import os
import zvec
from typing import Dict, Any, Optional, List, Tuple


class DataManager:
    def __init__(self, collection_path: str = "data/performers.zvec"):
        self.collection_path = collection_path
        self._metadata_cache: Dict[str, Dict] = {}
        self.collection = None
        self._load_collection()

    def _load_collection(self):
        try:
            self.collection = zvec.open(
                path=self.collection_path,
                option=zvec.CollectionOption(read_only=True, enable_mmap=True),
            )
            print(f"Collection loaded OK from {self.collection_path}")
        except Exception as e:
            import traceback
            print(f"FATAL: failed to load collection from {self.collection_path}: {e}")
            traceback.print_exc()

    def get_performer_info(self, stash_id: str, confidence: float, distance: float = 0.0) -> Optional[Dict[str, Any]]:
        meta = self._metadata_cache.get(stash_id)
        if not meta:
            return None

        confidence_int = int(confidence * 100)
        return {
            'id': stash_id,
            'name': meta.get("name", ""),
            'confidence': confidence_int,
            'distance': round(distance, 4),
            'image': meta.get("image", ""),
            'country': meta.get("country") or None,
            'hits': 1,
            'performer_url': f"https://stashdb.org/performers/{stash_id}"
        }

    def _query(self, field_name: str, embedding, limit: int) -> Tuple[List[str], List[float]]:
        if self.collection is None:
            return [], []

        results = self.collection.query(
            vectors=zvec.VectorQuery(field_name=field_name, vector=embedding.tolist()),
            topk=limit,
        )

        ids = []
        distances = []
        for doc in results:
            doc_id = doc.id if hasattr(doc, 'id') else doc['id']
            doc_score = doc.score if hasattr(doc, 'score') else doc['score']
            doc_fields = doc.fields if hasattr(doc, 'fields') else doc.get('fields', {})

            ids.append(doc_id)
            distances.append(doc_score)
            self._metadata_cache[doc_id] = doc_fields

        return ids, distances

    def query_facenet_index(self, embedding, limit: int) -> Tuple[List[str], List[float]]:
        return self._query("facenet", embedding, limit)

    def query_arc_index(self, embedding, limit: int) -> Tuple[List[str], List[float]]:
        return self._query("arc", embedding, limit)

    def query_multi(self, facenet_emb, arc_emb, limit: int) -> Tuple[List[str], List[float]]:
        if self.collection is None:
            return [], []

        results = self.collection.query(
            vectors=[
                zvec.VectorQuery(field_name="facenet", vector=facenet_emb.tolist()),
                zvec.VectorQuery(field_name="arc", vector=arc_emb.tolist()),
            ],
            topk=limit,
            reranker=zvec.WeightedReRanker(
                topn=limit,
                metric=zvec.MetricType.COSINE,
                weights={"facenet": 1.0, "arc": 1.0},
            ),
        )

        ids = []
        scores = []
        for doc in results:
            doc_id = doc.id if hasattr(doc, 'id') else doc['id']
            doc_score = doc.score if hasattr(doc, 'score') else doc['score']
            doc_fields = doc.fields if hasattr(doc, 'fields') else doc.get('fields', {})
            ids.append(doc_id)
            scores.append(doc_score)
            self._metadata_cache[doc_id] = doc_fields

        return ids, scores