"""
summarizer.py — glue logic: load → build context → call LLM → collect results

Public API
----------
- summarize_grants(current: list[dict], past_winners: list[dict] | None = None,
                    *, limit: int | None = None, include_context: bool = False) -> list[dict]

Returns a list of dicts with keys:
  grant_id, title, summary_md, context(optional), source_path(optional)

This file intentionally stays light; exporting and CLI live elsewhere.
"""
from __future__ import annotations

from typing import Any, Dict, Iterable, List, Optional
from pathlib import Path
import logging

from .context_builder import build_context
from .llm_client import LLMClient

logger = logging.getLogger(__name__)

# ----------------------------- Core API ---------------------------------------

def summarize_grants(
    current: List[Dict[str, Any]],
    past_winners: Optional[List[Dict[str, Any]]] = None,
    *,
    limit: Optional[int] = None,
    include_context: bool = False,
    client: Optional[LLMClient] = None,
) -> List[Dict[str, Any]]:
    """Summarize a batch of grants using an LLM.

    Parameters
    ----------
    current : list of grant dicts (from data_loader.load_current_grants)
    past_winners : optional list of past winner dicts (may be empty)
    limit : if provided, process at most this many grants
    include_context : whether to include the raw context text in the result
    client : optional pre-initialized LLMClient
    """
    client = client or LLMClient()

    # For this MVP, we pass the *same* past_winners list to every grant.
    # Later you can add filtering by theme if you want.
    results: List[Dict[str, Any]] = []

    items = current[: limit or len(current)]
    for i, g in enumerate(items, 1):
        grant_id = g.get("id") or g.get("title") or g.get("name") or f"grant_{i}"
        title = g.get("title") or g.get("name") or g.get("competition_title") or "(untitled)"
        try:
            ctx = build_context(g, past_winners)
            summary = client.summarize(ctx)
            row = {
                "grant_id": grant_id,
                "title": title,
                "summary_md": summary,
            }
            if include_context:
                row["context"] = ctx
            if g.get("_path"):
                row["source_path"] = g["_path"]
            results.append(row)
            logger.info("Summarized: %s", title)
        except Exception as e:  # keep going even if one fails
            logger.exception("Failed to summarize %s: %s", title, e)
            results.append({
                "grant_id": grant_id,
                "title": title,
                "summary_md": f"Summary failed: {e}",
            })
    return results


# ----------------------------- Ad-hoc test ------------------------------------
if __name__ == "__main__":
    # Tiny smoke test using fake data
    logging.basicConfig(level=logging.INFO)
    fake_current = [
        {
            "id": "demo-1",
            "title": "AI in Manufacturing",
            "sections": {
                "summary_raw": "Funding for AI-driven manufacturing improvements.",
                "scope_raw": "Projects should demonstrate measurable productivity gains.",
            },
            "deadline": "2025-12-17",
            "funding_amount": "up to £1M",
        }
    ]
    fake_history = [
        {
            "project_title": "Smart Factory Vision",
            "lead_org": "Acme Robotics",
            "award_amount": "£450,000",
            "competition": "Manufacturing AI 2023",
            "abstract": "Computer vision for automated QA on production lines.",
        }
    ]

    out = summarize_grants(fake_current, fake_history, limit=1, include_context=True)
    from pprint import pprint
    pprint(out)