
    """
    if not HISTORY_DIR.exists():
        return []
    runs = []
    for p in sorted(HISTORY_DIR.glob("run_*.json"), reverse=True):
        try:
            with open(p, "r", encoding="utf-8") as f:
                data = json.load(f)
            summary = {k: v for k, v in data.items() if k != "jobs"}
            summary["_path"] = str(p)
            runs.append(summary)
        except Exception:
            pass
    return runs


def load_run(path: str) -> dict:
    """Load a full run (including jobs list) from a history JSON file."""
    try:
        with open(path, "r", encoding="utf-8") as f:
            return json.load(f)
    except Exception as e:
        log.error(f"Failed to load run {path}: {e}")
        return {}