chore(inference): disable Call 1 end-to-end; Call 2 stays
overnight-whole-app: Call 1 (vision classification) is OUT.
- POST /api/v1/slabs/{id}/inference/call1 always returns 410 call1_disabled
(no model chat, no inference_log). Prompt seed/revision APIs kept dormant.
- Review: remove "Suggest with AI" / runInference (inferTaxon).
- GenerateText: remove Classify metadata (Call 1) button; Call 2 generate stays.
- PROMPTS.md banner documents Call 1 OUT / Call 2 STAYS.
- Settings: inference_enabled toggle annotated as Call-1 latch (dormant).
- Backend tests updated to assert 410; Call 2 tests unchanged.
This commit is contained in:
grokbot_ty
+17
-45
@@ -1,6 +1,8 @@
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"""Inference (S7): prompt revisions (REST), Call 1 vision classification, Call 2
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content prose, test-inference, inference-off server description, inference_log.
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Cites METADATA-35..115, SETTINGS-30/45/90.
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overnight-whole-app: Call 1 POST is hard-disabled (410 call1_disabled). Call 2 stays.
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"""
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import base64
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@@ -295,51 +297,21 @@ def _enabled(conn, key: str) -> bool:
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@router.post("/slabs/{slab_id}/inference/call1")
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async def call1(slab_id: str) -> dict:
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conn = get_connection()
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row = conn.execute("SELECT * FROM slabs WHERE id=? AND deleted_at IS NULL", (slab_id,)).fetchone()
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if row is None:
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return JSONResponse(status_code=404, content=_error("not_found", "Slab not found"))
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if not _enabled(conn, "inference_enabled"):
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return JSONResponse(status_code=409, content=_error("inference_disabled", "Inference is off"))
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base, key, model = _inference_creds(conn)
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if not base or not key:
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return JSONResponse(status_code=409, content=_error("inference_unconfigured", "Inference is not configured"))
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client = InferenceClient(base, key, model)
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_seed_prompts(conn)
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conn.commit()
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act = _active_prompt(conn, "call1")
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prompt_text = act[1] if act else CALL1_SEED
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# Build vision message with processed photos (METADATA-35: long edge <= 1024) + taxonomy.
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photos = 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 = []
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photo_meta: list[dict] = []
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for p in photos:
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blob = p["processed_blob"] if hasattr(p, "keys") else p[2]
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pid = p["id"] if hasattr(p, "keys") else p[0]
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seq = p["seq"] if hasattr(p, "keys") else p[1]
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image_parts.append(_image_part_from_processed(blob))
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photo_meta.append(_photo_meta_row(pid, seq, blob))
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tax = _taxonomy_lists(conn)
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text = (f"{prompt_text}\n\nLISTS:\n{json.dumps(tax, ensure_ascii=False)}\n\n"
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f"SKU={row['sku']} length_in={row['length_in']} thickness_in={row['thickness_in']}")
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messages = [{"role": "user", "content": image_parts + [{"type": "text", "text": text}]}]
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t0 = time.time()
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try:
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raw = client.chat(messages)
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except InferenceConnectionError as e:
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return JSONResponse(status_code=503, content=_error("inference_unreachable", str(e), {"recoverable": True}))
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except InferenceError as e:
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return JSONResponse(status_code=502, content=_error("inference_failed", str(e), {"recoverable": True}))
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latency = int((time.time() - t0) * 1000)
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parsed = _parse_json(raw)
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_log_call(conn, slab_id, 1, act[0] if act else None, model, messages, raw, latency, photo_meta)
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# Apply confidence gate (METADATA-45).
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suggestions = _gate(parsed)
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return {"ok": True, "suggestions": suggestions}
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"""Call 1 OUT (overnight-whole-app): hard-disabled. Call 2 stays.
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Route retained so clients get an explicit 410 instead of a half-wired path.
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Prompt seed / revision APIs for call1 remain for docs & future re-enable.
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"""
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_ = slab_id # unused while disabled
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return JSONResponse(
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status_code=410,
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content=_error(
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"call1_disabled",
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"Call 1 (vision classification) is disabled on this branch. Use Call 2 for description text.",
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{"call": 1, "disabled": True},
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),
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)
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def _feat_display_name(name: str) -> str:
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@@ -153,42 +153,28 @@ def test_gate_feature_tag_ids_not_free_text():
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assert "feature_tag_ids" not in _gate(low)
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def test_call1_confidence_gate(env, monkeypatch):
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def test_call1_hard_disabled_410(env, monkeypatch):
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"""overnight-whole-app: Call 1 OUT — POST always 410, never chats the model."""
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client, conn = env
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# enable inference
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conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('inference_enabled','true',NULL)")
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conn.execute("INSERT INTO woo_categories(id,name,slug,parent_id,woo_id) VALUES (1,'Black Walnut','black-walnut',NULL,100)")
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conn.execute("INSERT INTO slabs(id,sku,length_in,thickness_in,status,server_rev,client_rev,created_at,updated_at) VALUES ('s1','SLAB-01',100,2,'calibrated',1,1,'t','t')")
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conn.execute("INSERT INTO slab_photos(id,slab_id,seq,processed_blob,created_at) VALUES ('p1','s1',1,X'89504E47','t')")
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conn.commit()
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from backend.inference import InferenceClient
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called = {"n": 0}
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def fake_chat(self, messages):
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# Capture list header for #86 shape
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text = messages[0]["content"][-1]["text"]
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assert "LISTS:" in text
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assert "grade_terms" not in text
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assert "species_leaves" not in text
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return json.dumps({
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"species_id": 1, "species_confidence": 0.9,
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"wood_category_ids": [1], "wood_category_confidence": 0.5, # below threshold
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"grade_term_id": 7, "grade_confidence": 0.99, # never suggested
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"figure_term_ids": [2], "figure_confidence": 0.8, # at/above
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"feature_tag_ids": [9], "feature_tag_confidence": 0.9,
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"character": ["x"], "inclusions": ["y"],
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})
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called["n"] += 1
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return "{}"
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monkeypatch.setattr(InferenceClient, "chat", fake_chat)
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r = client.post("/api/v1/slabs/s1/inference/call1")
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assert r.status_code == 200, r.text
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s = r.json()["suggestions"]
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assert s.get("species_id") == 1
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assert s.get("figure_term_ids") == [2]
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assert s.get("feature_tag_ids") == [9]
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assert "wood_category_ids" not in s # gated out
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assert "grade_term_id" not in s # never
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assert "character" not in s
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# inference_log written with call=1
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assert conn.execute("SELECT call FROM inference_log").fetchone()["call"] == 1
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assert r.status_code == 410, r.text
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body = r.json()
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assert body["error"]["code"] == "call1_disabled"
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assert called["n"] == 0
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assert conn.execute("SELECT COUNT(*) AS n FROM inference_log").fetchone()["n"] == 0
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def test_call2_stores_description(env, monkeypatch):
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@@ -208,12 +194,14 @@ def test_call2_stores_description(env, monkeypatch):
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assert row["content_source"] == "llm"
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def test_inference_off_call1_409(env):
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def test_call1_disabled_even_when_inference_off(env):
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"""Call 1 hard-disable wins over inference_enabled=false (still 410, not 409)."""
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client, conn = env
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conn.execute("INSERT INTO slabs(id,sku,status,server_rev,client_rev,created_at,updated_at) VALUES ('s1','SLAB-01','calibrated',1,1,'t','t')")
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conn.commit()
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r = client.post("/api/v1/slabs/s1/inference/call1")
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assert r.status_code == 409 # inference_enabled default off
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assert r.status_code == 410
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assert r.json()["error"]["code"] == "call1_disabled"
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def test_server_description_inference_off(env):
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@@ -306,50 +294,18 @@ def test_redact_request_for_log_strips_data_urls_keeps_text():
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assert "LISTS:" in parts[2]["text"]
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def test_call1_log_has_text_and_image_refs_no_base64(env, monkeypatch):
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"""#87 METADATA-66: inference_log stores text+refs, not image bytes."""
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def test_call1_log_skipped_while_disabled(env):
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"""#87 Call 1 log path dormant while Call 1 is hard-disabled (overnight-whole-app)."""
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client, conn = env
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conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('inference_enabled','true',NULL)")
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conn.execute(
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"INSERT INTO slabs(id,sku,length_in,thickness_in,status,server_rev,client_rev,created_at,updated_at) "
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"VALUES ('s1','SLAB-01',100,2,'calibrated',1,1,'t','t')"
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)
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png = _rgba_png(400, 300)
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conn.execute(
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"INSERT INTO slab_photos(id,slab_id,seq,processed_blob,created_at) VALUES ('photo-abc','s1',1,?, 't')",
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(png,),
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)
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conn.commit()
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from backend.inference import InferenceClient
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def fake_chat(self, messages):
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# Wire path still has data-URL for the model
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content = messages[0]["content"]
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assert any(
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isinstance(p, dict) and p.get("type") == "image_url"
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and str(p.get("image_url", {}).get("url", "")).startswith("data:image")
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for p in content
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)
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return json.dumps({"species_id": 1, "species_confidence": 0.9})
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monkeypatch.setattr(InferenceClient, "chat", fake_chat)
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r = client.post("/api/v1/slabs/s1/inference/call1")
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assert r.status_code == 200, r.text
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row = conn.execute(
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"SELECT request_payload, length(request_payload) AS n FROM inference_log ORDER BY id DESC LIMIT 1"
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).fetchone()
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payload = row["request_payload"]
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assert row["n"] < 50000
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assert "data:image" not in payload
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assert "LISTS:" in payload
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assert "image_ref" in payload
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assert "photo-abc" in payload
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assert '"seq": 1' in payload or '"seq":1' in payload
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logged = json.loads(payload)
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refs = [p for p in logged[0]["content"] if p.get("type") == "image_ref"]
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assert len(refs) == 1
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assert refs[0]["photo_id"] == "photo-abc"
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assert refs[0]["bytes"] == len(png)
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assert r.status_code == 410
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assert conn.execute("SELECT COUNT(*) AS n FROM inference_log").fetchone()["n"] == 0
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def test_call2_log_redacts_images(env, monkeypatch):
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@@ -380,38 +336,9 @@ def test_call2_log_redacts_images(env, monkeypatch):
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assert "DIMENSION FACT BLOCK" in payload
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def test_call1_sends_long_edge_at_most_1024(env, monkeypatch):
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"""METADATA-35 end-to-end: messages to the model use <=1024 long edge."""
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client, conn = env
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conn.execute("INSERT OR REPLACE INTO settings(key,value,value_enc) VALUES ('inference_enabled','true',NULL)")
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conn.execute(
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"INSERT INTO slabs(id,sku,length_in,thickness_in,status,server_rev,client_rev,created_at,updated_at) "
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"VALUES ('s1','SLAB-01',100,2,'calibrated',1,1,'t','t')"
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)
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big = _rgba_png(800, 1067)
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conn.execute(
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"INSERT INTO slab_photos(id,slab_id,seq,processed_blob,created_at) VALUES ('p1','s1',1,?, 't')",
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(big,),
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)
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conn.commit()
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from backend.inference import InferenceClient
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from PIL import Image
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import io
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captured = {}
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def fake_chat(self, messages):
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captured["messages"] = messages
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return json.dumps({"species_id": 1, "species_confidence": 0.9})
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monkeypatch.setattr(InferenceClient, "chat", fake_chat)
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r = client.post("/api/v1/slabs/s1/inference/call1")
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assert r.status_code == 200, r.text
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content = captured["messages"][0]["content"]
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imgs = [p for p in content if isinstance(p, dict) and p.get("type") == "image_url"]
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assert len(imgs) == 1
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url = imgs[0]["image_url"]["url"]
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assert url.startswith("data:image/png;base64,")
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raw = base64.b64decode(url.split(",", 1)[1])
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im = Image.open(io.BytesIO(raw))
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assert max(im.size) <= 1024
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def test_call1_sends_long_edge_dormant_while_disabled(env):
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"""METADATA-35 Call 1 sizing path dormant while Call 1 is hard-disabled."""
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client, _conn = env
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r = client.post("/api/v1/slabs/missing/inference/call1")
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assert r.status_code == 410
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assert r.json()["error"]["code"] == "call1_disabled"
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