"""
run.py
======
CLI entry point. Demonstrates the full pipeline and prints a QA summary.

Usage:
    # Extraction + validation only (no API key needed):
    python run.py path/to/Master_Sheet.xlsx --no-llm

    # Full run with narratives (requires GOOGLE_API_KEY):
    python run.py path/to/Master_Sheet.xlsx --developer "Green Edge Consortium"

The mini-grid capacities are an external input (developer-supplied, not in the
survey data). Pass them with --solar-pv / --battery / --annual-consumption, or
fill them in afterwards on the returned object before rendering.
"""

from __future__ import annotations

import argparse

from pue_report_agent import extract_master_sheet, validate, recompute
from pue_report_agent.schema import EnergySystemSpec


def main() -> None:
    ap = argparse.ArgumentParser(description="PUE report builder")
    ap.add_argument("master_sheet")
    ap.add_argument("--developer", default="the mini-grid developer")
    ap.add_argument("--no-llm", action="store_true",
                    help="skip narrative generation (no API key required)")
    ap.add_argument("--solar-pv", default="")
    ap.add_argument("--battery", default="")
    ap.add_argument("--annual-consumption", default="")
    ap.add_argument("--date", default="", help="report date for the title page")
    ap.add_argument("--docx-out", default="",
                    help="render a template-faithful .docx to this path")
    ap.add_argument("--json-out", default="",
                    help="write the full PUEReportData JSON to this path")
    ap.add_argument("--developer-logo", default="",
                    help="optional path to developer logo image "
                         "(placed on the left of the page header)")
    ap.add_argument("--dreef-logo", default="",
                    help="optional path to DREEF logo image "
                         "(placed on the right of the page header)")
    args = ap.parse_args()

    # 1. Deterministic extraction
    data = extract_master_sheet(args.master_sheet)

    # 2. Recompute every total from line items (never trust a sheet total)
    data = recompute(data)

    # 3. External developer-supplied capacity overrides (optional). Merge into
    #    the already-extracted minigrid so we don't discard the distribution
    #    metrics / inverter that extraction pulled from Key Findings.
    if args.solar_pv:
        data.minigrid.solar_pv = args.solar_pv
    if args.battery:
        data.minigrid.battery_storage = args.battery
    if args.annual_consumption:
        data.minigrid.annual_consumption = args.annual_consumption

    # 4. Validation / reconciliation
    data = validate(data)

    # 5. Narratives (optional). All narratives use Gemini.
    if not args.no_llm:
        from pue_report_agent import generate_narratives
        if generate_narratives is None:
            print("[warn] langchain not installed; skipping narratives.")
        else:
            data = generate_narratives(data)

    # 6. QA summary
    _print_summary(data)

    # 7. Render the template-faithful Word document
    if args.docx_out:
        from pue_report_agent.render import render
        render(data, args.docx_out, developer=args.developer,
               report_date=args.date,
               developer_logo_path=args.developer_logo or None,
               dreef_logo_path=args.dreef_logo or None)
        print(f"\nWrote {args.docx_out}")

    if args.json_out:
        with open(args.json_out, "w") as fh:
            fh.write(data.model_dump_json(indent=2))
        print(f"Wrote {args.json_out}")


def _print_summary(data) -> None:
    c = data.community
    print("=" * 64)
    print(f" {c.name}  ({c.lga}, {c.state})")
    print("=" * 64)
    print(f" Interviewed: {data.interviews.total}  "
          f"(farmers {data.interviews.farmers}, processors "
          f"{data.interviews.agroprocessors}, SME {data.interviews.sme}, "
          f"mobility {data.interviews.mobility})")
    print(f" Farmers: {data.farmers.male}M / {data.farmers.female}F, "
          f"{len(data.farmers.crops)} crops")
    print(f" Processors: {data.processors.male}M / {data.processors.female}F, "
          f"{len(data.processors.machinery)} machines on record")
    print(f" SME types: {len(data.sme.types)}   Mobility: {data.mobility.total}")
    print(f" Pilot energy: {data.equipment_pilot.projection_total_kwh} kWh   "
          f"ScaleUp: {data.equipment_scaleup.projection_total_kwh} kWh")
    print(f" Budget grand total: ₦{(data.budget.grand_total or 0):,.0f}")
    print(f" Financial models: {[m.model_name for m in data.financial_models]}")
    print()
    if data.warnings:
        print(f" ⚠  {len(data.warnings)} item(s) need review:")
        for w in data.warnings:
            print(f"    • {w}")
    else:
        print(" ✓ no warnings")


if __name__ == "__main__":
    main()