dtoro
574d71371f
Drops face recognition, OCR, object detection, and semantic embeddings. The sole remaining vision task is a CLIP-based binary classifier (photography vs other); photos in "other" get needs_review=true so screenshots, documents, memes and scans can be triaged from a new filter pill in the UI. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
"""
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Search API router — unified hybrid search endpoint.
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"""
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from typing import Optional
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from fastapi import APIRouter, Depends
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from pydantic import BaseModel
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from app.database import get_db
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from app.models import Photo
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from app.services.search import hybrid_search
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from app.models.user import User
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from app.dependencies import get_current_user
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router = APIRouter()
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class SearchRequest(BaseModel):
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q: Optional[str] = None
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filters: Optional[dict] = None
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limit: int = 50
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offset: int = 0
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@router.post("")
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async def search_photos(body: SearchRequest, db: AsyncSession = Depends(get_db), current_user: User = Depends(get_current_user)):
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"""FTS search over photo metadata with optional tag and date filters."""
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filters = body.filters or {}
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results = await hybrid_search(
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db=db,
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q=body.q,
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tag_ids=filters.get("tag_ids"),
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date_from=filters.get("date_from"),
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date_to=filters.get("date_to"),
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limit=body.limit,
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offset=body.offset,
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)
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if not results:
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return {"results": [], "total": 0}
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# Hydrate with photo data
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photo_ids = [r["photo_id"] for r in results]
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stmt = select(Photo).where(Photo.id.in_(photo_ids), Photo.user_id == current_user.id)
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rows = (await db.execute(stmt)).scalars().all()
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photo_map = {p.id: p for p in rows}
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hydrated = []
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for r in results:
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photo = photo_map.get(r["photo_id"])
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if not photo:
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continue
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hydrated.append({
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"id": photo.id,
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"filename": photo.filename,
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"filepath": photo.filepath,
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"media_type": photo.media_type,
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"width": photo.width,
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"height": photo.height,
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"taken_at": photo.taken_at.isoformat() if photo.taken_at else None,
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"rating": photo.rating,
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"color_label": photo.color_label,
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"thumb_small": photo.thumb_small,
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"thumb_medium": photo.thumb_medium,
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"score": r["score"],
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})
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return {"results": hydrated, "total": len(hydrated)}
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