feat(video-render): providers/veo.py — Veo 3.1 Vertex AI client (SP-7)
predictLongRunning → fetchPredictOperation 폴링 (12초 × 50).
결과 gs://bucket/veo/{task_id}/sample_0.mp4 → google-cloud-storage SDK로
다운로드 → NAS SMB. GOOGLE_PROJECT_ID/LOCATION/GCS_BUCKET/APPLICATION_CREDENTIALS env.
Plan-B-Video Phase 2.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
176
services/video-render/providers/veo.py
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176
services/video-render/providers/veo.py
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"""Veo 3.1 video generation — Google Vertex AI.
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POST .../models/{MODEL}:predictLongRunning → POST :fetchPredictOperation 폴링 →
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결과 gs://bucket/path/sample_0.mp4 → google-cloud-storage로 다운로드 → NAS SMB.
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"""
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from __future__ import annotations
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import logging
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import os
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import subprocess
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import time
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from typing import Optional
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import requests
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from nas_client import webhook_update_task
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logger = logging.getLogger(__name__)
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VIDEO_MEDIA_ROOT = os.getenv("VIDEO_MEDIA_ROOT", "/mnt/nas/webpage/data/video")
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VIDEO_MEDIA_URL_PREFIX = os.getenv("VIDEO_MEDIA_URL_PREFIX", "/media/video")
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POLL_INTERVAL = 12 # Veo는 30~120초 소요
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POLL_MAX_ATTEMPTS = 50 # 최대 ~10분
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DEFAULT_MODEL = "veo-3.1-fast-generate-001"
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def _gcloud_access_token() -> Optional[str]:
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"""GOOGLE_APPLICATION_CREDENTIALS service account JSON으로 access token 발행.
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google-auth가 컨테이너 안에서 자동 인증 — Bearer 토큰을 GCS SDK가 직접 사용.
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REST API 호출용으로는 명시적 token이 필요 → google.auth로 발행.
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"""
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try:
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from google.auth import default as google_default_auth
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from google.auth.transport.requests import Request as GoogleAuthRequest
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credentials, _ = google_default_auth(
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scopes=["https://www.googleapis.com/auth/cloud-platform"],
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)
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credentials.refresh(GoogleAuthRequest())
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return credentials.token
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except Exception:
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logger.exception("Google credentials refresh 실패")
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return None
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def _download_gcs(gcs_uri: str, local_path: str) -> bool:
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"""gs://bucket/path/file.mp4 → local_path 다운로드. 성공 여부 반환."""
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try:
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from google.cloud import storage as gcs_storage
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if not gcs_uri.startswith("gs://"):
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return False
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without_scheme = gcs_uri[len("gs://"):]
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bucket_name, blob_path = without_scheme.split("/", 1)
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client = gcs_storage.Client(project=os.getenv("GOOGLE_PROJECT_ID"))
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bucket = client.bucket(bucket_name)
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blob = bucket.blob(blob_path)
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blob.download_to_filename(local_path)
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return True
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except Exception:
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logger.exception("GCS 다운로드 실패: %s", gcs_uri)
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return False
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def run_veo_generation(task_id: str, params: dict) -> None:
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"""Veo 3.1로 영상 생성 → GCS → NAS SMB → webhook."""
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try:
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project_id = os.getenv("GOOGLE_PROJECT_ID", "")
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location = os.getenv("GOOGLE_LOCATION", "us-central1")
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gcs_bucket = os.getenv("GOOGLE_GCS_BUCKET", "")
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if not project_id or not gcs_bucket:
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webhook_update_task(task_id, "failed", 0, "",
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error="GOOGLE_PROJECT_ID 또는 GOOGLE_GCS_BUCKET 미설정")
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return
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token = _gcloud_access_token()
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if not token:
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webhook_update_task(task_id, "failed", 0, "",
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error="Google access token 발행 실패 (서비스 계정 JSON 확인)")
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return
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webhook_update_task(task_id, "processing", 5, "Veo API 호출 중...")
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model_id = params.get("model") or DEFAULT_MODEL
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endpoint_base = (
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f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}"
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f"/locations/{location}/publishers/google/models/{model_id}"
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)
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headers = {
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"Authorization": f"Bearer {token}",
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"Content-Type": "application/json",
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}
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body = {
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"instances": [{"prompt": params["prompt"]}],
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"parameters": {
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"storageUri": f"gs://{gcs_bucket}/veo/{task_id}/",
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"sampleCount": 1,
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"aspectRatio": params.get("aspect_ratio") or "16:9",
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},
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}
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if params.get("duration"):
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body["parameters"]["duration"] = params["duration"]
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if params.get("negative_prompt"):
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body["parameters"]["negativePrompt"] = params["negative_prompt"]
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resp = requests.post(f"{endpoint_base}:predictLongRunning",
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headers=headers, json=body, timeout=30)
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if resp.status_code != 200:
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webhook_update_task(task_id, "failed", 0, "",
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error=f"Veo API 오류: {resp.status_code} {resp.text[:300]}")
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return
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op_name = resp.json().get("name", "")
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if not op_name:
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webhook_update_task(task_id, "failed", 0, "", error="Veo 응답에 operation name 없음")
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return
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webhook_update_task(task_id, "processing", 15, f"Veo 작업 시작됨")
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# 폴링 — fetchPredictOperation
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gcs_uri = None
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for attempt in range(POLL_MAX_ATTEMPTS):
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time.sleep(POLL_INTERVAL)
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fetch = requests.post(
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f"{endpoint_base}:fetchPredictOperation",
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headers=headers,
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json={"operationName": op_name},
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timeout=30,
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)
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if fetch.status_code != 200:
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continue
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fd = fetch.json()
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done = fd.get("done", False)
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scaled = min(15 + int((attempt / POLL_MAX_ATTEMPTS) * 65), 79)
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webhook_update_task(task_id, "processing", scaled, "Veo 생성 중...")
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if done:
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if "error" in fd:
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webhook_update_task(task_id, "failed", 0, "",
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error=f"Veo 작업 실패: {fd['error'].get('message','?')}")
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return
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videos = (fd.get("response") or {}).get("videos") or []
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if not videos:
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webhook_update_task(task_id, "failed", 0, "", error="Veo 완료했으나 videos 비어 있음")
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return
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gcs_uri = videos[0].get("gcsUri", "")
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break
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else:
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webhook_update_task(task_id, "failed", 0, "", error="Veo 폴링 timeout (10분)")
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return
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if not gcs_uri:
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webhook_update_task(task_id, "failed", 0, "", error="Veo 응답에 gcsUri 없음")
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return
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webhook_update_task(task_id, "processing", 85, "GCS에서 mp4 다운로드 중...")
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filename = f"{task_id}.mp4"
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os.makedirs(VIDEO_MEDIA_ROOT, exist_ok=True)
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file_path = os.path.join(VIDEO_MEDIA_ROOT, filename)
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ok = _download_gcs(gcs_uri, file_path)
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if not ok:
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webhook_update_task(task_id, "failed", 0, "", error=f"GCS 다운로드 실패: {gcs_uri}")
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return
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local_url = f"{VIDEO_MEDIA_URL_PREFIX}/{filename}"
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webhook_update_task(task_id, "succeeded", 100, "Veo 생성 완료", video_url=local_url)
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except requests.Timeout:
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webhook_update_task(task_id, "failed", 0, "", error="Veo API 타임아웃")
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except Exception as e:
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logger.exception("Veo generation error task=%s", task_id)
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webhook_update_task(task_id, "failed", 0, "", error=str(e))
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