Compare commits
4 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 37edef716a | |||
| d5a94947de | |||
| 615b842b03 | |||
| 998cc2e472 |
@@ -114,6 +114,13 @@ def _run_migrations(conn: duckdb.DuckDBPyConnection) -> None:
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conn.execute("""
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ALTER TABLE models_cache ADD COLUMN IF NOT EXISTS output_modalities VARCHAR
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""")
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# Migration: add video job request params + generation type
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conn.execute("""
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ALTER TABLE generated_videos ADD COLUMN IF NOT EXISTS request_params VARCHAR
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""")
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conn.execute("""
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ALTER TABLE generated_videos ADD COLUMN IF NOT EXISTS generation_type VARCHAR DEFAULT 'text_to_video'
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""")
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_seed_admin(conn)
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+9
-1
@@ -5,7 +5,9 @@ from .routers import ai
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from .routers import generate
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from .routers import images
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from .routers import models
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from .db import close_db, init_db
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from .db import close_db, get_conn, get_write_lock, init_db
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from .services.video_worker import run_worker
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import asyncio
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import os
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from contextlib import asynccontextmanager
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@@ -19,7 +21,13 @@ load_dotenv()
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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init_db()
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worker_task = asyncio.create_task(run_worker(get_conn(), get_write_lock()))
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yield
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worker_task.cancel()
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try:
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await worker_task
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except asyncio.CancelledError:
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pass
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close_db()
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@@ -185,3 +185,43 @@ async def admin_mark_timed_out(_: dict = Depends(require_admin)) -> dict[str, in
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conn = get_conn()
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count = mark_timed_out_video_jobs(conn, timeout_minutes=120)
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return {"timed_out": count}
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@router.post("/videos/{job_id}/retry", status_code=200)
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async def admin_retry_video_job(job_id: str, _: dict = Depends(require_admin)) -> dict[str, str]:
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"""Reset a failed or cancelled video job back to 'queued' for reprocessing."""
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conn = get_conn()
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lock = get_write_lock()
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now = datetime.now(timezone.utc)
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async with lock:
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row = conn.execute(
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"SELECT status FROM generated_videos WHERE id = ?", [job_id]
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).fetchone()
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if row is None:
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from fastapi import HTTPException
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raise HTTPException(status_code=404, detail="Job not found")
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if row[0] not in ("failed", "cancelled"):
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from fastapi import HTTPException
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raise HTTPException(
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status_code=400, detail=f"Cannot retry job with status '{row[0]}'")
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conn.execute(
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"UPDATE generated_videos SET status = 'queued', updated_at = ? WHERE id = ?",
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[now, job_id],
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)
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return {"status": "ok", "job_id": job_id}
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@router.delete("/videos/{job_id}", status_code=200)
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async def admin_delete_video_job(job_id: str, _: dict = Depends(require_admin)) -> dict[str, str]:
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"""Permanently delete a video job record."""
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conn = get_conn()
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lock = get_write_lock()
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async with lock:
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row = conn.execute(
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"SELECT id FROM generated_videos WHERE id = ?", [job_id]
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).fetchone()
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if row is None:
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from fastapi import HTTPException
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raise HTTPException(status_code=404, detail="Job not found")
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conn.execute("DELETE FROM generated_videos WHERE id = ?", [job_id])
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return {"status": "ok", "job_id": job_id}
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@@ -1,4 +1,5 @@
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"""Generate router: text, image, video, and image-to-video generation."""
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import json
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from datetime import datetime, timezone
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import httpx
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@@ -209,54 +210,32 @@ async def generate_video(
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body: VideoRequest,
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current_user: dict = Depends(get_current_user),
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) -> VideoResponse:
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"""Generate a video from a text prompt."""
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try:
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result = await openrouter.generate_video(
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model=body.model,
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prompt=body.prompt,
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duration_seconds=body.duration_seconds,
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aspect_ratio=body.aspect_ratio,
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resolution=body.resolution,
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)
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except httpx.HTTPStatusError as exc:
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detail = (
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f"OpenRouter API error: {exc.response.status_code} - {exc.response.text}"
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)
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raise HTTPException(
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status_code=status.HTTP_502_BAD_GATEWAY, detail=detail)
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except Exception as exc:
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raise HTTPException(
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status_code=status.HTTP_502_BAD_GATEWAY, detail=f"OpenRouter error: {exc}"
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)
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"""Queue a text-to-video generation job for background processing."""
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user_id = current_user.get("id") or current_user.get("sub")
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job_id = result.get("id", "")
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polling_url = result.get("polling_url")
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job_status = result.get("status", "pending")
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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request_params = json.dumps({
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"model": body.model,
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"prompt": body.prompt,
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"duration_seconds": body.duration_seconds,
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"aspect_ratio": body.aspect_ratio,
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"resolution": body.resolution,
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})
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db_id = None
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async with get_write_lock():
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conn = get_conn()
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row = conn.execute(
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"""INSERT INTO generated_videos (user_id, job_id, model_id, prompt, polling_url, status, created_at, updated_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?) RETURNING id""",
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[user_id, job_id, body.model, body.prompt,
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polling_url, job_status, now, now],
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"""INSERT INTO generated_videos
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(user_id, job_id, model_id, prompt, status, request_params, generation_type, created_at, updated_at)
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VALUES (?, ?, ?, ?, 'queued', ?, 'text_to_video', ?, ?) RETURNING id""",
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[user_id, "", body.model, body.prompt, request_params, now, now],
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).fetchone()
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if row:
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db_id = str(row[0])
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urls = result.get("unsigned_urls") or result.get("video_urls")
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return VideoResponse(
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id=job_id,
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id="",
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db_id=db_id,
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model=body.model,
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status=job_status,
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polling_url=polling_url,
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video_urls=urls,
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video_url=(urls or [None])[0],
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error=result.get("error"),
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metadata=result.get("metadata"),
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status="queued",
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)
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@@ -265,55 +244,33 @@ async def generate_video_from_image(
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body: VideoFromImageRequest,
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current_user: dict = Depends(get_current_user),
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) -> VideoResponse:
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"""Generate a video from an image and a text prompt."""
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try:
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result = await openrouter.generate_video_from_image(
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model=body.model,
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image_url=body.image_url,
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prompt=body.prompt,
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duration_seconds=body.duration_seconds,
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aspect_ratio=body.aspect_ratio,
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resolution=body.resolution,
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)
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except httpx.HTTPStatusError as exc:
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detail = (
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f"OpenRouter API error: {exc.response.status_code} - {exc.response.text}"
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)
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raise HTTPException(
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status_code=status.HTTP_502_BAD_GATEWAY, detail=detail)
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except Exception as exc:
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raise HTTPException(
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status_code=status.HTTP_502_BAD_GATEWAY, detail=f"OpenRouter error: {exc}"
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)
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"""Queue an image-to-video generation job for background processing."""
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user_id = current_user.get("id") or current_user.get("sub")
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job_id = result.get("id", "")
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polling_url = result.get("polling_url")
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job_status = result.get("status", "pending")
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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request_params = json.dumps({
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"model": body.model,
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"image_url": body.image_url,
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"prompt": body.prompt,
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"duration_seconds": body.duration_seconds,
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"aspect_ratio": body.aspect_ratio,
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"resolution": body.resolution,
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})
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db_id = None
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async with get_write_lock():
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conn = get_conn()
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row = conn.execute(
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"""INSERT INTO generated_videos (user_id, job_id, model_id, prompt, polling_url, status, created_at, updated_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?) RETURNING id""",
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[user_id, job_id, body.model, body.prompt,
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polling_url, job_status, now, now],
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"""INSERT INTO generated_videos
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(user_id, job_id, model_id, prompt, status, request_params, generation_type, created_at, updated_at)
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VALUES (?, ?, ?, ?, 'queued', ?, 'image_to_video', ?, ?) RETURNING id""",
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[user_id, "", body.model, body.prompt, request_params, now, now],
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).fetchone()
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if row:
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db_id = str(row[0])
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urls = result.get("unsigned_urls") or result.get("video_urls")
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return VideoResponse(
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id=job_id,
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id="",
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db_id=db_id,
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model=body.model,
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status=job_status,
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polling_url=polling_url,
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video_urls=urls,
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video_url=(urls or [None])[0],
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error=result.get("error"),
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metadata=result.get("metadata"),
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status="queued",
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)
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@@ -0,0 +1,158 @@
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"""Background worker: processes queued/processing video generation jobs."""
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import asyncio
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import json
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import logging
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from datetime import datetime, timezone
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import duckdb
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from . import openrouter
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from .models import mark_timed_out_video_jobs
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logger = logging.getLogger(__name__)
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# Interval between worker ticks (seconds)
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WORKER_INTERVAL = 15
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# Jobs to process per tick (prevents unbounded bursts)
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BATCH_SIZE = 5
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async def process_queued_jobs(conn: duckdb.DuckDBPyConnection, lock: asyncio.Lock) -> int:
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"""Submit queued jobs to OpenRouter and transition them to 'processing'."""
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rows = conn.execute(
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"""SELECT id, generation_type, request_params
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FROM generated_videos
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WHERE status = 'queued' AND request_params IS NOT NULL
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ORDER BY created_at ASC
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LIMIT ?""",
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[BATCH_SIZE],
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).fetchall()
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processed = 0
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for row in rows:
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db_id, generation_type, raw_params = str(row[0]), row[1], row[2]
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try:
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params = json.loads(raw_params)
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except (json.JSONDecodeError, TypeError):
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logger.error("Bad request_params for video job %s", db_id)
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continue
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try:
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if generation_type == "image_to_video":
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result = await openrouter.generate_video_from_image(
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model=params["model"],
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image_url=params.get("image_url", ""),
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prompt=params.get("prompt", ""),
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duration_seconds=params.get("duration_seconds"),
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aspect_ratio=params.get("aspect_ratio", "16:9"),
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resolution=params.get("resolution"),
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)
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else:
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result = await openrouter.generate_video(
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model=params["model"],
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prompt=params.get("prompt", ""),
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duration_seconds=params.get("duration_seconds"),
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||||
aspect_ratio=params.get("aspect_ratio", "16:9"),
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resolution=params.get("resolution"),
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)
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except Exception as exc:
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logger.warning("OpenRouter call failed for job %s: %s", db_id, exc)
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now = datetime.now(timezone.utc).replace(tzinfo=None)
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async with lock:
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conn.execute(
|
||||
"UPDATE generated_videos SET status = 'failed', updated_at = ? WHERE id = ?",
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[now, db_id],
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||||
)
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||||
continue
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||||
|
||||
job_id = result.get("id", "")
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||||
polling_url = result.get("polling_url")
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||||
new_status = result.get("status", "processing")
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# Normalise terminal statuses returned immediately (rare but possible)
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if new_status not in ("queued", "processing", "completed", "failed", "cancelled"):
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new_status = "processing"
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|
||||
urls = result.get("unsigned_urls") or result.get("video_urls")
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||||
video_url = (urls or [None])[0]
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||||
now = datetime.now(timezone.utc).replace(tzinfo=None)
|
||||
|
||||
async with lock:
|
||||
conn.execute(
|
||||
"""UPDATE generated_videos
|
||||
SET job_id = ?, polling_url = ?, status = ?, video_url = ?, updated_at = ?
|
||||
WHERE id = ?""",
|
||||
[job_id, polling_url, new_status, video_url, now, db_id],
|
||||
)
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||||
processed += 1
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||||
logger.info("Video job %s → %s (provider id: %s)",
|
||||
db_id, new_status, job_id)
|
||||
|
||||
return processed
|
||||
|
||||
|
||||
async def process_processing_jobs(conn: duckdb.DuckDBPyConnection, lock: asyncio.Lock) -> int:
|
||||
"""Poll in-progress jobs and update to 'completed' or 'failed'."""
|
||||
rows = conn.execute(
|
||||
"""SELECT id, polling_url
|
||||
FROM generated_videos
|
||||
WHERE status = 'processing' AND polling_url IS NOT NULL
|
||||
ORDER BY updated_at ASC
|
||||
LIMIT ?""",
|
||||
[BATCH_SIZE],
|
||||
).fetchall()
|
||||
|
||||
updated = 0
|
||||
for row in rows:
|
||||
db_id, polling_url = str(row[0]), row[1]
|
||||
try:
|
||||
result = await openrouter.poll_video_status(polling_url)
|
||||
except Exception as exc:
|
||||
logger.warning("Polling failed for job %s: %s", db_id, exc)
|
||||
continue
|
||||
|
||||
job_status = result.get("status", "processing")
|
||||
if job_status not in ("completed", "failed"):
|
||||
continue # still in-progress — check again next tick
|
||||
|
||||
urls = result.get("unsigned_urls") or result.get("video_urls")
|
||||
video_url = (urls or [None])[0]
|
||||
now = datetime.now(timezone.utc).replace(tzinfo=None)
|
||||
|
||||
async with lock:
|
||||
conn.execute(
|
||||
"""UPDATE generated_videos
|
||||
SET status = ?, video_url = ?, updated_at = ?
|
||||
WHERE id = ?""",
|
||||
[job_status, video_url, now, db_id],
|
||||
)
|
||||
updated += 1
|
||||
logger.info("Video job %s → %s", db_id, job_status)
|
||||
|
||||
return updated
|
||||
|
||||
|
||||
async def worker_tick(conn: duckdb.DuckDBPyConnection, lock: asyncio.Lock) -> None:
|
||||
"""Single worker tick: submit queued, poll processing, expire timed-out."""
|
||||
queued = await process_queued_jobs(conn, lock)
|
||||
polled = await process_processing_jobs(conn, lock)
|
||||
async with lock:
|
||||
timed_out = mark_timed_out_video_jobs(conn, timeout_minutes=120)
|
||||
if queued or polled or timed_out:
|
||||
logger.info(
|
||||
"Worker tick: submitted=%d polled=%d timed_out=%d",
|
||||
queued, polled, timed_out,
|
||||
)
|
||||
|
||||
|
||||
async def run_worker(conn: duckdb.DuckDBPyConnection, lock: asyncio.Lock) -> None:
|
||||
"""Infinite loop: run a worker tick every WORKER_INTERVAL seconds."""
|
||||
logger.info("Video worker started (interval=%ds)", WORKER_INTERVAL)
|
||||
while True:
|
||||
try:
|
||||
await worker_tick(conn, lock)
|
||||
except asyncio.CancelledError:
|
||||
logger.info("Video worker stopped.")
|
||||
return
|
||||
except Exception as exc:
|
||||
logger.exception("Unexpected error in video worker: %s", exc)
|
||||
await asyncio.sleep(WORKER_INTERVAL)
|
||||
@@ -4,7 +4,8 @@ Describes the relevant requirements and the driving forces that software archite
|
||||
|
||||
## Requirements Overview
|
||||
|
||||
**Project name**: All You Can GET AI Biz
|
||||
**Project name**: All You Can GET AI
|
||||
**URL**: [https://ai.allucanget.biz](https://ai.allucanget.biz)
|
||||
**Purpose**: Provide AI‑powered text, image, and video generation services via a web application.
|
||||
|
||||
Users can choose between different AI models for:
|
||||
@@ -14,6 +15,8 @@ Users can choose between different AI models for:
|
||||
- Text‑to‑video generation
|
||||
- Image‑to‑video generation
|
||||
|
||||
Users can create accounts, log in, and view their generation history in a gallery. An admin dashboard allows managing users, models, and video generation jobs.
|
||||
|
||||
## Quality Goals
|
||||
|
||||
| Priority | Quality Goal | Scenario |
|
||||
|
||||
@@ -5,21 +5,21 @@ Static decomposition of the system into building blocks (modules, components, su
|
||||
## Level 1 – Whitebox Overall System
|
||||
|
||||
```text
|
||||
┌───────────────────────┐
|
||||
│ Frontend (Flask) │
|
||||
└───────┬───────────────┘
|
||||
┌────────────────────────┐
|
||||
│ Frontend (Flask) │
|
||||
└───────┬────────────────┘
|
||||
│ REST API calls
|
||||
┌───────▼───────────────┐
|
||||
│ FastAPI Backend │
|
||||
│ ├─ Auth Service │
|
||||
│ ├─ User Service │
|
||||
│ ├─ AI Service │
|
||||
┌───────▼────────────────┐
|
||||
│ FastAPI Backend │
|
||||
│ ├─ Auth Service │
|
||||
│ ├─ User Service │
|
||||
│ ├─ AI Service │
|
||||
│ └─ DB Service (DuckDB)│
|
||||
└───────┬───────────────┘
|
||||
└───────┬────────────────┘
|
||||
│ DB access
|
||||
┌───────▼───────────────┐
|
||||
│ DuckDB Database │
|
||||
└───────────────────────┘
|
||||
┌───────▼────────────────┐
|
||||
│ DuckDB Database │
|
||||
└────────────────────────┘
|
||||
```
|
||||
|
||||
**Motivation:** Separating the UI (Flask) from the API (FastAPI) allows independent scaling and testing of each layer.
|
||||
|
||||
+58
-50
@@ -5,71 +5,79 @@ Describes:
|
||||
1. Technical infrastructure used to execute your system, with infrastructure elements like geographical locations, environments, computers, processors, channels and net topologies.
|
||||
2. Mapping of (software) building blocks to that infrastructure elements.
|
||||
|
||||
**See**: [Coolify Deployment Guide](./deployment/coolify.md) for detailed instructions.
|
||||
|
||||
## Infrastructure Level 1
|
||||
|
||||
```text
|
||||
┌────────────────────────────────────────────┐
|
||||
│ Host / VM │
|
||||
│ ┌─────────────┐ ┌────────────────────┐ │
|
||||
│ │ frontend │ │ backend │ │
|
||||
│ │ (Flask) │ │ (FastAPI) │ │
|
||||
│ │ :12016 │ │ :12015 │ │
|
||||
│ └──────┬──────┘ └─────────┬──────────┘ │
|
||||
│ │ │ │
|
||||
│ └────────┬──────────┘ │
|
||||
│ │ │
|
||||
│ ┌───────▼────────┐ │
|
||||
│ │ db (DuckDB) │ │
|
||||
│ │ data/app.db │ │
|
||||
│ └────────────────┘ │
|
||||
└────────────────────────────────────────────┘
|
||||
Hosted on a single VM running docker containers, deployed via Coolify with Nixpacks to 192.168.88.18 for production.
|
||||
|
||||
Containers run behind nginx at 192.168.88.11 which handles TLS termination and reverse proxying to the frontend on port 12016 and backend on port 12015. The database is a file on the host filesystem at `data/app.db` accessed by the backend service.
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
Users[Users / Internet]
|
||||
Nginx[nginx reverse proxy\nTLS termination]
|
||||
Users -->|HTTPS| Nginx
|
||||
|
||||
subgraph Coolify Server
|
||||
direction TB
|
||||
subgraph AI Frontend
|
||||
AI_Frontend[AI Frontend\nFlask\nServes HTML/CSS/JS UI]
|
||||
end
|
||||
subgraph AI Backend
|
||||
AI_Backend[AI Backend\nFastAPI\nCommunicates with openrouter.ai API]
|
||||
db[(DuckDB Database\nFile: data/app.db)]
|
||||
AI_Backend --> db
|
||||
end
|
||||
AI_Frontend -->|BACKEND_URL:12015| AI_Backend
|
||||
end
|
||||
Nginx -->|12016| AI_Frontend
|
||||
```
|
||||
|
||||
**Motivation:** All three components run on a single VM (or as Docker containers) for simplicity and low operational overhead.
|
||||
**Motivation:** All three components run as Docker containers for simplicity and low operational overhead.
|
||||
|
||||
**Quality and/or Performance Features:** The frontend and backend are stateless; DuckDB persists data on the host filesystem.
|
||||
|
||||
**Mapping of Building Blocks to Infrastructure:**
|
||||
|
||||
| Building Block | Container / Process | Port |
|
||||
| --------------- | ---------------------------- | ----- |
|
||||
| Flask frontend | `frontend` | 12016 |
|
||||
| FastAPI backend | `backend` | 12015 |
|
||||
| DuckDB | File on host (`data/app.db`) | — |
|
||||
| Building Block | Container / Process | Port |
|
||||
| --------------- | ---------------------------- | --------------- |
|
||||
| Nginx | `nginx` | 80/443 (public) |
|
||||
| Coolify Server | `coolify` | — |
|
||||
| Flask frontend | `frontend` | 12016 |
|
||||
| FastAPI backend | `backend` | 12015 |
|
||||
| DuckDB | File on host (`data/app.db`) | — |
|
||||
|
||||
## Infrastructure Level 2
|
||||
|
||||
### Coolify with Nixpacks (Production)
|
||||
|
||||
Both services are deployed as separate Nixpacks resources in Coolify:
|
||||
Both services are deployed as separate Nixpacks resources in Coolify, which results in two separate containers running on the same host. The database is a file on the host filesystem, mounted as a volume in the backend container.
|
||||
|
||||
```text
|
||||
┌──────────────────────────────────────────────────────────┐
|
||||
│ Coolify Server │
|
||||
│ ┌────────────────────────────┐ │
|
||||
│ │ Backend Service (FastAPI) │ │
|
||||
│ │ - Base Dir: /backend │ │
|
||||
│ │ - Port: 12015 │ │
|
||||
│ │ - Volume: /app/data │ │
|
||||
│ ├────────────────────────────┤ │
|
||||
│ │ Frontend Service (Flask) │ │
|
||||
│ │ - Base Dir: /frontend │ │
|
||||
│ │ - Port: 12016 (public) │ │
|
||||
│ │ - BACKEND_URL: :12015 │ │
|
||||
│ └────────────────────────────┘ │
|
||||
│ ▲ │
|
||||
│ Coolify reverse proxy (TLS termination) │
|
||||
└──────────────────────────────────────────────────────────┘
|
||||
│
|
||||
Users / Internet
|
||||
#### Frontend
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
subgraph Coolify Server
|
||||
direction TB
|
||||
subgraph AI Frontend
|
||||
AI_Frontend[AI Frontend\nNixpacks\nBase Dir: /frontend]
|
||||
end
|
||||
end
|
||||
Users[Users / Internet] -->|HTTPS| AI_Frontend
|
||||
```
|
||||
|
||||
**Deployment Steps:**
|
||||
#### Backend
|
||||
|
||||
1. Create backend Nixpacks service in Coolify with Base Directory `/backend`
|
||||
2. Create frontend Nixpacks service with Base Directory `/frontend`
|
||||
3. Set environment variables per service
|
||||
4. Attach domain to frontend on port `12016`
|
||||
5. Enable Auto HTTPS in Coolify
|
||||
|
||||
**See**: [Coolify Deployment Guide](./deployment/coolify.md) for detailed instructions.
|
||||
```mermaid
|
||||
graph TD
|
||||
subgraph Coolify Server
|
||||
direction TB
|
||||
subgraph AI Backend
|
||||
AI_Backend[AI Backend\nNixpacks\nBase Dir: /backend]
|
||||
db[(DuckDB Database\nVolume: /app/data)]
|
||||
AI_Backend --> db
|
||||
end
|
||||
end
|
||||
Frontend[Frontend Container] -->|BACKEND_URL:12015| AI_Backend
|
||||
```
|
||||
|
||||
@@ -4,6 +4,14 @@ Describes crosscutting concepts (practices, patterns, regulations or solution id
|
||||
|
||||
> Pick **only** the most-needed topics for your system.
|
||||
|
||||
## OpenRouter API Integration
|
||||
|
||||
see [docs/8.1-openrouter.md](./8.1-openrouter.md) for details on how the backend integrates with OpenRouter for multi-modal AI generation, including image and video generation flows.
|
||||
|
||||
## DuckDB Concurrency and Storage
|
||||
|
||||
See [docs/8.2-duckdb.md](./8.2-duckdb.md) for details on how the backend handles concurrent access to DuckDB and manages the database file on the host filesystem.
|
||||
|
||||
## Security
|
||||
|
||||
- All API endpoints (except `/auth/login`) require a valid JWT in the `Authorization: Bearer` header.
|
||||
@@ -25,72 +33,3 @@ Describes crosscutting concepts (practices, patterns, regulations or solution id
|
||||
|
||||
- All secrets (API keys, DB path, JWT secret) loaded from environment variables or `.env` file.
|
||||
- No secrets committed to source control.
|
||||
|
||||
## DuckDB Concurrency and Storage
|
||||
|
||||
### Single Writer Per Process
|
||||
|
||||
DuckDB allows only one process to open the database file in read-write mode at a time. The FastAPI backend must be run with a single worker (`uvicorn --workers 1`). Running multiple workers against the same DuckDB file will cause startup errors.
|
||||
|
||||
### asyncio.Lock for Writes
|
||||
|
||||
All database write operations (`INSERT`, `UPDATE`, `DELETE`) in the FastAPI async context are wrapped in a single `asyncio.Lock` (`get_write_lock()` from `backend/app/db.py`). This prevents concurrent coroutines from issuing overlapping writes within the single process, which would otherwise raise DuckDB optimistic concurrency errors.
|
||||
|
||||
Read operations (`SELECT`) do not require the lock — DuckDB's MVCC provides consistent read snapshots.
|
||||
|
||||
### Schema
|
||||
|
||||
```sql
|
||||
CREATE TABLE users (
|
||||
id UUID DEFAULT uuid() PRIMARY KEY,
|
||||
email VARCHAR NOT NULL UNIQUE,
|
||||
password_hash VARCHAR NOT NULL,
|
||||
role VARCHAR DEFAULT 'user',
|
||||
created_at TIMESTAMP DEFAULT now(),
|
||||
updated_at TIMESTAMP DEFAULT now()
|
||||
);
|
||||
|
||||
CREATE TABLE refresh_tokens (
|
||||
jti UUID DEFAULT uuid() PRIMARY KEY,
|
||||
user_id UUID NOT NULL, -- soft FK to users.id
|
||||
issued_at TIMESTAMP DEFAULT now(),
|
||||
expires_at TIMESTAMP NOT NULL,
|
||||
revoked BOOLEAN DEFAULT false
|
||||
);
|
||||
```
|
||||
|
||||
> The `REFERENCES users(id)` foreign key is intentionally omitted from `refresh_tokens`. DuckDB fires FK checks on `UPDATE` of the parent table (including email changes), causing false constraint violations. Referential integrity is enforced manually: deleting a user also deletes their refresh tokens in the same write transaction.
|
||||
|
||||
### Access Tokens
|
||||
|
||||
Access tokens are **stateless** JWTs — not stored in the database. They are validated by signature and expiry claim only. The short TTL (15 minutes) limits the blast radius if a token is leaked.
|
||||
|
||||
### Refresh Tokens
|
||||
|
||||
Refresh tokens store a JTI (JWT ID) UUID in the `refresh_tokens` table. On each use the old JTI is revoked and a new one issued (rotation). On logout the JTI is immediately revoked. Expired and revoked tokens can be purged via `POST /admin/tokens/purge`.
|
||||
|
||||
### Future: AI Generation History
|
||||
|
||||
AI generation metadata (model, prompt, cost, result URLs) can be stored as JSON columns in a future `generation_history` table in DuckDB, enabling per-user analytics and usage dashboards at zero extra infrastructure cost.
|
||||
|
||||
## OpenRouter API Integration
|
||||
|
||||
### Image Generation
|
||||
|
||||
Image generation uses two different OpenRouter endpoints depending on the model:
|
||||
|
||||
- **Legacy endpoint** (`/images/generations`): Used by DALL-E 3 and similar models. Returns `data[].url` and `data[].b64_json`.
|
||||
- **Chat completions** (`/chat/completions` with `modalities: ["image"]`): Used by FLUX.2 Klein 4B and GPT-5 Image Mini. Returns `choices[0].message.images[].image_url.url` as base64 data URLs.
|
||||
|
||||
The router auto-detects the model type and routes accordingly. Image configuration (`aspect_ratio`, `image_size`) is passed via `image_config` for chat-based models.
|
||||
|
||||
### Video Generation
|
||||
|
||||
Video generation uses OpenRouter's `/api/v1/videos` endpoint with a **submit-and-poll** pattern:
|
||||
|
||||
1. `POST /api/v1/videos` with `model`, `prompt`, `aspect_ratio`, `resolution`, `duration_seconds`
|
||||
2. Response: `{"id": "job_id", "polling_url": "https://..."}` with `status: "queued"`
|
||||
3. Poll `GET polling_url` every 5 seconds until `status` is `"completed"` or `"failed"`
|
||||
4. Completed response includes `unsigned_urls: [str]` array with video download URLs
|
||||
|
||||
Supported models: `openai/sora-2-pro`, `google/veo-3.1-fast`. Both text-to-video and image-to-video use the same `/api/v1/videos` endpoint (image-to-video includes `image_url` in the request body).
|
||||
|
||||
@@ -0,0 +1,26 @@
|
||||
# OpenRouter API Integration
|
||||
|
||||
## Text Generation
|
||||
|
||||
> [!warning]
|
||||
> TODO: Add more details on how the backend integrates with OpenRouter for text generation, including chat completions and single-prompt generation flows.
|
||||
|
||||
## Image Generation
|
||||
|
||||
Image generation uses two different OpenRouter endpoints depending on the model:
|
||||
|
||||
- **Legacy endpoint** (`/images/generations`): Used by DALL-E 3 and similar models. Returns `data[].url` and `data[].b64_json`.
|
||||
- **Chat completions** (`/chat/completions` with `modalities: ["image"]`): Used by FLUX.2 Klein 4B and GPT-5 Image Mini. Returns `choices[0].message.images[].image_url.url` as base64 data URLs.
|
||||
|
||||
The router auto-detects the model type and routes accordingly. Image configuration (`aspect_ratio`, `image_size`) is passed via `image_config` for chat-based models.
|
||||
|
||||
## Video Generation
|
||||
|
||||
Video generation uses OpenRouter's `/api/v1/videos` endpoint with a **submit-and-poll** pattern:
|
||||
|
||||
1. `POST /api/v1/videos` with `model`, `prompt`, `aspect_ratio`, `resolution`, `duration_seconds`
|
||||
2. Response: `{"id": "job_id", "polling_url": "https://..."}` with `status: "queued"`
|
||||
3. Poll `GET polling_url` every 5 seconds until `status` is `"completed"` or `"failed"`
|
||||
4. Completed response includes `unsigned_urls: [str]` array with video download URLs
|
||||
|
||||
Supported models: `openai/sora-2-pro`, `google/veo-3.1-fast`. Both text-to-video and image-to-video use the same `/api/v1/videos` endpoint (image-to-video includes `image_url` in the request body).
|
||||
@@ -0,0 +1,46 @@
|
||||
# DuckDB Concurrency and Storage
|
||||
|
||||
## Single Writer Per Process
|
||||
|
||||
DuckDB allows only one process to open the database file in read-write mode at a time. The FastAPI backend must be run with a single worker (`uvicorn --workers 1`). Running multiple workers against the same DuckDB file will cause startup errors.
|
||||
|
||||
## asyncio.Lock for Writes
|
||||
|
||||
All database write operations (`INSERT`, `UPDATE`, `DELETE`) in the FastAPI async context are wrapped in a single `asyncio.Lock` (`get_write_lock()` from `backend/app/db.py`). This prevents concurrent coroutines from issuing overlapping writes within the single process, which would otherwise raise DuckDB optimistic concurrency errors.
|
||||
|
||||
Read operations (`SELECT`) do not require the lock — DuckDB's MVCC provides consistent read snapshots.
|
||||
|
||||
## Schema
|
||||
|
||||
```sql
|
||||
CREATE TABLE users (
|
||||
id UUID DEFAULT uuid() PRIMARY KEY,
|
||||
email VARCHAR NOT NULL UNIQUE,
|
||||
password_hash VARCHAR NOT NULL,
|
||||
role VARCHAR DEFAULT 'user',
|
||||
created_at TIMESTAMP DEFAULT now(),
|
||||
updated_at TIMESTAMP DEFAULT now()
|
||||
);
|
||||
|
||||
CREATE TABLE refresh_tokens (
|
||||
jti UUID DEFAULT uuid() PRIMARY KEY,
|
||||
user_id UUID NOT NULL, -- soft FK to users.id
|
||||
issued_at TIMESTAMP DEFAULT now(),
|
||||
expires_at TIMESTAMP NOT NULL,
|
||||
revoked BOOLEAN DEFAULT false
|
||||
);
|
||||
```
|
||||
|
||||
> The `REFERENCES users(id)` foreign key is intentionally omitted from `refresh_tokens`. DuckDB fires FK checks on `UPDATE` of the parent table (including email changes), causing false constraint violations. Referential integrity is enforced manually: deleting a user also deletes their refresh tokens in the same write transaction.
|
||||
|
||||
## Access Tokens
|
||||
|
||||
Access tokens are **stateless** JWTs — not stored in the database. They are validated by signature and expiry claim only. The short TTL (15 minutes) limits the blast radius if a token is leaked.
|
||||
|
||||
## Refresh Tokens
|
||||
|
||||
Refresh tokens store a JTI (JWT ID) UUID in the `refresh_tokens` table. On each use the old JTI is revoked and a new one issued (rotation). On logout the JTI is immediately revoked. Expired and revoked tokens can be purged via `POST /admin/tokens/purge`.
|
||||
|
||||
## Future: AI Generation History
|
||||
|
||||
AI generation metadata (model, prompt, cost, result URLs) can be stored as JSON columns in a future `generation_history` table in DuckDB, enabling per-user analytics and usage dashboards at zero extra infrastructure cost.
|
||||
@@ -173,188 +173,3 @@ All required environment variables:
|
||||
- [ ] Domain names configured
|
||||
- [ ] Health checks passing
|
||||
- [ ] Logs reviewed for errors
|
||||
|
||||
1. In Coolify, click **Add Resource** → **Deploy a new resource** → **Git**
|
||||
2. Connect your Git repository (`git.allucanget.biz`)
|
||||
3. Select the `ai.allucanget.biz` repository
|
||||
4. Choose the `main` branch
|
||||
5. Set **Build Pack** to `nixpacks`
|
||||
6. **CRITICAL: Set Base Directory to `/backend`** — this tells Nixpacks to look in the `backend/` subdirectory for `requirements.txt` and the Python application
|
||||
7. Set **Ports Exposed** to `12015`
|
||||
8. Set **Start Command** to:
|
||||
|
||||
```txt
|
||||
uvicorn app.main:app --host 0.0.0.0 --port 12015
|
||||
```
|
||||
|
||||
9. Click **Create Resource**
|
||||
|
||||
> **Important:** Nixpacks copies the **contents** of the Base Directory to `/app/` in the container. When Base Directory is `/backend`, the `backend/` folder wrapper is removed — only `app/`, `tests/`, and `requirements.txt` are copied. Therefore the start command uses `app.main:app` (not `backend.app.main:app`).
|
||||
|
||||
### Backend Environment Variables
|
||||
|
||||
Add these as **Runtime** environment variables in Coolify:
|
||||
|
||||
| Variable | Description | Example |
|
||||
| -------------------- | ------------------------------------ | ------------------------------------ |
|
||||
| `OPENROUTER_API_KEY` | OpenRouter API key for AI generation | `sk-or-v1-...` |
|
||||
| `JWT_SECRET` | Secret key for JWT token signing | Generate with `openssl rand -hex 32` |
|
||||
| `APP_URL` | Public URL of the backend | `https://api.ai.allucanget.biz` |
|
||||
| `APP_NAME` | Application name | `All You Can GET AI` |
|
||||
| `CORS_ORIGINS` | Comma-separated allowed origins | `https://ai.allucanget.biz` |
|
||||
|
||||
## Step 2: Create Frontend Service
|
||||
|
||||
1. In Coolify, click **Add Resource** → **Deploy a new resource** → **Git**
|
||||
2. Select the same repository
|
||||
3. Choose the `main` branch
|
||||
4. Set **Build Pack** to `nixpacks`
|
||||
5. **CRITICAL: Set Base Directory to `/frontend`** — this tells Nixpacks to look in the `frontend/` subdirectory for `requirements.txt` and the Python application
|
||||
6. Set **Ports Exposed** to `12016`
|
||||
7. Set **Start Command** to:
|
||||
|
||||
```txt
|
||||
gunicorn app.main:app --bind 0.0.0.0:12016 --workers 2 --timeout 120
|
||||
```
|
||||
|
||||
8. Click **Create Resource**
|
||||
|
||||
> **Note:** The frontend uses `requirements.txt` for production dependencies and `requirements-dev.txt` for development dependencies (like pytest). Nixpacks will automatically detect and install only the production dependencies.
|
||||
> **Important:** Nixpacks copies the **contents** of the Base Directory to `/app/` in the container. When Base Directory is `/frontend`, the `frontend/` folder wrapper is removed — only `app/`, `tests/`, and `requirements.txt` are copied. Therefore the start command uses `app.main:app` (not `frontend.app.main:app`).
|
||||
|
||||
### Frontend Environment Variables
|
||||
|
||||
Add these as **Runtime** environment variables in Coolify:
|
||||
|
||||
| Variable | Description | Example |
|
||||
| ------------------ | ----------------------------------------- | --------------------------------------------------------------- |
|
||||
| `FLASK_SECRET_KEY` | Flask session cookie signing key | Generate with `openssl rand -hex 32` |
|
||||
| `BACKEND_URL` | Internal URL to reach the backend service | `http://localhost:12015` (or use Coolify's internal networking) |
|
||||
|
||||
## Step 3: Configure Reverse Proxy
|
||||
|
||||
Coolify provides a built-in reverse proxy. Configure routing rules:
|
||||
|
||||
### Backend Proxy Rules
|
||||
|
||||
- **Domain**: `api.ai.allucanget.biz` (or subdomain of your choice)
|
||||
- **Port**: `12015`
|
||||
- **Path**: `/api/*` → forward to backend
|
||||
|
||||
### Frontend Proxy Rules
|
||||
|
||||
- **Domain**: `ai.allucanget.biz`
|
||||
- **Port**: `12016`
|
||||
- **Path**: `/` → forward to frontend
|
||||
|
||||
### Nginx Configuration (Optional)
|
||||
|
||||
If you need custom Nginx configuration, create `nginx/coolify.conf`:
|
||||
|
||||
```nginx
|
||||
# Reverse proxy configuration for Coolify
|
||||
# This file is for reference — Coolify's built-in proxy handles routing
|
||||
|
||||
# Backend API proxy
|
||||
location /api/ {
|
||||
proxy_pass http://backend:12015;
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
}
|
||||
|
||||
# Frontend proxy
|
||||
location / {
|
||||
proxy_pass http://frontend:12016;
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
}
|
||||
```
|
||||
|
||||
## Step 4: SSL/TLS
|
||||
|
||||
Enable HTTPS in Coolify for both services:
|
||||
|
||||
1. Go to each service's settings
|
||||
2. Enable **Auto HTTPS** (Let's Encrypt)
|
||||
3. Configure domain names
|
||||
4. Coolify automatically handles certificate renewal
|
||||
|
||||
## Step 5: Persistent Storage (Optional)
|
||||
|
||||
If you want to persist DuckDB data:
|
||||
|
||||
1. In Coolify, go to the **Backend** service
|
||||
2. Navigate to **Persistent Storage**
|
||||
3. Add a volume mount:
|
||||
- **Host Path**: `/data` (or any path on the host)
|
||||
- **Container Path**: `/app/data`
|
||||
- **Type**: `Bind Mount` or `Volume`
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Docker Compose deployment fails in Coolify
|
||||
|
||||
- Verify Coolify uses `docker-compose.coolify.yml`, not local `docker-compose.yml`
|
||||
- Verify public domain points to `frontend` service on port `12016`
|
||||
- Do not add `nginx` to the Coolify stack — bind-mounting a local config file will fail since the file doesn't exist on the Coolify server
|
||||
|
||||
### Backend healthcheck stays unhealthy
|
||||
|
||||
- Check backend logs in Coolify
|
||||
- Verify `OPENROUTER_API_KEY` and `JWT_SECRET` are set
|
||||
- Verify volume mount at `/app/data` is writable
|
||||
|
||||
### Backend won't start
|
||||
|
||||
- Check that `OPENROUTER_API_KEY` is set
|
||||
- Verify `JWT_SECRET` is a sufficiently long random string
|
||||
- Check logs in Coolify's **Logs** tab
|
||||
|
||||
### Frontend can't reach backend
|
||||
|
||||
- Ensure `BACKEND_URL` points to the correct internal URL
|
||||
- If both services are on the same Coolify server, use `http://localhost:12015`
|
||||
- Check that the backend service is running and healthy
|
||||
|
||||
### CORS errors
|
||||
|
||||
- Set `CORS_ORIGINS` to include your frontend domain
|
||||
- Example: `https://ai.allucanget.biz`
|
||||
|
||||
### Nixpacks build fails
|
||||
|
||||
- Verify the base directory is correct (`/backend` or `/frontend`)
|
||||
- Check that `requirements.txt` exists in the base directory
|
||||
- Review build logs in Coolify
|
||||
|
||||
## Environment Variable Summary
|
||||
|
||||
All required environment variables:
|
||||
|
||||
| Variable | Service | Required |
|
||||
| -------------------- | -------- | ------------------------------------- |
|
||||
| `OPENROUTER_API_KEY` | Backend | Yes |
|
||||
| `JWT_SECRET` | Backend | Yes |
|
||||
| `APP_URL` | Backend | Yes |
|
||||
| `APP_NAME` | Backend | No (defaults to "All You Can GET AI") |
|
||||
| `CORS_ORIGINS` | Backend | Yes |
|
||||
| `FLASK_SECRET_KEY` | Frontend | Yes |
|
||||
| `BACKEND_URL` | Frontend | Yes |
|
||||
|
||||
## Deployment Checklist
|
||||
|
||||
- [ ] Repository pushed to Git
|
||||
- [ ] For Docker Compose: Coolify resource uses `docker-compose.coolify.yml`
|
||||
- [ ] For Docker Compose: domain points to `frontend` service on port `12016`
|
||||
- [ ] Backend service created with correct base directory (`/backend`)
|
||||
- [ ] Backend environment variables configured
|
||||
- [ ] Frontend service created with correct base directory (`/frontend`)
|
||||
- [ ] Frontend environment variables configured
|
||||
- [ ] SSL certificates enabled
|
||||
- [ ] Domain names configured
|
||||
- [ ] Health checks passing
|
||||
- [ ] Logs reviewed for errors
|
||||
|
||||
@@ -469,6 +469,15 @@ def generate_video_status():
|
||||
return jsonify(resp.json()), resp.status_code
|
||||
|
||||
|
||||
@app.get("/generate/video/<video_id>/status")
|
||||
@login_required
|
||||
def generate_video_db_status(video_id: str):
|
||||
"""Return current DB status for a video job (polled by frontend JS)."""
|
||||
resp = _api(
|
||||
"GET", f"/generate/videos/{video_id}", token=session["access_token"])
|
||||
return jsonify(resp.json()), resp.status_code
|
||||
|
||||
|
||||
# ── Admin ─────────────────────────────────────────────────────────────────
|
||||
|
||||
@app.get("/admin")
|
||||
|
||||
+18
-16
@@ -63,15 +63,14 @@ document.addEventListener("DOMContentLoaded", () => {
|
||||
// ── Video status polling ───────────────────────────────
|
||||
const pollDiv = document.getElementById("video-poll-status");
|
||||
if (pollDiv) {
|
||||
const pollingUrl = pollDiv.dataset.pollingUrl;
|
||||
const videoId = pollDiv.dataset.videoId;
|
||||
const statusText = document.getElementById("poll-status-text");
|
||||
const videoContainer = document.getElementById("poll-video-container");
|
||||
|
||||
const interval = setInterval(async () => {
|
||||
try {
|
||||
const resp = await fetch(
|
||||
"/generate/video/status?polling_url=" +
|
||||
encodeURIComponent(pollingUrl),
|
||||
"/generate/video/" + encodeURIComponent(videoId) + "/status",
|
||||
);
|
||||
if (!resp.ok) return;
|
||||
const data = await resp.json();
|
||||
@@ -82,25 +81,28 @@ document.addEventListener("DOMContentLoaded", () => {
|
||||
|
||||
if (data.status === "completed") {
|
||||
clearInterval(interval);
|
||||
if (data.video_url && videoContainer) {
|
||||
const vid = document.createElement("video");
|
||||
vid.src = data.video_url;
|
||||
vid.controls = true;
|
||||
vid.className = "generated-video";
|
||||
videoContainer.appendChild(vid);
|
||||
const msg = pollDiv.querySelector("p");
|
||||
if (msg) msg.textContent = "Video ready!";
|
||||
if (data.video_url) {
|
||||
if (videoContainer) {
|
||||
const vid = document.createElement("video");
|
||||
vid.src = data.video_url;
|
||||
vid.controls = true;
|
||||
vid.className = "generated-video";
|
||||
videoContainer.appendChild(vid);
|
||||
const msg = pollDiv.querySelector("p");
|
||||
if (msg) msg.textContent = "Video ready!";
|
||||
} else {
|
||||
// video_detail page: reload to show the video element
|
||||
window.location.reload();
|
||||
}
|
||||
}
|
||||
} else if (data.status === "failed") {
|
||||
} else if (data.status === "failed" || data.status === "cancelled") {
|
||||
clearInterval(interval);
|
||||
pollDiv.innerHTML =
|
||||
'<div class="alert alert-error">Generation failed: ' +
|
||||
(data.error || "Unknown error") +
|
||||
"</div>";
|
||||
'<div class="alert alert-error">Generation failed or was cancelled.</div>';
|
||||
}
|
||||
} catch (e) {
|
||||
console.error("Video polling error:", e);
|
||||
}
|
||||
}, 12016);
|
||||
}, 5000);
|
||||
}
|
||||
});
|
||||
|
||||
@@ -76,5 +76,208 @@
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
<!-- ── Video Jobs ──────────────────────────────────────────────── -->
|
||||
<h2 class="section-title" style="margin-top: 2rem">Video Jobs</h2>
|
||||
|
||||
<div
|
||||
style="
|
||||
display: flex;
|
||||
gap: 1rem;
|
||||
align-items: center;
|
||||
flex-wrap: wrap;
|
||||
margin-bottom: 1rem;
|
||||
"
|
||||
>
|
||||
<label for="vj-status-filter" style="font-weight: 600"
|
||||
>Filter by status:</label
|
||||
>
|
||||
<select id="vj-status-filter" class="form-control" style="width: auto">
|
||||
<option value="">All</option>
|
||||
<option value="queued">Queued</option>
|
||||
<option value="processing">Processing</option>
|
||||
<option value="completed">Completed</option>
|
||||
<option value="failed">Failed</option>
|
||||
<option value="cancelled">Cancelled</option>
|
||||
</select>
|
||||
<label for="vj-sort" style="font-weight: 600">Sort:</label>
|
||||
<select id="vj-sort" class="form-control" style="width: auto">
|
||||
<option value="created_desc">Created (newest first)</option>
|
||||
<option value="created_asc">Created (oldest first)</option>
|
||||
<option value="updated_desc">Updated (newest first)</option>
|
||||
<option value="status_asc">Status (A–Z)</option>
|
||||
<option value="model_asc">Model (A–Z)</option>
|
||||
</select>
|
||||
<button id="vj-refresh" class="btn btn-sm">Refresh</button>
|
||||
<span
|
||||
id="vj-count"
|
||||
style="color: var(--text-muted, #888); font-size: 0.9em"
|
||||
></span>
|
||||
</div>
|
||||
|
||||
<div class="table-wrap">
|
||||
<table id="vj-table">
|
||||
<thead>
|
||||
<tr>
|
||||
<th>User</th>
|
||||
<th>Status</th>
|
||||
<th>Model</th>
|
||||
<th>Prompt</th>
|
||||
<th>Created</th>
|
||||
<th>Updated</th>
|
||||
<th>Actions</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody id="vj-tbody">
|
||||
<tr>
|
||||
<td colspan="7" class="text-muted">Loading…</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
(function () {
|
||||
const BACKEND = "{{ config['BACKEND_URL'] }}";
|
||||
const TOKEN = "{{ session['access_token'] }}";
|
||||
const headers = { Authorization: "Bearer " + TOKEN };
|
||||
|
||||
let allJobs = [];
|
||||
|
||||
async function loadJobs() {
|
||||
document.getElementById("vj-tbody").innerHTML =
|
||||
'<tr><td colspan="7" class="text-muted">Loading…</td></tr>';
|
||||
try {
|
||||
const r = await fetch(BACKEND + "/admin/videos", { headers });
|
||||
if (!r.ok) throw new Error(await r.text());
|
||||
allJobs = await r.json();
|
||||
renderJobs();
|
||||
} catch (e) {
|
||||
document.getElementById("vj-tbody").innerHTML =
|
||||
`<tr><td colspan="7" style="color:red;">Error: ${e.message}</td></tr>`;
|
||||
}
|
||||
}
|
||||
|
||||
function renderJobs() {
|
||||
const statusFilter = document.getElementById("vj-status-filter").value;
|
||||
const sort = document.getElementById("vj-sort").value;
|
||||
|
||||
let jobs = statusFilter
|
||||
? allJobs.filter((j) => j.status === statusFilter)
|
||||
: [...allJobs];
|
||||
|
||||
jobs.sort((a, b) => {
|
||||
if (sort === "created_asc")
|
||||
return new Date(a.created_at) - new Date(b.created_at);
|
||||
if (sort === "updated_desc")
|
||||
return new Date(b.updated_at) - new Date(a.updated_at);
|
||||
if (sort === "status_asc") return a.status.localeCompare(b.status);
|
||||
if (sort === "model_asc") return a.model_id.localeCompare(b.model_id);
|
||||
return new Date(b.created_at) - new Date(a.created_at); // created_desc default
|
||||
});
|
||||
|
||||
document.getElementById("vj-count").textContent =
|
||||
`${jobs.length} job${jobs.length !== 1 ? "s" : ""}`;
|
||||
|
||||
const tbody = document.getElementById("vj-tbody");
|
||||
if (jobs.length === 0) {
|
||||
tbody.innerHTML =
|
||||
'<tr><td colspan="7" class="text-muted">No jobs found.</td></tr>';
|
||||
return;
|
||||
}
|
||||
|
||||
const statusColor = {
|
||||
completed: "color:var(--success-color,#4caf50)",
|
||||
failed: "color:var(--danger-color,#e53935)",
|
||||
cancelled: "color:var(--danger-color,#e53935)",
|
||||
processing: "color:var(--warning-color,#fb8c00)",
|
||||
queued: "color:var(--warning-color,#fb8c00)",
|
||||
};
|
||||
|
||||
tbody.innerHTML = jobs
|
||||
.map((job) => {
|
||||
const sc = statusColor[job.status] || "";
|
||||
const canRetry =
|
||||
job.status === "failed" || job.status === "cancelled";
|
||||
const canCancel =
|
||||
job.status === "queued" || job.status === "processing";
|
||||
const actions = [
|
||||
canRetry
|
||||
? `<button class="btn btn-sm vj-retry" data-id="${job.id}">Retry</button>`
|
||||
: "",
|
||||
canCancel
|
||||
? `<button class="btn btn-sm vj-cancel" data-id="${job.id}">Cancel</button>`
|
||||
: "",
|
||||
`<button class="btn btn-sm btn-danger vj-delete" data-id="${job.id}">Delete</button>`,
|
||||
].join(" ");
|
||||
const prompt =
|
||||
job.prompt.length > 60 ? job.prompt.slice(0, 57) + "…" : job.prompt;
|
||||
const created = job.created_at
|
||||
? new Date(job.created_at).toLocaleString()
|
||||
: "—";
|
||||
const updated = job.updated_at
|
||||
? new Date(job.updated_at).toLocaleString()
|
||||
: "—";
|
||||
return `<tr>
|
||||
<td>${job.user_email || "—"}</td>
|
||||
<td style="${sc};font-weight:600;">${job.status}</td>
|
||||
<td style="font-size:.85em;">${job.model_id}</td>
|
||||
<td title="${job.prompt.replace(/"/g, """)}">${prompt}</td>
|
||||
<td style="white-space:nowrap;">${created}</td>
|
||||
<td style="white-space:nowrap;">${updated}</td>
|
||||
<td style="white-space:nowrap;">${actions}</td>
|
||||
</tr>`;
|
||||
})
|
||||
.join("");
|
||||
}
|
||||
|
||||
async function apiPost(path) {
|
||||
const r = await fetch(BACKEND + path, { method: "POST", headers });
|
||||
if (!r.ok) {
|
||||
const d = await r.json().catch(() => ({}));
|
||||
throw new Error(d.detail || r.statusText);
|
||||
}
|
||||
return r.json();
|
||||
}
|
||||
|
||||
async function apiDelete(path) {
|
||||
const r = await fetch(BACKEND + path, { method: "DELETE", headers });
|
||||
if (!r.ok) {
|
||||
const d = await r.json().catch(() => ({}));
|
||||
throw new Error(d.detail || r.statusText);
|
||||
}
|
||||
return r.json();
|
||||
}
|
||||
|
||||
document
|
||||
.getElementById("vj-tbody")
|
||||
.addEventListener("click", async function (e) {
|
||||
const btn = e.target.closest("button");
|
||||
if (!btn) return;
|
||||
const id = btn.dataset.id;
|
||||
try {
|
||||
if (btn.classList.contains("vj-retry"))
|
||||
await apiPost(`/admin/videos/${id}/retry`);
|
||||
if (btn.classList.contains("vj-cancel"))
|
||||
await apiPost(`/admin/videos/${id}/cancel`);
|
||||
if (btn.classList.contains("vj-delete")) {
|
||||
if (!confirm("Permanently delete this video job?")) return;
|
||||
await apiDelete(`/admin/videos/${id}`);
|
||||
}
|
||||
await loadJobs();
|
||||
} catch (err) {
|
||||
alert("Error: " + err.message);
|
||||
}
|
||||
});
|
||||
|
||||
document
|
||||
.getElementById("vj-status-filter")
|
||||
.addEventListener("change", renderJobs);
|
||||
document.getElementById("vj-sort").addEventListener("change", renderJobs);
|
||||
document.getElementById("vj-refresh").addEventListener("click", loadJobs);
|
||||
|
||||
loadJobs();
|
||||
})();
|
||||
</script>
|
||||
{% endblock %}
|
||||
|
||||
@@ -192,8 +192,7 @@ content %}
|
||||
<div id="loading-indicator" class="flex justify-center py-8 hidden">
|
||||
<div class="spinner"></div>
|
||||
</div>
|
||||
|
||||
{% block scripts %}
|
||||
{% endblock %} {% block scripts %}
|
||||
<script>
|
||||
document.addEventListener("DOMContentLoaded", function () {
|
||||
const galleryContainers = document.querySelectorAll(".grid[data-grid]");
|
||||
@@ -219,7 +218,8 @@ content %}
|
||||
if (scrollPosition >= bottomThreshold) {
|
||||
isLoading = true;
|
||||
loadingIndicator.classList.remove("hidden");
|
||||
|
||||
// TODO: Implement actual fetching of next page of results and appending to the correct grid(s)
|
||||
// For demo purposes, we'll just simulate a delay and then hide the loading indicator
|
||||
// Simulate API call for next page
|
||||
// In real implementation, replace with actual backend fetch
|
||||
setTimeout(() => {
|
||||
|
||||
@@ -155,9 +155,9 @@ AI{% endblock %} {% block content %}
|
||||
{% endif %} {% if result %}
|
||||
<div class="result">
|
||||
<h2>Video job</h2>
|
||||
<p>Job ID: <code>{{ result.id }}</code></p>
|
||||
{% if result.status in ('queued', 'processing') and result.polling_url %}
|
||||
<div id="video-poll-status" data-polling-url="{{ result.polling_url }}">
|
||||
<p>Job ID: <code>{{ result.db_id or result.id }}</code></p>
|
||||
{% if result.status in ('queued', 'processing') and result.db_id %}
|
||||
<div id="video-poll-status" data-video-id="{{ result.db_id }}">
|
||||
<p>
|
||||
<span id="poll-status-text"
|
||||
>Status: <strong>{{ result.status }}</strong></span
|
||||
|
||||
@@ -12,11 +12,11 @@ block content %}
|
||||
<div class="bg-gray-800 rounded-lg shadow-lg overflow-hidden">
|
||||
{% if video.status == 'completed' and video.video_url %}
|
||||
<video src="{{ video.video_url }}" controls class="w-full"></video>
|
||||
{% elif video.status in ('queued', 'processing') and video.polling_url %}
|
||||
{% elif video.status in ('queued', 'processing') %}
|
||||
<div
|
||||
class="w-full bg-black aspect-video flex flex-col items-center justify-center p-6 text-center"
|
||||
id="video-poll-status"
|
||||
data-polling-url="{{ video.polling_url }}"
|
||||
data-video-id="{{ video.id }}"
|
||||
>
|
||||
<p class="text-xl font-semibold">
|
||||
Status: <strong id="poll-status-text">{{ video.status }}</strong>
|
||||
|
||||
Reference in New Issue
Block a user