"""
Catalog Management Endpoints

Provides API for viewing and enriching the data catalog.
"""

from fastapi import APIRouter, HTTPException, BackgroundTasks
from pydantic import BaseModel
from typing import List, Optional, Dict, Any

router = APIRouter()


class CatalogStatsResponse(BaseModel):
    total_datasets: int
    enriched_datasets: int
    by_category: Dict[str, int]
    by_tag: Dict[str, int]


class TableMetadataResponse(BaseModel):
    name: str
    path: str
    description: str
    semantic_description: Optional[str]
    tags: List[str]
    data_type: str
    columns: List[str]
    row_count: Optional[int]
    category: str
    last_indexed: Optional[str]
    last_enriched: Optional[str]


class EnrichmentRequest(BaseModel):
    table_names: Optional[List[str]] = None  # None = all tables
    force_refresh: bool = False


class EnrichmentResponse(BaseModel):
    status: str
    message: str
    tables_queued: int


@router.get("/stats", response_model=CatalogStatsResponse)
async def get_catalog_stats():
    """Get statistics about the data catalog."""
    from backend.core.data_catalog import get_data_catalog
    
    catalog = get_data_catalog()
    stats = catalog.get_stats()
    
    return CatalogStatsResponse(
        total_datasets=stats["total_datasets"],
        enriched_datasets=stats.get("enriched_datasets", 0),
        by_category=stats["by_category"],
        by_tag=stats["by_tag"]
    )


@router.get("/tables", response_model=List[TableMetadataResponse])
async def list_catalog_tables():
    """List all tables in the catalog with their metadata."""
    from backend.core.data_catalog import get_data_catalog
    
    catalog = get_data_catalog()
    tables = []
    
    for name, meta in catalog.catalog.items():
        tables.append(TableMetadataResponse(
            name=name,
            path=meta.get("path", ""),
            description=meta.get("description", ""),
            semantic_description=meta.get("semantic_description"),
            tags=meta.get("tags", []),
            data_type=meta.get("data_type", "static"),
            columns=meta.get("columns", []),
            row_count=meta.get("row_count"),
            category=meta.get("category", "unknown"),
            last_indexed=meta.get("last_indexed"),
            last_enriched=meta.get("last_enriched")
        ))
    
    return tables


@router.get("/tables/{table_name}", response_model=TableMetadataResponse)
async def get_table_metadata(table_name: str):
    """Get metadata for a specific table."""
    from backend.core.data_catalog import get_data_catalog
    
    catalog = get_data_catalog()
    meta = catalog.get_table_metadata(table_name)
    
    if not meta:
        raise HTTPException(status_code=404, detail=f"Table '{table_name}' not found")
    
    return TableMetadataResponse(
        name=table_name,
        path=meta.get("path", ""),
        description=meta.get("description", ""),
        semantic_description=meta.get("semantic_description"),
        tags=meta.get("tags", []),
        data_type=meta.get("data_type", "static"),
        columns=meta.get("columns", []),
        row_count=meta.get("row_count"),
        category=meta.get("category", "unknown"),
        last_indexed=meta.get("last_indexed"),
        last_enriched=meta.get("last_enriched")
    )


@router.post("/enrich", response_model=EnrichmentResponse)
async def enrich_catalog(request: EnrichmentRequest, background_tasks: BackgroundTasks):
    """
    Trigger LLM enrichment for catalog tables.
    
    Enrichment generates semantic descriptions and refined tags.
    Runs in the background to avoid blocking.
    """
    from backend.core.data_catalog import get_data_catalog
    
    catalog = get_data_catalog()
    
    if request.table_names:
        # Validate table names
        invalid = [t for t in request.table_names if t not in catalog.catalog]
        if invalid:
            raise HTTPException(
                status_code=400, 
                detail=f"Unknown tables: {invalid}"
            )
        tables_to_enrich = request.table_names
    else:
        tables_to_enrich = list(catalog.catalog.keys())
    
    # Queue enrichment in background
    async def run_enrichment():
        for table_name in tables_to_enrich:
            await catalog.enrich_table(table_name, request.force_refresh)
    
    background_tasks.add_task(run_enrichment)
    
    return EnrichmentResponse(
        status="queued",
        message=f"Enrichment started for {len(tables_to_enrich)} tables",
        tables_queued=len(tables_to_enrich)
    )


@router.post("/enrich/{table_name}")
async def enrich_single_table(table_name: str, force: bool = False):
    """
    Immediately enrich a single table (synchronous).
    
    Use for testing or when you need the result right away.
    """
    from backend.core.data_catalog import get_data_catalog
    
    catalog = get_data_catalog()
    
    if table_name not in catalog.catalog:
        raise HTTPException(status_code=404, detail=f"Table '{table_name}' not found")
    
    success = await catalog.enrich_table(table_name, force)
    
    if success:
        meta = catalog.get_table_metadata(table_name)
        return {
            "status": "success",
            "table": table_name,
            "semantic_description": meta.get("semantic_description"),
            "tags": meta.get("tags", [])
        }
    else:
        raise HTTPException(status_code=500, detail=f"Failed to enrich table '{table_name}'")


@router.get("/search")
async def search_tables(query: str, top_k: int = 10):
    """
    Search for tables using semantic search.
    
    Returns the most relevant tables for a natural language query.
    """
    from backend.core.semantic_search import get_semantic_search
    from backend.core.data_catalog import get_data_catalog
    
    semantic = get_semantic_search()
    catalog = get_data_catalog()
    
    results = semantic.search(query, top_k=top_k)
    
    response = []
    for table_name, score in results:
        meta = catalog.get_table_metadata(table_name)
        if meta:
            response.append({
                "table": table_name,
                "score": round(score, 4),
                "description": meta.get("semantic_description") or meta.get("description"),
                "tags": meta.get("tags", [])
            })
    
    return {"query": query, "results": response}


@router.post("/rebuild-embeddings")
async def rebuild_embeddings():
    """
    Rebuild all semantic search embeddings from current catalog.
    
    Use after bulk enrichment or catalog updates.
    """
    from backend.core.semantic_search import get_semantic_search
    from backend.core.data_catalog import get_data_catalog
    
    semantic = get_semantic_search()
    catalog = get_data_catalog()
    
    # Force re-embed all tables
    count = 0
    for table_name, metadata in catalog.catalog.items():
        if semantic.embed_table(table_name, metadata):
            count += 1
    
    semantic._save_embeddings()
    
    return {
        "status": "success",
        "message": f"Rebuilt embeddings for {count} tables",
        "total_embeddings": len(semantic.embeddings)
    }