domverse
457dd5bd90
Ports the safety review and the Photo Analyzer Library/Analyze/Stats experiences onto the shared API + service layer, and adds the Workflow home, enforcing the pipeline gates and the one-mutating-job policy. Backend - migration 0005 + models: safety_reviews (append-only, latest row is the current decision) and analysis_results (donor photos schema re-keyed to asset_id). - SafetyService: persist scores/decisions, review queue with filters, and the EXIF safety checkpoint (mutually-exclusive sfw/nsfw keyword written, read back, current_sha256 refreshed) that upload eligibility depends on. - AnalysisService: the privacy gate — the vision provider is called ONLY for canonical, confirmed-SFW assets; nsfw/undecided are recorded skipped without a request. Provider is an injected adapter (real OpenAI-compatible Gemini call extracted from photo_analyzer.analyze_image; a fake in tests). - LibraryService: Library search + Stats read model ported from webapp/query.py (LIKE search in place of FTS5; facets, top tags, years, albums, people). - WorkflowService + GET /api/v1/workflow: per-stage readiness derived from the source tables — counts, blockers, last-run, action, and an active_job that drives read-only-during-jobs. Safety scoring and analysis run as durable jobs under the library_write lock via new domain handlers, so a second mutating job is refused. - routes: workflow, safety (queue/counts/decisions/jobs), analysis (counts/results/jobs), library (assets/facets/stats). Frontend - five views (frontend/js/views.js) on the US02-05 shell: Workflow stepper (status text+icon, not colour alone; actions disabled with a reason while a job runs), Safety review (filter tabs, decide, persists across reload), Library (search + cards), Analyze (counts + live job log via the SSE adapter), Stats. Shared DOM helpers extracted to dom.js; Workflow is the home route. Tests - integration: provider-call privacy (nsfw never reaches the provider), sfw→nsfw flip drops analysis eligibility, decision persistence, one-mutating- job rejection, workflow counts, and the exiftool safety-keyword write/verify. - e2e: Workflow cards, actions disabled+explained during a job, safety decide-persists-across-reload, Library search, Stats, Analyze counts. - traceability map updated for US02-05 and US02-06. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
67 lines
3.3 KiB
Python
67 lines
3.3 KiB
Python
"""Safety review and content-analysis persistence (US02-06).
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Both tables key on the stable ``asset_id``, not a path — the donors used a
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path-keyed ``nsfw_scores.csv`` and a path-keyed ``photos`` table, which broke on
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every move/rename. ``safety_reviews`` is append-only history; the latest row per
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asset is the current decision (the privacy gate reads it). ``analysis_results`` is
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one current row per asset (the donor photos schema re-keyed to asset identity).
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Per-stage ``asset_stage_states``/``exif_projections`` from the concept are not
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modelled here: the workflow view derives its counts directly from these source
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tables plus duplicate clusters, which is enough for this story's gates.
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ponytail: add the full stage-state projection when a stage needs history the
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source tables can't reconstruct.
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"""
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from __future__ import annotations
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from datetime import datetime
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from sqlalchemy import DateTime, ForeignKey, Integer, String, func
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from sqlalchemy.orm import Mapped, mapped_column
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from photo_pipeline.db import Base
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class SafetyReview(Base):
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__tablename__ = "safety_reviews"
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id: Mapped[str] = mapped_column(String, primary_key=True)
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asset_id: Mapped[str] = mapped_column(ForeignKey("assets.id"), nullable=False, index=True)
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# score without decision = scored-but-unreviewed; decision without score = manual.
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score: Mapped[float | None] = mapped_column()
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decision: Mapped[str | None] = mapped_column(String) # sfw | nsfw | deferred
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prior_decision: Mapped[str | None] = mapped_column(String)
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reviewer: Mapped[str | None] = mapped_column(String)
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# Set once the mutually-exclusive safety keyword is written to EXIF and read
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# back — upload eligibility depends on this verified checkpoint.
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exif_verified_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
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result_sha256: Mapped[str | None] = mapped_column(String)
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created_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, server_default=func.now()
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)
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class AnalysisResult(Base):
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__tablename__ = "analysis_results"
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asset_id: Mapped[str] = mapped_column(ForeignKey("assets.id"), primary_key=True)
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# pending | analyzed | error | skipped_nsfw
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status: Mapped[str] = mapped_column(String, nullable=False, default="pending")
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description: Mapped[str | None] = mapped_column(String)
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tags: Mapped[str | None] = mapped_column(String) # JSON array string (donor shape)
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people_count: Mapped[int | None] = mapped_column(Integer)
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setting: Mapped[str | None] = mapped_column(String)
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time_of_day: Mapped[str | None] = mapped_column(String)
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season: Mapped[str | None] = mapped_column(String)
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mood: Mapped[str | None] = mapped_column(String)
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location_hint: Mapped[str | None] = mapped_column(String)
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approx_year: Mapped[int | None] = mapped_column(Integer)
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model: Mapped[str | None] = mapped_column(String)
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prompt_version: Mapped[str | None] = mapped_column(String)
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tokens_total: Mapped[int | None] = mapped_column(Integer)
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raw_response: Mapped[str | None] = mapped_column(String)
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error_message: Mapped[str | None] = mapped_column(String)
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analyzed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
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exif_written_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
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