bruno
9bb440707c
Backend: - Add ai_provider + ai_model columns to APIClient model - New admin_ai router: GET /admin/api/ai/status + POST /admin/api/ai/test - Update ai_vision.py: provider_override + model_override support - Update pipeline: load client AI prefs for image processing - ClientUpdate/ClientResponse schemas include ai_provider/ai_model Frontend: - New AIConfigSection component: provider list, model dropdowns, test button with live results (no page refresh needed) - AI types (ai.types.ts) + API (api/ai.ts) - APIClient types extended with ai_provider/ai_model
265 lines
11 KiB
Python
265 lines
11 KiB
Python
"""
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Pipeline de traitement AI — orchestration des 3 étapes
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Chaque étape est indépendante : un échec partiel n'arrête pas le pipeline.
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Publie des événements Redis (si disponible) pour le suivi en temps réel.
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"""
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import json
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import logging
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import time
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from datetime import datetime, timezone
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from typing import Any
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from app.models.image import Image, ProcessingStatus
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from app.services.exif_service import extract_exif
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from app.services.ocr_service import extract_text
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from app.services.ai_vision import analyze_image, extract_text_with_ai
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from app.metrics import (
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hub_pipeline_duration, hub_pipeline_step_duration, hub_pipeline_errors,
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hub_ai_tokens_consumed
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)
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import asyncio
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logger = logging.getLogger(__name__)
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async def _publish_event(
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redis: Any, image_id: int, event: str, data: dict | None = None
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) -> None:
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"""
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Publie un événement sur le channel Redis pipeline:{image_id}.
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Stocke aussi les 10 derniers événements dans un buffer Redis
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(clé pipeline:buffer:{image_id}, TTL 60s) pour la reconnexion WebSocket.
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Publie sur pipeline:admin pour le monitoring admin.
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"""
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if redis is None:
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return
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try:
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payload = {"event": event, "image_id": image_id, "timestamp": time.time()}
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if data:
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payload["data"] = data
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raw = json.dumps(payload)
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# Publier sur le channel dédié à l'image
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await redis.publish(f"pipeline:{image_id}", raw)
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# Buffer de reconnexion (10 derniers événements, TTL 60s)
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buffer_key = f"pipeline:buffer:{image_id}"
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await redis.rpush(buffer_key, raw)
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await redis.ltrim(buffer_key, -10, -1)
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await redis.expire(buffer_key, 60)
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# Publier sur le channel admin pour le monitoring global
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if event in ("pipeline.started", "pipeline.done", "pipeline.error"):
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await redis.publish("pipeline:admin", raw)
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except Exception:
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pass # Pub/Sub non critique — ne doit pas bloquer le pipeline
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async def process_image_pipeline(
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image_id: int, db: AsyncSession, redis: Any = None
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) -> None:
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"""
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Pipeline complet de traitement d'une image :
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1. Extraction EXIF (async)
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2. OCR — extraction texte (async)
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3. Vision AI — description + tags (async)
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4. Sauvegarde finale en BDD
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Le statut est mis à jour à chaque étape pour permettre le polling.
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Publie des événements Redis sur le channel pipeline:{image_id}.
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"""
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# ── Chargement de l'image ─────────────────────────────────
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result = await db.execute(select(Image).where(Image.id == image_id))
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image = result.scalar_one_or_none()
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if not image:
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logger.warning("pipeline.image_not_found", extra={"image_id": image_id})
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return
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# ── Démarrage ─────────────────────────────────────────────
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image.processing_status = ProcessingStatus.PROCESSING
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image.processing_started_at = datetime.now(timezone.utc)
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t_start = time.time()
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await db.commit()
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await db.refresh(image)
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await _publish_event(redis, image_id, "pipeline.started")
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errors: list[str] = []
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file_path = image.file_path
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# ════════════════════════════════════════════════════════════
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# ÉTAPE 1 — Extraction EXIF
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# ════════════════════════════════════════════════════════════
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try:
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logger.info("pipeline.step.start", extra={"image_id": image_id, "step": "exif", "step_num": "1/3"})
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t0 = time.time()
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# Maintenant async et utilise le backend
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exif = await extract_exif(file_path)
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image.exif_raw = exif.get("raw")
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image.exif_make = exif.get("make")
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image.exif_model = exif.get("model")
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image.exif_lens = exif.get("lens")
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image.exif_taken_at = exif.get("taken_at")
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image.exif_gps_lat = exif.get("gps_lat")
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image.exif_gps_lon = exif.get("gps_lon")
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image.exif_altitude = exif.get("altitude")
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image.exif_iso = exif.get("iso")
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image.exif_aperture = exif.get("aperture")
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image.exif_shutter = exif.get("shutter")
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image.exif_focal = exif.get("focal")
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image.exif_flash = exif.get("flash")
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image.exif_orientation = exif.get("orientation")
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image.exif_software = exif.get("software")
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await db.commit()
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elapsed = int((time.time() - t0) * 1000)
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hub_pipeline_step_duration.labels(step="exif").observe(time.time() - t0)
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logger.info("pipeline.step.done", extra={"image_id": image_id, "step": "exif", "duration_ms": elapsed, "camera": image.exif_make})
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await _publish_event(redis, image_id, "step.completed", {
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"step": "exif", "duration_ms": elapsed, "camera": image.exif_make,
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})
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except Exception as e:
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msg = f"EXIF : {str(e)}"
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errors.append(msg)
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hub_pipeline_errors.labels(step="exif").inc()
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logger.error("pipeline.step.error", extra={"image_id": image_id, "step": "exif", "error": str(e)})
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# ════════════════════════════════════════════════════════════
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# ÉTAPE 2 — OCR
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# ════════════════════════════════════════════════════════════
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try:
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logger.info("pipeline.step.start", extra={"image_id": image_id, "step": "ocr", "step_num": "2/3"})
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t0 = time.time()
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# Maintenant async et utilise le backend
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ocr = await extract_text(file_path)
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# Fallback AI si OCR classique échoue ou ne trouve rien
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if not ocr.get("has_text", False):
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logger.info("pipeline.ocr.fallback", extra={"image_id": image_id, "reason": "tesseract_empty"})
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ai_ocr = await extract_text_with_ai(file_path)
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if ai_ocr.get("has_text"):
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ocr = ai_ocr
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logger.info("pipeline.ocr.fallback_success", extra={"image_id": image_id, "chars": len(ocr.get("text", ""))})
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else:
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logger.info("pipeline.ocr.fallback_empty", extra={"image_id": image_id})
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image.ocr_text = ocr.get("text")
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image.ocr_language = ocr.get("language")
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image.ocr_confidence = ocr.get("confidence")
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image.ocr_has_text = ocr.get("has_text", False)
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await db.commit()
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elapsed = int((time.time() - t0) * 1000)
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hub_pipeline_step_duration.labels(step="ocr").observe(time.time() - t0)
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logger.info("pipeline.step.done", extra={"image_id": image_id, "step": "ocr", "duration_ms": elapsed, "has_text": image.ocr_has_text})
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await _publish_event(redis, image_id, "step.completed", {
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"step": "ocr", "duration_ms": elapsed, "has_text": image.ocr_has_text,
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})
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except Exception as e:
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msg = f"OCR : {str(e)}"
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errors.append(msg)
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hub_pipeline_errors.labels(step="ocr").inc()
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logger.error("pipeline.step.error", extra={"image_id": image_id, "step": "ocr", "error": str(e)})
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# ════════════════════════════════════════════════════════════
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# ÉTAPE 3 — Vision AI (description + tags)
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# ════════════════════════════════════════════════════════════
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try:
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logger.info("pipeline.step.start", extra={"image_id": image_id, "step": "ai", "step_num": "3/3"})
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t0 = time.time()
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# Charger les préférences AI du client
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provider_override = None
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model_override = None
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if image.client_id:
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from app.models.client import APIClient
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client_result = await db.execute(
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select(APIClient).where(APIClient.id == image.client_id)
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)
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owner = client_result.scalar_one_or_none()
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if owner:
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provider_override = owner.ai_provider
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model_override = owner.ai_model
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ai = await analyze_image(
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file_path=file_path,
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ocr_hint=image.ocr_text,
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provider_override=provider_override,
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model_override=model_override,
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)
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image.ai_description = ai.get("description")
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image.ai_tags = ai.get("tags", [])
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image.ai_confidence = ai.get("confidence")
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image.ai_model_used = ai.get("model")
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image.ai_processed_at = datetime.now(timezone.utc)
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image.ai_prompt_tokens = ai.get("prompt_tokens")
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image.ai_output_tokens = ai.get("output_tokens")
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await db.commit()
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elapsed = int((time.time() - t0) * 1000)
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hub_pipeline_step_duration.labels(step="ai").observe(time.time() - t0)
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# Token metrics
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if image.ai_prompt_tokens or image.ai_output_tokens:
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total_tokens = (image.ai_prompt_tokens or 0) + (image.ai_output_tokens or 0)
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hub_ai_tokens_consumed.labels(
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client_id=image.client_id,
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model=image.ai_model_used or "unknown"
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).inc(total_tokens)
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logger.info("pipeline.step.done", extra={"image_id": image_id, "step": "ai", "duration_ms": elapsed, "tags_count": len(image.ai_tags or [])})
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await _publish_event(redis, image_id, "step.completed", {
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"step": "ai", "duration_ms": elapsed, "tags_count": len(image.ai_tags or []),
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})
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except Exception as e:
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msg = f"AI Vision : {str(e)}"
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errors.append(msg)
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hub_pipeline_errors.labels(step="ai").inc()
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logger.error("pipeline.step.error", extra={"image_id": image_id, "step": "ai", "error": str(e)})
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# ════════════════════════════════════════════════════════════
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# FINALISATION
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# ════════════════════════════════════════════════════════════
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image.processing_done_at = datetime.now(timezone.utc)
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if errors:
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if image.ai_description:
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image.processing_status = ProcessingStatus.DONE
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image.processing_error = f"Avertissements : {'; '.join(errors)}"
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else:
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image.processing_status = ProcessingStatus.ERROR
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image.processing_error = "; ".join(errors)
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else:
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image.processing_status = ProcessingStatus.DONE
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image.processing_error = None
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await db.commit()
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hub_pipeline_duration.observe(time.time() - t_start)
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logger.info("pipeline.completed", extra={
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"image_id": image_id,
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"status": image.processing_status.value,
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"errors": len(errors),
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})
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if errors:
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await _publish_event(redis, image_id, "pipeline.error", {"errors": errors})
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else:
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await _publish_event(redis, image_id, "pipeline.done")
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