feat(greenfield): rebuild SlabUploader M1-M3 on clean foundation, all gates green
Clean re-implementation of the full product (not a port of the old M3 tree): - P0 foundation: 13-table schema + per-call sqlite factory (thread-safe), FastAPI app (health, JSON logging + secret redaction, CORS, startup guard). - P1 M1 Capture: mask/sheetKeep, geometry/rect (min-area rect), measure, png crop/inventory/overlay, EXIF decodeOrientedBitmap. - P2 M2 Photos: stateful capture session (batch, pick-cal, apply mask/crop to batch) + photos-ready lineup. - P3 M3 Create: real backend (crypto, woo client, taxonomy sync, pricing, inference Call1/Call2, review, publish, settings, slabs, photos) + real FE review/publish/settings screens consuming the API (no hardcoded Woo ids). - P4 gate exit: check_requirements + check_coverage both exit 0 (137/155 IDs, 0 gaps); backend 130/130, frontend 150/150, build green. Cite: docs/REQUIREMENTS.md (155 rows), docs/MILESTONES.md M1-M3.
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"""Inference core (no FastAPI): OpenAI-compatible client, METADATA-35 image
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sizing, closed taxonomy lists, the METADATA-45 confidence gate, and the
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METADATA-66 log redaction. Routers in inference.py compose these.
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"""
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from __future__ import annotations
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import base64
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import io
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import json
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import time
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from typing import Any
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import httpx
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from PIL import Image
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from .crypto import decrypt
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_INFER_MAX_LONG_EDGE = 1024
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# METADATA-45: confidence gate threshold.
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_THRESHOLD = 0.7
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# METADATA-66 / #87: inference_log payload cap (safety net after redaction).
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_LOG_PAYLOAD_CAP = 200_000
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def fit_long_edge_png(png_bytes: bytes, max_long_edge: int = _INFER_MAX_LONG_EDGE) -> bytes:
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"""Downscale a PNG so max(width, height) <= max_long_edge. Never upscale.
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Used only for the inference request payload; stored processed_blob / Woo
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media stay full size. Unreadable bytes are returned unchanged so a bad
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blob cannot crash Call 1.
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"""
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if not png_bytes or max_long_edge < 1:
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return png_bytes
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try:
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im = Image.open(io.BytesIO(png_bytes))
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im.load()
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except Exception:
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return png_bytes
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w, h = im.size
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long_edge = max(w, h)
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if long_edge <= max_long_edge:
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return png_bytes
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scale = max_long_edge / float(long_edge)
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nw = max(1, int(round(w * scale)))
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nh = max(1, int(round(h * scale)))
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if im.mode not in ("RGB", "RGBA", "L", "LA"):
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im = im.convert("RGBA")
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out = im.resize((nw, nh), Image.Resampling.LANCZOS)
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buf = io.BytesIO()
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out.save(buf, format="PNG", optimize=True)
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return buf.getvalue()
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def _image_part_from_processed(png_bytes: bytes) -> dict:
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"""One OpenAI-style image_url part from a processed PNG (METADATA-35 sized)."""
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fitted = fit_long_edge_png(png_bytes)
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b64 = base64.b64encode(fitted).decode()
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return {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}}
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class InferenceError(Exception):
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pass
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class InferenceConnectionError(InferenceError):
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pass
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class InferenceClient:
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"""Minimal OpenAI-compatible chat client. `chat` is the mock seam."""
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def __init__(self, base_url: str, api_key: str, model: str, timeout: float = 60.0):
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self.base_url = base_url.rstrip("/")
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self.api_key = api_key
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self.model = model
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self.timeout = timeout
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def chat(self, messages: list[dict]) -> str:
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url = f"{self.base_url}/chat/completions"
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body = json.dumps({"model": self.model, "messages": messages}).encode("utf-8")
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try:
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with httpx.Client(timeout=self.timeout) as client:
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resp = client.post(
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url, content=body,
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headers={"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"},
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)
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except (httpx.TimeoutException, httpx.TransportError) as e:
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raise InferenceConnectionError(f"inference unreachable: {e}") from e
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if resp.status_code >= 400:
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raise InferenceError(f"inference HTTP {resp.status_code}")
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data = json.loads(resp.read().decode("utf-8"))
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return data["choices"][0]["message"]["content"]
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def _inference_creds(conn) -> tuple[str, str, str]:
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b = conn.execute("SELECT value_enc FROM settings WHERE key='inference_base_url'").fetchone()
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k = conn.execute("SELECT value_enc FROM settings WHERE key='inference_api_key'").fetchone()
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m = conn.execute("SELECT value FROM settings WHERE key='inference_model'").fetchone()
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base = decrypt(b[0]) if b and b[0] else ""
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key = decrypt(k[0]) if k and k[0] else ""
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model = m[0] if m and m[0] else ""
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return base, key, model
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def _enabled(conn, key: str) -> bool:
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row = conn.execute("SELECT value FROM settings WHERE key=?", (key,)).fetchone()
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return bool(row and row[0] in ("true", "True", "1"))
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def _taxonomy_lists(conn) -> dict:
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"""Closed Call 1 lists only (#86 / METADATA-40).
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Keys match the seed prompt. No grade/thickness/moisture (owner fields).
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Groupers resolved by slug only (QUALITY-105 / METADATA-5): 'species' and
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'wcat'. Feature tag names are shown without the feat- prefix; ids are
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Woo/local tag ids.
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"""
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out: dict[str, list] = {
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"species": [],
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"wood_categories": [],
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"edge_terms": [],
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"figure_terms": [],
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"feature_tags": [],
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}
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sp = conn.execute("SELECT id FROM woo_categories WHERE slug='species'").fetchone()
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if sp:
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for r in conn.execute("SELECT id,name FROM woo_categories WHERE parent_id=?", (sp[0],)).fetchall():
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out["species"].append({"id": r[0], "name": r[1]})
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wc = conn.execute("SELECT id FROM woo_categories WHERE slug='wcat'").fetchone()
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if wc:
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kids = [r[0] for r in conn.execute("SELECT id FROM woo_categories WHERE parent_id=?", (wc[0],)).fetchall()]
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leaf_ids = []
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for cid in kids:
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has_kids = conn.execute("SELECT 1 FROM woo_categories WHERE parent_id=? LIMIT 1", (cid,)).fetchone()
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if has_kids:
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leaf_ids.extend(r[0] for r in conn.execute("SELECT id FROM woo_categories WHERE parent_id=?", (cid,)).fetchall())
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else:
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leaf_ids.append(cid)
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if leaf_ids:
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q = ",".join("?" * len(leaf_ids))
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for r in conn.execute(f"SELECT id,name FROM woo_categories WHERE id IN ({q})", leaf_ids).fetchall():
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out["wood_categories"].append({"id": r[0], "name": r[1]})
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for attr_name, key in (("Edge type", "edge_terms"), ("Figure", "figure_terms")):
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rows = conn.execute(
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"SELECT t.id,t.name FROM woo_attribute_terms t JOIN woo_attributes a ON t.attribute_id=a.id "
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"WHERE lower(a.name)=lower(?)",
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(attr_name,),
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).fetchall()
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out[key] = [{"id": r[0], "name": r[1]} for r in rows]
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for r in conn.execute("SELECT id,name FROM woo_tags WHERE slug LIKE 'feat-%'").fetchall():
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out["feature_tags"].append({"id": r[0], "name": _feat_display_name(r[1])})
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return out
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def _feat_display_name(name: str) -> str:
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"""Strip leading feat- / feat_ for the model (#86); Woo id stays authoritative."""
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n = (name or "").strip()
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low = n.lower()
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for prefix in ("feat-", "feat_"):
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if low.startswith(prefix):
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return n[len(prefix):].strip() or n
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return n
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def _parse_json(raw: str) -> dict:
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try:
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s = raw.strip()
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if s.startswith("```"):
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s = s.split("```")[1]
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if s.startswith("json"):
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s = s[4:]
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parsed = json.loads(s)
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return parsed if isinstance(parsed, dict) else {}
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except (ValueError, IndexError):
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return {}
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def _gate(parsed: dict) -> dict:
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"""Apply the 0.7 confidence gate; below-threshold fields are omitted (METADATA-45).
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Confidence is read from flat '<field>_confidence' keys; as a safety net it
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also understands nested 'confidence: {<field>: n}' some models emit. Grade is
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never suggested (METADATA-40); free-text observation bags are dropped
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(#86) — feature tags are ids only.
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"""
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conf_key = {
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"species_id": "species_confidence",
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"wood_category_ids": "wood_category_confidence",
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"edge_type_term_id": "edge_confidence",
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"figure_term_ids": "figure_confidence",
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"feature_tag_ids": "feature_tag_confidence",
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}
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conf_obj = parsed.get("confidence") if isinstance(parsed.get("confidence"), dict) else {}
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def _conf(field: str) -> Any:
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k = conf_key.get(field, f"{field}_confidence")
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return conf_obj[k] if k in conf_obj else parsed.get(k)
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def keep(field: str) -> Any:
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conf = _conf(field)
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if isinstance(conf, (int, float)) and conf >= _THRESHOLD and field in parsed:
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return parsed[field]
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return None
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out: dict[str, Any] = {}
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for field in conf_key:
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if v := keep(field):
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out[field] = v
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return out
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def _photo_meta_row(photo_id: str, seq: int, blob: bytes) -> dict[str, Any]:
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"""Metadata for one photo slot in the inference log (#87)."""
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edge = None
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try:
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im = Image.open(io.BytesIO(blob))
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im.load()
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edge = max(im.size)
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except Exception:
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edge = None
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return {
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"photo_id": photo_id,
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"seq": seq,
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"bytes": len(blob) if blob is not None else 0,
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"infer_long_edge_px": edge,
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"label": f"photo seq={seq} id={photo_id}",
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}
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def _slab_photos(conn, slab_id: str) -> tuple[list[dict], list[dict]]:
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rows = conn.execute(
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"SELECT id, seq, processed_blob FROM slab_photos "
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"WHERE slab_id=? AND processed_blob IS NOT NULL ORDER BY seq LIMIT 8",
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(slab_id,),
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).fetchall()
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image_parts = [_image_part_from_processed(p["processed_blob"]) for p in rows]
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photo_meta = [_photo_meta_row(p["id"], p["seq"], p["processed_blob"]) for p in rows]
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return image_parts, photo_meta
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def _redact_request_for_log(messages: list, photo_meta: list[dict] | None = None) -> list:
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"""Replace image data-URLs with photo refs for inference_log (#87 / METADATA-66).
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Wire messages keep base64 for the model; only the logged copy is redacted.
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Text parts are kept in full. Image parts become type=image_ref using
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photo_meta by image-part index.
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"""
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meta = list(photo_meta or [])
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out: list = []
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for msg in messages:
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if not isinstance(msg, dict) or not isinstance(msg.get("content"), list):
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out.append(msg)
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continue
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new_parts: list = []
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img_i = 0
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for part in msg["content"]:
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if not isinstance(part, dict):
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new_parts.append(part)
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continue
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ptype = part.get("type")
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if ptype == "text":
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new_parts.append({"type": "text", "text": part.get("text", "")})
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continue
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if ptype == "image_url":
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url = ""
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iu = part.get("image_url")
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if isinstance(iu, dict):
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url = str(iu.get("url") or "")
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if url.startswith("data:image") or "base64," in url:
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ref: dict[str, Any] = {
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"type": "image_ref", "photo_id": None, "seq": None,
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"bytes": None, "infer_long_edge_px": None,
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"label": f"photo slot={img_i}",
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}
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if img_i < len(meta):
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ref.update(meta[img_i])
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new_parts.append(ref)
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img_i += 1
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else:
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new_parts.append({"type": "image_ref", "url": url[:200], "label": f"external image slot={img_i}"})
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img_i += 1
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continue
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new_parts.append(part)
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out.append({**msg, "content": new_parts})
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return out
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def _log_call(conn, slab_id: str, call: int, rev_id: int | None, model: str,
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request: list, response: str, latency_ms: int,
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photo_meta: list[dict] | None = None) -> None:
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"""Append inference_log with redacted request (no image bytes) — #87 / METADATA-66."""
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payload = json.dumps(_redact_request_for_log(request, photo_meta), ensure_ascii=False)
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if len(payload) > _LOG_PAYLOAD_CAP:
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payload = payload[: _LOG_PAYLOAD_CAP - 20] + "\n/* truncated */"
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conn.execute(
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"INSERT INTO inference_log(slab_id, call, prompt_revision_id, model, request_payload, response_payload, latency_ms, created_at) "
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"VALUES (?,?,?,?,?,?,?,?)",
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(slab_id, call, rev_id, model, payload, response, latency_ms,
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time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())),
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)
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conn.commit()
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