"""
Search API Blueprint

Handles endpoints related to searching indexed files.
"""

from dataclasses import dataclass
from typing import TYPE_CHECKING

from quart import Blueprint, current_app, request
from quart_schema import validate_request, validate_response

from cosma_backend.api.models import FileResponse

if TYPE_CHECKING:
    from cosma_backend.app import app as current_app

search_bp = Blueprint('search', __name__)


@dataclass
class SearchRequest:
    """Request body for searching files"""
    query: str
    filters: dict[str, str] | None = None
    limit: int = 50
    directory: str | None = None
    offset: int = 0


@dataclass
class SearchResultItem:
    """A single search result"""
    file: FileResponse
    relevance_score: float


@dataclass
class SearchResponse:
    """Response for search queries"""
    results: list[SearchResultItem]
    total_count: int = 0


@search_bp.post("/")  # type: ignore[return-value]
@validate_request(SearchRequest)
@validate_response(SearchResponse, 200)
async def search(data: SearchRequest) -> tuple[SearchResponse, int]:
    """Search for files based on query"""
    # TODO: Implement search functionality
    # 1. Parse query and filters
    # 2. Search database (could use FTS if implemented)
    # 3. Rank results by relevance
    # 4. Return sorted results
    results = await current_app.searcher.search(data.query, directory=data.directory)
    total_count = len(results)
    paginated = results[data.offset:data.offset + data.limit]

    return SearchResponse(
        results=[
            SearchResultItem(
                r.file_metadata.to_response(), r.combined_score
            )
            for r in paginated],
        total_count=total_count,
    ), 200


@dataclass
class KeywordSearchRequest:
    """Request body for keyword-based search"""
    keywords: list[str]
    match_all: bool = False


# @search_bp.post("/keywords")  # type: ignore[return-value]
# @validate_request(KeywordSearchRequest)
# @validate_response(SearchResponse, 200)
# async def search_by_keywords(data: KeywordSearchRequest) -> tuple[SearchResponse, int]:
#     """
#     Search for files by keywords.
#     
#     POST /api/search/keywords
#     
#     Request body:
#         {
#             "keywords": ["python", "api", "database"],
#             "match_all": false
#         }
#     
#     Returns:
#         200: Search results matching keywords
#     """
#     # TODO: Implement keyword search
#     # 1. Query files with matching keywords
#     # 2. If match_all=true, require all keywords
#     # 3. If match_all=false, match any keyword
#     # 4. Rank by number of matching keywords
#     
#     return SearchResponse(
#         results=[],
#         total=0,
#         query=f"Keywords: {', '.join(data.keywords)}"
#     ), 200


@dataclass
class SimilarFilesResponse:
    """Response for similar files query"""
    files: list[SearchResultItem]
    total: int


# @search_bp.get("/<int:file_id>/similar")  # type: ignore[return-value]
# @validate_response(SimilarFilesResponse, 200)
# async def find_similar_files(file_id: int) -> tuple[SimilarFilesResponse, int]:
#     """
#     Find files similar to a given file.
#     
#     GET /api/search/{file_id}/similar
#     
#     Query parameters:
#         limit: Maximum number of results (default: 10)
#     
#     Returns:
#         200: Similar files
#         404: Source file not found
#     """
#     # TODO: Implement similarity search
#     # 1. Get the source file
#     # 2. Compare keywords/summaries with other files
#     # 3. Rank by similarity
#     # 4. Return top N results
#     
#     return SimilarFilesResponse(
#         files=[],
#         total=0
#     ), 200


@dataclass
class AutocompleteResponse:
    """Response for autocomplete suggestions"""
    suggestions: list[str]


# @search_bp.get("/autocomplete")  # type: ignore[return-value]
# @validate_response(AutocompleteResponse, 200)
# async def autocomplete() -> tuple[AutocompleteResponse, int]:
#     """
#     Get autocomplete suggestions for search queries.
#     
#     GET /api/search/autocomplete?q=py
#     
#     Query parameters:
#         q: Partial query string
#         limit: Maximum suggestions (default: 10)
#     
#     Returns:
#         200: List of suggestions
#     """
#     # TODO: Implement autocomplete
#     # 1. Get partial query from request args
#     # 2. Search for matching filenames, keywords, or common terms
#     # 3. Return suggestions
#     
#     query = request.args.get('q', '')
#     
#     return AutocompleteResponse(
#         suggestions=[]
#     ), 200