import os
import asyncio
import logging
from typing import List, Tuple

from app.models.schemas import Candidate

logger = logging.getLogger(__name__)


class MatchService:
    """
    Stage 2: Embedding-based semantic matching using Pinecone + SentenceTransformers.
    Stores candidate embeddings, queries with JD embedding, returns top-K candidates.
    """

    def __init__(self):
        self._model = None
        self._index = None
        self._initialized = False

    def _lazy_init(self):
        """Defer heavy imports until first use to keep startup fast."""
        if self._initialized:
            return

        try:
            from pinecone import Pinecone
            from sentence_transformers import SentenceTransformer

            api_key = os.getenv("PINECONE_API_KEY", "")
            index_name = os.getenv("PINECONE_INDEX", "recruitment-index")
            model_name = os.getenv("EMBEDDING_MODEL", "BAAI/bge-m3")

            if not api_key:
                raise ValueError("PINECONE_API_KEY not set in environment.")

            logger.info(f"[MatchService] Connecting to Pinecone index: {index_name}")
            pc = Pinecone(api_key=api_key)
            self._index = pc.Index(index_name)

            logger.info(f"[MatchService] Loading embedding model: {model_name}")
            self._model = SentenceTransformer(model_name)

            self._initialized = True
            logger.info("[MatchService] Ready.")

        except Exception as e:
            logger.error(f"[MatchService] Initialization failed: {e}")
            raise

    def get_embedding(self, text: str) -> List[float]:
        self._lazy_init()
        return self._model.encode(text, normalize_embeddings=True).tolist()

    def _build_search_text(self, c: Candidate) -> str:
        parts = [
            c.name or "",
            c.skills or "",
            c.experience or "",
            c.projects or "",
            c.education or "",
            c.resume_text or "",
        ]
        return " ".join(p for p in parts if p.strip())

    async def get_top_candidates(
        self, jd: str, candidates: List[Candidate], top_k: int = None
    ) -> List[Candidate]:
        """
        1. Embed all candidates and upsert to Pinecone.
        2. Embed JD and query Pinecone.
        3. Return top_k candidates sorted by similarity.
        """
        if top_k is None:
            top_k = int(os.getenv("STAGE2_TOP_K", "20"))

        self._lazy_init()
        candidate_map = {c.id: c for c in candidates}

        # Build and embed vectors (run in thread to avoid blocking event loop)
        loop = asyncio.get_event_loop()

        def build_vectors():
            vectors = []
            for c in candidates:
                text = self._build_search_text(c)
                embedding = self.get_embedding(text)
                vectors.append({
                    "id": c.id,
                    "values": embedding,
                    "metadata": {
                        "name": c.name,
                        "email": c.email or "",
                    },
                })
            return vectors

        logger.info(f"[MatchService] Embedding {len(candidates)} candidates...")
        vectors = await loop.run_in_executor(None, build_vectors)

        # Upsert in batches of 100 (Pinecone limit)
        batch_size = 100
        for i in range(0, len(vectors), batch_size):
            batch = vectors[i: i + batch_size]
            self._index.upsert(vectors=batch)

        # Embed JD and query
        logger.info("[MatchService] Querying Pinecone with JD embedding...")
        jd_embedding = await loop.run_in_executor(None, self.get_embedding, jd)

        effective_k = min(top_k, len(candidates))
        query_results = self._index.query(
            vector=jd_embedding,
            top_k=effective_k,
            include_metadata=True,
        )

        top_candidates: List[Candidate] = []
        for match in query_results.matches:
            if match.id in candidate_map:
                top_candidates.append(candidate_map[match.id])

        logger.info(f"[MatchService] Retrieved {len(top_candidates)} top candidates.")
        return top_candidates

    async def cleanup_index(self, candidate_ids: List[str]):
        """Optional: remove candidate vectors after evaluation to keep index clean."""
        try:
            self._index.delete(ids=candidate_ids)
            logger.info(f"[MatchService] Cleaned up {len(candidate_ids)} vectors from index.")
        except Exception as e:
            logger.warning(f"[MatchService] Cleanup failed: {e}")


match_service = MatchService()