S6-1: /api/exam analyse endpoint — structure → ghost-region suggestions (async)

The canvas "Auto-detect" path (spec R3.1/R3.3/R3.5/R5.4). Async because extraction on an image-only paper takes ~15-60s (Card-12 grounding): POST /api/exam/templates/{id}/analyse -> {job_id} (BackgroundTasks) GET /api/exam/jobs/{id} -> {status, counts, suggestions} suggestions = questions + response_areas + boundaries, each source:'ai',confirmed:false with confidence + page-fraction bounds + deterministic uuids — rendered as ghosts the teacher confirms/dismisses; never overwrites manual shapes; persistence stays PUT /templates/{id}. Access is as-the-user (E1/E2): the template is read under RLS (unseen -> 404, IDOR-safe) and a job is only visible to the user who started it. The heavy pipeline stays OUT of the API behind modules/services/exam_extract.py — an HTTP client (aiohttp) to the exam-structure extraction service (the docling-exam-spike pipeline, GPU-attached, deterministic), mirroring how the app calls Docling Serve/Ollama. Configured via EXAM_EXTRACT_URL; unset -> the job fails cleanly as 'extraction unavailable'. Follow-on (S6-1b): stand up that extraction service (POST /api/extract: source -> suggestions) wrapping scripts/structure.py + scripts/analyse.py. Co-Authored-By: Claude Opus 4.8 <[email protected]>
2026-07-02 19:45:26 +00:00
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+"""Exam structure-EXTRACTION client — the seam between the /api/exam analyse endpoint and the
+deterministic exam-structure pipeline (Card 12 = GO; verdict in docling-exam-spike).
+
+Why a service, not an in-process port: the pipeline is heavy (Docling + OCR + a question/mark/geometry
+parser) and already exists, GPU-attached, in the spike. Following the same pattern the app uses for
+Docling Serve and Ollama (§2 infra map), the API calls it over HTTP rather than vendoring thousands of
+lines. This module is the ONLY place the API knows about that service.
+
+Contract (the extraction service returns the app's ghost-region 'analyse' suggestions directly — the
+mapping lives in the pipeline, `scripts/analyse.py` in the spike; see results/ANALYSE-BRIDGE.md):
+
+    POST  {EXAM_EXTRACT_URL}/api/extract   {"source": {...}, "exam_code": "AQA-PHYS-8463-1H-22-JUN"}
+      ->  {"status": "complete",
+           "coordinate_space": "page_fraction",
+           "suggestions": {"questions": [...], "response_areas": [...], "boundaries": [...]}}
+
+Every suggestion is `source:'ai', confirmed:false` with a `confidence` float and page-fraction bounds
+(the canvas scales by the PDF render size, R3.2); uuids are deterministic so re-analysing is stable
+(§2 join keys). The API never trusts these as final — the teacher confirms/dismisses (R3.1/R3.3).
+"""
+from __future__ import annotations
+
+import os
+from typing import Any, Dict, Optional
+
+import aiohttp
+
+from modules.logger_tool import initialise_logger
+
+logger = initialise_logger(__name__, os.getenv("LOG_LEVEL"), os.getenv("LOG_PATH"), "default", True)
+
+# The exam-structure extraction service (the docling-exam-spike pipeline deployed as a service).
+EXAM_EXTRACT_URL = os.getenv("EXAM_EXTRACT_URL", "").rstrip("/")
+EXAM_EXTRACT_TIMEOUT = float(os.getenv("EXAM_EXTRACT_TIMEOUT", "180"))
+
+
+class ExtractionUnavailable(RuntimeError):
+    """The extraction service is not configured/reachable — surfaced to the job as a clean failure."""
+
+
+async def analyse_source(source: Dict[str, Any], exam_code: Optional[str] = None) -> Dict[str, Any]:
+    """Run the extraction pipeline on a template's source paper → ghost-region suggestions.
+
+    `source` identifies the paper for the service to fetch/render: a storage-resolvable pointer
+    (e.g. {"storage_url": "..."} or {"file_id": "..."}) and/or a catalogue {"exam_code": "..."}.
+    Resolution + PDF rendering happen service-side; the API only forwards the pointer + exam_code.
+    """
+    if not EXAM_EXTRACT_URL:
+        raise ExtractionUnavailable(
+            "EXAM_EXTRACT_URL is not set — the exam-structure extraction service is not wired. "
+            "Deploy the docling-exam-spike pipeline as the extraction service and set EXAM_EXTRACT_URL."
+        )
+    payload = {"source": source, "exam_code": exam_code}
+    try:
+        timeout = aiohttp.ClientTimeout(total=EXAM_EXTRACT_TIMEOUT)
+        async with aiohttp.ClientSession(timeout=timeout) as session:
+            async with session.post(f"{EXAM_EXTRACT_URL}/api/extract", json=payload) as resp:
+                resp.raise_for_status()
+                data = await resp.json()
+    except aiohttp.ClientError as exc:
+        logger.error(f"exam extraction service call failed: {exc}")
+        raise ExtractionUnavailable(f"extraction service error: {exc}") from exc
+    sugg = (data or {}).get("suggestions") or {}
+    # normalise: the three suggestion arrays are always present so the canvas can render partial results
+    return {
+        "coordinate_space": data.get("coordinate_space", "page_fraction"),
+        "questions": sugg.get("questions", []),
+        "response_areas": sugg.get("response_areas", []),
+        "boundaries": sugg.get("boundaries", []),
+        "meta": data.get("meta"),
+    }