File size: 8,209 Bytes
de1e3fc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 | """Admin dataset management endpoints (auth-protected, under /admin).
Covers list/view/purge, image→dataset lookup, embed-all, and match-all. The dataset *load* job
endpoints live in api/jobs.py (background job + polling for the dashboard's load form).
"""
from __future__ import annotations
from datetime import date
from fastapi import APIRouter, Depends, HTTPException, status
from sqlalchemy import select
from sqlalchemy.orm import Session
from ..db import get_db
from ..models import Dataset, Picture, User
from ..models.base import SubjectType
from ..schemas.dataset import (
DatasetDetailOut,
DatasetOut,
EmbedAllResult,
ImageDatasetOut,
MatchDatasetResult,
PurgeResult,
)
from ..security import require_admin
from ..services import datasets as ds
router = APIRouter(prefix="/admin", tags=["admin-datasets"])
def _get_dataset(db: Session, dataset_id: int) -> Dataset:
dataset = db.get(Dataset, dataset_id)
if not dataset:
raise HTTPException(status.HTTP_404_NOT_FOUND, "Dataset not found")
return dataset
@router.get("/datasets", response_model=list[DatasetOut])
def list_datasets(
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> list[DatasetOut]:
datasets = db.execute(select(Dataset).order_by(Dataset.creation_time.desc())).scalars().all()
out: list[DatasetOut] = []
for d in datasets:
item = DatasetOut.model_validate(d)
item.case_count = ds.case_count(db, d)
out.append(item)
return out
@router.get("/datasets/{dataset_id}", response_model=DatasetDetailOut)
def get_dataset(
dataset_id: int,
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> DatasetDetailOut:
dataset = _get_dataset(db, dataset_id)
stats = ds.dataset_stats(db, dataset)
base = DatasetOut.model_validate(dataset)
base.case_count = stats["case_count"]
return DatasetDetailOut(**base.model_dump(), stats=stats)
@router.get("/test-match")
def test_match(
kind: str,
dog_id: int,
top_k: int = 10,
apply_breed_gate: bool = False,
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> dict:
"""Score one dog against ALL dogs in the opposite category by embedding similarity (testing)."""
if kind not in ("known", "unknown"):
raise HTTPException(status.HTTP_400_BAD_REQUEST, "kind must be 'known' or 'unknown'")
subject_type = SubjectType.known if kind == "known" else SubjectType.unknown
query = ds.dog_profile(db, subject_type, dog_id)
if query is None:
raise HTTPException(status.HTTP_404_NOT_FOUND, "Dog not found")
top_k = max(1, min(top_k, 50))
from ..services.matching import rank_dog_against_opposite
ranked, considered, query_embedded = rank_dog_against_opposite(
db, subject_type=subject_type, subject_id=dog_id, top_k=top_k,
apply_breed_gate=apply_breed_gate,
)
results = []
for cand_type, cid, score in ranked:
prof = ds.dog_profile(db, cand_type, cid)
if prof:
results.append({**prof, "score": score})
from ..ml import get_embedder
e = get_embedder()
return {
"query": query,
"results": results,
"model": f"{e.name}/{e.version}",
"candidate_count": considered,
"query_embedded": query_embedded,
}
@router.get("/dog/{kind}/{dog_id}")
def dog_detail(
kind: str,
dog_id: int,
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> dict:
"""A dog's profile plus ALL of its photos (full images) — for the profile viewer."""
if kind not in ("known", "unknown"):
raise HTTPException(status.HTTP_400_BAD_REQUEST, "kind must be 'known' or 'unknown'")
subject_type = SubjectType.known if kind == "known" else SubjectType.unknown
profile = ds.dog_profile(db, subject_type, dog_id)
if profile is None:
raise HTTPException(status.HTTP_404_NOT_FOUND, "Dog not found")
from .helpers import pictures_for
photos = [p.model_dump() for p in pictures_for(db, subject_type, dog_id)]
return {"profile": profile, "photos": photos}
@router.get("/breeds")
def list_breeds(
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> dict:
"""Predicted breed labels (HF softmax, active model) available to filter by."""
return {"model": ds.active_breed_model_in_db(db), "breeds": ds.list_breeds(db)}
@router.get("/dogs")
def list_all_dogs(
kind: str = "all",
limit: int = 30,
offset: int = 0,
breed: str | None = None,
breed_k: int = 10,
sort: str = "newest",
zip: str | None = None,
added_from: date | None = None,
added_to: date | None = None,
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> dict:
"""Browse every dog profile, filterable by predicted breed / ZIP / added-date and sortable.
``breed_k`` = 1 matches the top predicted breed; 5–10 matches within the top-K. ``sort`` is
``newest`` (default) or ``oldest``. ``zip`` matches a dog's ZIP by prefix; ``added_from`` /
``added_to`` bound the added date (inclusive).
"""
kind = kind if kind in ("all", "known", "unknown") else "all"
limit = max(1, min(limit, 100))
offset = max(0, offset)
breed_k = max(1, min(breed_k, 50))
sort = sort if sort in ("newest", "oldest") else "newest"
return ds.list_all_dogs(
db, kind, limit, offset, breed=breed or None, breed_k=breed_k,
sort=sort, zip_prefix=(zip or None), added_from=added_from, added_to=added_to,
)
@router.get("/datasets/{dataset_id}/dogs")
def list_dataset_dogs(
dataset_id: int,
limit: int = 25,
offset: int = 0,
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> dict:
dataset = _get_dataset(db, dataset_id)
limit = max(1, min(limit, 100))
return ds.list_dogs(db, dataset, limit, offset)
@router.delete("/datasets/{dataset_id}", response_model=PurgeResult)
def delete_dataset(
dataset_id: int,
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> PurgeResult:
dataset = _get_dataset(db, dataset_id)
deleted = ds.purge_dataset(db, dataset)
return PurgeResult(dataset_id=dataset_id, deleted=deleted)
@router.get("/image/{image_id}/dataset", response_model=ImageDatasetOut)
def image_dataset(
image_id: int,
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> ImageDatasetOut:
pic = db.get(Picture, image_id)
if not pic:
raise HTTPException(status.HTTP_404_NOT_FOUND, "Image not found")
from ..models import KnownDog, UnknownDog
if pic.subject_type == SubjectType.known:
dog = db.get(KnownDog, pic.subject_id)
else:
dog = db.get(UnknownDog, pic.subject_id)
dataset_id = getattr(dog, "dataset_id", None) if dog else None
dataset_name = None
if dataset_id:
d = db.get(Dataset, dataset_id)
dataset_name = d.name if d else None
return ImageDatasetOut(
image_id=image_id,
subject_type=pic.subject_type.value,
subject_id=pic.subject_id,
dataset_id=dataset_id,
dataset_name=dataset_name,
)
@router.post("/datasets/{dataset_id}/embed-all", response_model=EmbedAllResult)
def embed_all(
dataset_id: int,
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> EmbedAllResult:
dataset = _get_dataset(db, dataset_id)
result = ds.embed_all(db, dataset)
return EmbedAllResult(dataset_id=dataset_id, **result)
@router.post("/datasets/{dataset_id}/match", response_model=MatchDatasetResult)
def match_dataset(
dataset_id: int,
candidate_dataset_id: int | None = None,
_: User = Depends(require_admin),
db: Session = Depends(get_db),
) -> MatchDatasetResult:
dataset = _get_dataset(db, dataset_id)
result = ds.match_dataset(db, dataset, candidate_dataset_id=candidate_dataset_id)
return MatchDatasetResult(
dataset_id=dataset_id,
cases_processed=result["cases_processed"],
matches_created=result["matches_created"],
candidate_dataset_id=result["candidate_dataset_id"],
)
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