Add character consistency, SDXL-friendly dimensions, real vision checks, and Piper narration
This commit is contained in:
Justin Oros
@@ -1,5 +1,7 @@
|
||||
data/
|
||||
videos/
|
||||
models/
|
||||
tmp/
|
||||
client_secret.json
|
||||
__pycache__/
|
||||
*.pyc
|
||||
|
||||
@@ -1,15 +1,20 @@
|
||||
# Shorts Autopilot
|
||||
|
||||
WebUI that finds trending YouTube videos, writes original 60 second scripts with a local Ollama model, generates clips with Google Veo, stitches them with ffmpeg, and uploads them to YouTube as Shorts with generated tags.
|
||||
Self-hosted WebUI that turns trending YouTube topics into original 60 second Shorts and uploads them to your channel with generated tags.
|
||||
|
||||
Two video engines:
|
||||
|
||||
- **local** (default, free): scene illustrations from Stable Diffusion on your own GPU, narration from local text to speech, slow pan and stitch with ffmpeg. No API costs and no quotas.
|
||||
- **veo** (optional, paid): true AI video from Google's Veo through the Gemini API. Needs billing enabled.
|
||||
|
||||
Scripts and tags always come from a local Ollama model, so text generation is free either way.
|
||||
|
||||
## Requirements
|
||||
|
||||
- Python 3.10+
|
||||
- ffmpeg on PATH
|
||||
- Ollama with a text model and a vision model:
|
||||
- `ollama pull llama3.1:8b`
|
||||
- `ollama pull llama3.2-vision`
|
||||
- Gemini API key with billing enabled (Veo)
|
||||
- Python 3.10+, ffmpeg, Ollama (installed by `install.sh`)
|
||||
- Ollama models: `llama3.1:8b` for text, `llama3.2-vision` for kids frame checks
|
||||
- For the local engine: about 15 GB of disk for torch and the image model
|
||||
- For the veo engine: a Gemini API key with billing enabled
|
||||
- Google Cloud project with YouTube Data API v3 enabled
|
||||
|
||||
## Google OAuth setup
|
||||
@@ -17,37 +22,66 @@ WebUI that finds trending YouTube videos, writes original 60 second scripts with
|
||||
1. Google Cloud Console: enable YouTube Data API v3.
|
||||
2. Create an OAuth client of type Web application.
|
||||
3. Add redirect URI `http://localhost:8000/auth/callback`.
|
||||
4. Download it as `client_secret.json` next to `app.py`.
|
||||
5. Add your Google account as a test user on the OAuth consent screen.
|
||||
4. Download the JSON at creation time, rename it `client_secret.json`, and put it next to `app.py`.
|
||||
5. Add your Google account as a test user, or publish the app so logins do not expire weekly.
|
||||
|
||||
## Run
|
||||
## Install and run
|
||||
|
||||
```
|
||||
pip install -r requirements.txt
|
||||
python app.py
|
||||
chmod +x install.sh run.sh install-local.sh
|
||||
./install.sh
|
||||
./install-local.sh
|
||||
./run.sh
|
||||
```
|
||||
|
||||
Open http://localhost:8000
|
||||
|
||||
1. Settings: set the storage folder, Gemini key, and profile options, then Save.
|
||||
1. Settings: set the folders, pick the video engine, adjust the profile, then Save.
|
||||
2. Dashboard: Link YouTube channel to this profile.
|
||||
3. Click GO.
|
||||
|
||||
## Storage layout
|
||||
## Folders
|
||||
|
||||
All large writes are configurable in Settings, so nothing has to land on a small internal disk:
|
||||
|
||||
- **Storage folder**: job folders and finished videos
|
||||
- **Models folder**: AI model downloads (several GB)
|
||||
- **Temp folder**: scratch space during rendering
|
||||
|
||||
Layout under the storage folder:
|
||||
|
||||
```
|
||||
<storage folder>\
|
||||
output\<profile>\<job>\ in progress or failed jobs
|
||||
published\<profile>\<job>\ uploaded to YouTube
|
||||
output/<profile>/<job>/ in progress or failed jobs
|
||||
published/<profile>/<job>/ uploaded to YouTube
|
||||
```
|
||||
|
||||
App settings, profiles, OAuth tokens, and logs live in `data\`. The log file is `data\logs\app.log`.
|
||||
App settings, profiles, OAuth tokens, and logs live in `data/`. The log file is `data/logs/app.log`.
|
||||
|
||||
To keep the virtual environment off the internal disk:
|
||||
|
||||
```
|
||||
mv .venv /Volumes/YourDisk/shorts-autopilot/venv
|
||||
ln -s /Volumes/YourDisk/shorts-autopilot/venv .venv
|
||||
```
|
||||
|
||||
## Profiles
|
||||
|
||||
Each profile has its own audience settings and its own linked YouTube channel. Generation settings and folders are shared. Use one profile per channel and switch with the dropdown.
|
||||
|
||||
## Made for kids
|
||||
|
||||
When enabled on a profile, uploads are flagged as made for kids and scripts go through word filters, an LLM compliance review, and per-clip vision checks. By default kids uploads are held as private for manual review. Also set your channel audience to made for kids in YouTube Studio.
|
||||
When enabled on a profile:
|
||||
|
||||
- Uploads are flagged as made for kids and as AI generated
|
||||
- Scripts pass hard-coded word and call-to-action filters
|
||||
- An LLM reviews each script in advisory, block, or off mode
|
||||
- Every generated scene is checked by a local vision model
|
||||
- Uploads are held as private for manual review by default
|
||||
|
||||
Also set the channel audience to made for kids in YouTube Studio.
|
||||
|
||||
## Notes
|
||||
|
||||
- Uploads cost about 1,600 of the default 10,000 daily API quota units.
|
||||
- Uploads from unverified Google API projects are locked to private until the project passes Google's audit.
|
||||
- If Ollama stops responding, restart it: `brew services restart ollama` on macOS, `sudo systemctl restart ollama` on Linux.
|
||||
|
||||
@@ -5,6 +5,8 @@ import os
|
||||
import re
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
import threading
|
||||
from collections import deque
|
||||
from datetime import date, datetime, timedelta, timezone
|
||||
@@ -14,9 +16,7 @@ from pathlib import Path
|
||||
import requests
|
||||
from fastapi import FastAPI, Request
|
||||
from fastapi.responses import FileResponse, JSONResponse, RedirectResponse
|
||||
from google import genai
|
||||
from google.auth.transport.requests import Request as GoogleRequest
|
||||
from google.genai import types
|
||||
from google.oauth2.credentials import Credentials
|
||||
from google_auth_oauthlib.flow import Flow
|
||||
from googleapiclient.discovery import build
|
||||
@@ -45,16 +45,33 @@ MASK = "********"
|
||||
MIN_FREE_GB = 2
|
||||
LATIN_LANGS = {"en", "es", "fr", "de", "it", "pt", "nl", "sv", "no", "da", "fi", "pl", "cs", "ro", "hu", "tr", "id", "ms", "vi", "tl"}
|
||||
REVIEW_MODES = ("advisory", "block", "off")
|
||||
ENGINES = ("local", "veo")
|
||||
TTS_ENGINES = ("auto", "say", "espeak", "piper")
|
||||
SUGGESTED_TEXT_MODELS = ["llama3.1:8b", "llama3.2:3b", "qwen2.5:7b", "mistral:7b"]
|
||||
SUGGESTED_VISION_MODELS = ["moondream", "llava:7b", "llama3.2-vision", "qwen2.5vl:7b"]
|
||||
SMALL_VISION_MODELS = ("moondream", "llava-phi3", "bakllava")
|
||||
|
||||
GLOBAL_DEFAULTS = {
|
||||
"active_profile": "default",
|
||||
"storage_dir": "videos",
|
||||
"models_dir": "",
|
||||
"temp_dir": "",
|
||||
"delete_after_publish": False,
|
||||
"keep_clips": False,
|
||||
"video_engine": "local",
|
||||
"ollama_url": "http://localhost:11434",
|
||||
"ollama_model": "llama3.1:8b",
|
||||
"vision_model": "llama3.2-vision",
|
||||
"vision_model": "moondream",
|
||||
"ollama_timeout": 300,
|
||||
"sd_model": "stabilityai/sdxl-turbo",
|
||||
"sd_steps": 4,
|
||||
"sd_guidance": 0,
|
||||
"sd_width": 512,
|
||||
"sd_height": 896,
|
||||
"tts_engine": "auto",
|
||||
"tts_voice": "Samantha",
|
||||
"tts_rate": 170,
|
||||
"piper_model": "",
|
||||
"gemini_api_key": "",
|
||||
"veo_model": "veo-3.1-fast-generate-preview",
|
||||
"aspect_ratio": "9:16",
|
||||
@@ -107,10 +124,21 @@ KIDS_VEO_SUFFIX = (
|
||||
"safe cheerful setting, nothing scary or dangerous, no realistic people, no logos, no flashing lights."
|
||||
)
|
||||
|
||||
KIDS_VISION_PROMPT = """These are still frames from a short video intended for young children.
|
||||
Mark it unsafe if ANY frame shows: realistic humans or children, violence, weapons, blood, injury, scary, creepy, or disturbing imagery, distorted or malformed faces or bodies, nudity or suggestive content, alcohol, tobacco, drugs, brand logos, readable text, or anything a parent would find inappropriate for a 4 year old.
|
||||
KIDS_IMAGE_SUFFIX = ", children's picture book illustration, cute cartoon characters, bright soft colors, cheerful and safe, gentle"
|
||||
|
||||
IMAGE_NEGATIVE = (
|
||||
"photorealistic, realistic human, real person, photograph, text, letters, words, watermark, logo, signature, "
|
||||
"scary, creepy, horror, violence, weapon, blood, gore, deformed, distorted, extra limbs, ugly, blurry, low quality, nsfw"
|
||||
)
|
||||
|
||||
KIDS_VISION_PROMPT = """This is a still frame from a short video intended for young children.
|
||||
Mark it unsafe if the image shows: realistic humans or children, violence, weapons, blood, injury, scary, creepy, or disturbing imagery, distorted or malformed faces or bodies, nudity or suggestive content, alcohol, tobacco, drugs, brand logos, readable text, or anything a parent would find inappropriate for a 4 year old.
|
||||
Return JSON only: {"safe": true or false, "reason": "short explanation"}"""
|
||||
|
||||
VISION_TEXT_PROMPT = """Look at this image, which is meant for young children.
|
||||
Answer with one word, safe or unsafe, then a short reason.
|
||||
Answer unsafe if it shows realistic humans or children, violence, weapons, blood, injury, scary or creepy imagery, distorted faces or bodies, nudity, alcohol, tobacco, drugs, brand logos, or anything a parent would find inappropriate for a 4 year old."""
|
||||
|
||||
KIDS_BANNED = re.compile(
|
||||
r"\b(kill\w*|blood\w*|bleed\w*|guns?|knife|knives|swords?|weapons?|bombs?|murder\w*|dead|die|dies|dying|death|"
|
||||
r"scary|spooky|terrif\w*|horror|creepy|nightmare\w*|ghost\w*|haunt\w*|zombie\w*|demon\w*|devil\w*|bhoot|"
|
||||
@@ -151,6 +179,8 @@ for _h in (
|
||||
|
||||
state_lock = threading.RLock()
|
||||
settings_lock = threading.RLock()
|
||||
pipe_lock = threading.Lock()
|
||||
pipeline = {"id": None, "obj": None}
|
||||
stop_event = threading.Event()
|
||||
runner = None
|
||||
pending_flow = None
|
||||
@@ -162,6 +192,10 @@ class Cancelled(Exception):
|
||||
pass
|
||||
|
||||
|
||||
class VisionUnavailable(Exception):
|
||||
pass
|
||||
|
||||
|
||||
def check():
|
||||
if stop_event.is_set():
|
||||
raise Cancelled()
|
||||
@@ -204,6 +238,14 @@ def resolve_storage(path_str):
|
||||
return p if p.is_absolute() else BASE / p
|
||||
|
||||
|
||||
def resolve_sub(s, key, fallback):
|
||||
raw = str(s.get(key, "")).strip().strip('"')
|
||||
if raw:
|
||||
p = Path(raw).expanduser()
|
||||
return p if p.is_absolute() else BASE / p
|
||||
return resolve_storage(s["storage_dir"]) / fallback
|
||||
|
||||
|
||||
def output_root(s):
|
||||
return resolve_storage(s["storage_dir"]) / "output"
|
||||
|
||||
@@ -212,16 +254,21 @@ def published_root(s):
|
||||
return resolve_storage(s["storage_dir"]) / "published"
|
||||
|
||||
|
||||
def validate_storage(path_str):
|
||||
root = resolve_storage(path_str)
|
||||
def writable(root):
|
||||
try:
|
||||
(root / "output").mkdir(parents=True, exist_ok=True)
|
||||
(root / "published").mkdir(parents=True, exist_ok=True)
|
||||
root.mkdir(parents=True, exist_ok=True)
|
||||
probe = root / ".write_test"
|
||||
probe.write_text("ok", encoding="utf-8")
|
||||
probe.unlink()
|
||||
except OSError as e:
|
||||
raise ValueError(f"Storage folder '{root}' is not writable: {e}")
|
||||
raise ValueError(f"Folder '{root}' is not writable: {e}")
|
||||
return root
|
||||
|
||||
|
||||
def validate_storage(path_str):
|
||||
root = writable(resolve_storage(path_str))
|
||||
(root / "output").mkdir(parents=True, exist_ok=True)
|
||||
(root / "published").mkdir(parents=True, exist_ok=True)
|
||||
return root
|
||||
|
||||
|
||||
@@ -232,12 +279,24 @@ def free_gb(path):
|
||||
return None
|
||||
|
||||
|
||||
def apply_paths(s):
|
||||
models = writable(resolve_sub(s, "models_dir", "models"))
|
||||
temp = writable(resolve_sub(s, "temp_dir", "tmp"))
|
||||
os.environ["HF_HOME"] = str(models)
|
||||
os.environ["HF_HUB_CACHE"] = str(models / "hub")
|
||||
os.environ["TORCH_HOME"] = str(models / "torch")
|
||||
os.environ["TMPDIR"] = str(temp)
|
||||
tempfile.tempdir = str(temp)
|
||||
return models, temp
|
||||
|
||||
|
||||
def prepare_storage(s):
|
||||
root = validate_storage(s["storage_dir"])
|
||||
free = free_gb(root)
|
||||
if free is not None and free < MIN_FREE_GB:
|
||||
raise RuntimeError(f"Only {free:.1f} GB free at {root}, need at least {MIN_FREE_GB} GB")
|
||||
logger.info("Storage: %s (%.1f GB free)", root, free or 0)
|
||||
models, temp = apply_paths(s)
|
||||
logger.info("Storage: %s (%.1f GB free) | Models: %s | Temp: %s", root, free or 0, models, temp)
|
||||
|
||||
|
||||
def finish_files(s, job_dir, job_id):
|
||||
@@ -404,11 +463,12 @@ def map_videos(items):
|
||||
|
||||
|
||||
def mostly_latin(text):
|
||||
letters = [c for c in text if c.isalpha()]
|
||||
stripped = re.sub(r"#\S+|https?://\S+|@\S+", " ", str(text))
|
||||
letters = [c for c in stripped if c.isalpha()]
|
||||
if not letters:
|
||||
return True
|
||||
return False
|
||||
latin = sum(1 for c in letters if ord(c) < 0x250)
|
||||
return latin / len(letters) >= 0.6
|
||||
return latin / len(letters) >= 0.8
|
||||
|
||||
|
||||
def filter_language(videos, lang):
|
||||
@@ -465,18 +525,80 @@ def fetch_trending(s):
|
||||
return filter_language(map_videos(items), lang)
|
||||
|
||||
|
||||
def ollama_json(s, prompt, temperature=0.9, model=None, images=None, num_predict=2048):
|
||||
def ollama_tags(s):
|
||||
try:
|
||||
r = requests.get(f"{s['ollama_url'].rstrip('/')}/api/tags", timeout=10)
|
||||
r.raise_for_status()
|
||||
return sorted(m["name"] for m in r.json().get("models", []) if m.get("name"))
|
||||
except Exception as e:
|
||||
logger.debug("Could not list Ollama models: %s", e)
|
||||
return []
|
||||
|
||||
|
||||
def ollama_has(s, model):
|
||||
installed = ollama_tags(s)
|
||||
return model in installed or f"{model}:latest" in installed
|
||||
|
||||
|
||||
def ollama_pull(s, model):
|
||||
model = (model or "").strip()
|
||||
if not model:
|
||||
raise RuntimeError("No model name given")
|
||||
if ollama_has(s, model):
|
||||
return False
|
||||
logger.info("Model '%s' is not installed, pulling it now (this can take a while)", model)
|
||||
url = f"{s['ollama_url'].rstrip('/')}/api/pull"
|
||||
last = ""
|
||||
with requests.post(url, json={"model": model, "stream": True}, stream=True, timeout=(10, 3600)) as r:
|
||||
r.raise_for_status()
|
||||
for line in r.iter_lines():
|
||||
check()
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
msg = json.loads(line)
|
||||
except ValueError:
|
||||
continue
|
||||
if msg.get("error"):
|
||||
raise RuntimeError(f"Could not pull '{model}': {msg['error']}")
|
||||
st = msg.get("status", "")
|
||||
total, done = msg.get("total"), msg.get("completed")
|
||||
if total and done:
|
||||
pct = int(done * 100 / total)
|
||||
line_txt = f"{st} {pct}%"
|
||||
if line_txt != last and pct % 10 == 0:
|
||||
logger.info("Pulling %s: %s", model, line_txt)
|
||||
last = line_txt
|
||||
elif st and st != last:
|
||||
logger.info("Pulling %s: %s", model, st)
|
||||
last = st
|
||||
logger.info("Model '%s' is ready", model)
|
||||
return True
|
||||
|
||||
|
||||
def ensure_models(s, engine):
|
||||
needed = [s["ollama_model"].strip()]
|
||||
if s["made_for_kids"] and s["vision_model"].strip():
|
||||
needed.append(s["vision_model"].strip())
|
||||
for m in needed:
|
||||
if m and not ollama_has(s, m):
|
||||
set_stage(f"downloading model {m}")
|
||||
ollama_pull(s, m)
|
||||
|
||||
|
||||
def ollama_call(s, prompt, temperature=0.9, model=None, images=None, num_predict=2048, as_json=True):
|
||||
url = f"{s['ollama_url'].rstrip('/')}/api/generate"
|
||||
model = model or s["ollama_model"]
|
||||
timeout = max(30, int(s["ollama_timeout"]))
|
||||
payload = {
|
||||
"model": model,
|
||||
"prompt": prompt,
|
||||
"format": "json",
|
||||
"stream": False,
|
||||
"keep_alive": "30m",
|
||||
"keep_alive": "5m",
|
||||
"options": {"temperature": temperature, "num_predict": num_predict, "num_ctx": 8192},
|
||||
}
|
||||
if as_json:
|
||||
payload["format"] = "json"
|
||||
if images:
|
||||
payload["images"] = images
|
||||
started = datetime.now()
|
||||
@@ -488,11 +610,17 @@ def ollama_json(s, prompt, temperature=0.9, model=None, images=None, num_predict
|
||||
except requests.exceptions.ConnectionError:
|
||||
raise RuntimeError(f"Cannot reach Ollama at {s['ollama_url']}. Is it running?")
|
||||
if r.status_code == 404:
|
||||
raise RuntimeError(f"Ollama model '{model}' not found. Run: ollama pull {model}")
|
||||
logger.warning("Ollama model '%s' is missing, pulling it now", model)
|
||||
ollama_pull(s, model)
|
||||
r = requests.post(url, json=payload, timeout=(10, timeout))
|
||||
r.raise_for_status()
|
||||
text = r.json().get("response", "")
|
||||
logger.debug("Ollama %s responded in %.1fs: %s", model, (datetime.now() - started).total_seconds(), text[:2000])
|
||||
return json.loads(text)
|
||||
return json.loads(text) if as_json else text
|
||||
|
||||
|
||||
def ollama_json(s, prompt, temperature=0.9, model=None, images=None, num_predict=2048):
|
||||
return ollama_call(s, prompt, temperature=temperature, model=model, images=images, num_predict=num_predict, as_json=True)
|
||||
|
||||
|
||||
def write_script(s, src, feedback=None):
|
||||
@@ -522,7 +650,7 @@ Do not reuse the source's title, script, characters, jokes, branding, or channel
|
||||
Do not depict real people, celebrities, brands, logos, or copyrighted characters. Invent new characters.
|
||||
|
||||
The Short has exactly {n} scenes of {clip} seconds each. Each scene has:
|
||||
"visual": a detailed, self-contained cinematic shot description for an AI video model (subject, setting, action, camera, lighting). Repeat key character and setting details in every scene so they stay consistent.
|
||||
"visual": a detailed, self-contained shot description of one still image (subject, setting, action, mood, lighting). Repeat key character and setting details in every scene so they stay consistent. Never mention on-screen text or words.
|
||||
"narration": one spoken line of at most {words} words.
|
||||
|
||||
Scene 1 must hook the viewer in the first 2 seconds. The final scene must deliver a payoff.
|
||||
@@ -728,29 +856,194 @@ def produce_script(s, src):
|
||||
raise RuntimeError(f"Script failed kids compliance review after {attempts} attempts")
|
||||
|
||||
|
||||
def unload_pipeline():
|
||||
with pipe_lock:
|
||||
if pipeline["obj"] is None:
|
||||
return
|
||||
pipeline["obj"] = None
|
||||
pipeline["id"] = None
|
||||
try:
|
||||
import gc
|
||||
import torch
|
||||
gc.collect()
|
||||
if torch.backends.mps.is_available():
|
||||
torch.mps.empty_cache()
|
||||
elif torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
except Exception:
|
||||
pass
|
||||
logger.debug("Released image model from memory")
|
||||
|
||||
|
||||
def kids_vision_check(s, clip_path):
|
||||
model = s["vision_model"].strip()
|
||||
if not model:
|
||||
raise RuntimeError("Made for kids requires a vision model in Settings to check generated clips")
|
||||
clip = int(s["clip_seconds"])
|
||||
images = []
|
||||
for idx, t in enumerate((0.5, clip / 2, max(0.5, clip - 0.5))):
|
||||
frame = clip_path.with_name(f"{clip_path.stem}_f{idx}.jpg")
|
||||
frame = clip_path.with_name(f"{clip_path.stem}_frame.jpg")
|
||||
r = subprocess.run(
|
||||
["ffmpeg", "-y", "-ss", str(t), "-i", str(clip_path), "-frames:v", "1", "-vf", "scale=512:-2", str(frame)],
|
||||
["ffmpeg", "-y", "-ss", str(int(s["clip_seconds"]) / 2), "-i", str(clip_path), "-frames:v", "1", "-vf", "scale=512:-2", str(frame)],
|
||||
capture_output=True, text=True,
|
||||
)
|
||||
if r.returncode != 0 or not frame.exists():
|
||||
raise RuntimeError(f"Could not extract frame at {t}s for vision check")
|
||||
images.append(base64.b64encode(frame.read_bytes()).decode())
|
||||
raise RuntimeError("Could not extract a frame for the vision check")
|
||||
images = [base64.b64encode(frame.read_bytes()).decode()]
|
||||
frame.unlink(missing_ok=True)
|
||||
data = ollama_json(s, KIDS_VISION_PROMPT, temperature=0.1, model=model, images=images, num_predict=256)
|
||||
if not truthy(data.get("safe")):
|
||||
raise ValueError(f"Vision check rejected {clip_path.name}: {data.get('reason', 'no reason given')}")
|
||||
if not model.split(":")[0] in SMALL_VISION_MODELS:
|
||||
unload_pipeline()
|
||||
safe, reason = True, ""
|
||||
try:
|
||||
try:
|
||||
data = ollama_call(s, KIDS_VISION_PROMPT, temperature=0.1, model=model, images=images, num_predict=256)
|
||||
safe = truthy(data.get("safe"))
|
||||
reason = str(data.get("reason", ""))
|
||||
except (requests.exceptions.HTTPError, ValueError):
|
||||
text = ollama_call(s, VISION_TEXT_PROMPT, temperature=0.1, model=model, images=images, num_predict=128, as_json=False)
|
||||
low = text.lower()
|
||||
safe = "unsafe" not in low
|
||||
reason = text.strip()[:200]
|
||||
except Cancelled:
|
||||
raise
|
||||
except Exception as e:
|
||||
raise VisionUnavailable(f"vision model '{model}' could not run: {e}") from e
|
||||
if not safe:
|
||||
raise ValueError(f"Vision check rejected {clip_path.name}: {reason or 'no reason given'}")
|
||||
logger.info("Vision check passed for %s", clip_path.name)
|
||||
|
||||
|
||||
def generate_clip(client, s, prompt, path):
|
||||
def load_pipeline(s):
|
||||
with pipe_lock:
|
||||
model = s["sd_model"].strip()
|
||||
if pipeline["id"] == model and pipeline["obj"] is not None:
|
||||
return pipeline["obj"]
|
||||
try:
|
||||
import torch
|
||||
from diffusers import AutoPipelineForText2Image
|
||||
except ImportError:
|
||||
raise RuntimeError("Local engine needs extra packages. Run ./install-local.sh")
|
||||
if torch.backends.mps.is_available():
|
||||
device, dtype = "mps", torch.float16
|
||||
elif torch.cuda.is_available():
|
||||
device, dtype = "cuda", torch.float16
|
||||
else:
|
||||
device, dtype = "cpu", torch.float32
|
||||
logger.info("Loading image model %s on %s (first run downloads several GB)", model, device)
|
||||
pipe = AutoPipelineForText2Image.from_pretrained(model, torch_dtype=dtype, variant="fp16" if dtype == torch.float16 else None)
|
||||
pipe = pipe.to(device)
|
||||
pipe.set_progress_bar_config(disable=True)
|
||||
pipeline["id"] = model
|
||||
pipeline["obj"] = pipe
|
||||
logger.info("Image model ready")
|
||||
return pipe
|
||||
|
||||
|
||||
def generate_image(s, prompt, path):
|
||||
pipe = load_pipeline(s)
|
||||
kwargs = {
|
||||
"prompt": prompt[:900],
|
||||
"num_inference_steps": max(1, int(s["sd_steps"])),
|
||||
"guidance_scale": float(s["sd_guidance"]),
|
||||
"width": int(s["sd_width"]),
|
||||
"height": int(s["sd_height"]),
|
||||
}
|
||||
if kwargs["guidance_scale"] > 0:
|
||||
kwargs["negative_prompt"] = IMAGE_NEGATIVE
|
||||
started = datetime.now()
|
||||
image = pipe(**kwargs).images[0]
|
||||
image.save(str(path))
|
||||
logger.info("Generated %s in %.1fs", path.name, (datetime.now() - started).total_seconds())
|
||||
|
||||
|
||||
def tts_speak(s, text, out_wav):
|
||||
engine = s["tts_engine"] if s["tts_engine"] in TTS_ENGINES else "auto"
|
||||
if engine == "auto":
|
||||
engine = "say" if sys.platform == "darwin" else ("piper" if s["piper_model"].strip() else "espeak")
|
||||
text = text.strip() or "..."
|
||||
if engine == "say":
|
||||
aiff = out_wav.with_suffix(".aiff")
|
||||
cmd = ["say", "-r", str(int(s["tts_rate"])), "-o", str(aiff)]
|
||||
if s["tts_voice"].strip():
|
||||
cmd += ["-v", s["tts_voice"].strip()]
|
||||
cmd.append(text)
|
||||
r = subprocess.run(cmd, capture_output=True, text=True)
|
||||
if r.returncode != 0:
|
||||
raise RuntimeError(f"say failed: {r.stderr.strip()[:200]}")
|
||||
conv = subprocess.run(["ffmpeg", "-y", "-i", str(aiff), "-ar", "44100", "-ac", "2", str(out_wav)], capture_output=True, text=True)
|
||||
aiff.unlink(missing_ok=True)
|
||||
if conv.returncode != 0:
|
||||
raise RuntimeError("Could not convert narration audio")
|
||||
elif engine == "espeak":
|
||||
voice = s["tts_voice"].strip() or "en-us"
|
||||
r = subprocess.run(["espeak-ng", "-v", voice, "-s", str(int(s["tts_rate"])), "-w", str(out_wav), text], capture_output=True, text=True)
|
||||
if r.returncode != 0:
|
||||
raise RuntimeError(f"espeak-ng failed: {r.stderr.strip()[:200]}")
|
||||
else:
|
||||
model = s["piper_model"].strip()
|
||||
if not model:
|
||||
raise RuntimeError("Piper needs a voice model path in Settings")
|
||||
r = subprocess.run(["piper", "--model", model, "--output_file", str(out_wav)], input=text, capture_output=True, text=True)
|
||||
if r.returncode != 0:
|
||||
raise RuntimeError(f"piper failed: {r.stderr.strip()[:200]}")
|
||||
if not out_wav.exists() or out_wav.stat().st_size < 1000:
|
||||
raise RuntimeError("Narration audio was empty")
|
||||
|
||||
|
||||
def media_duration(path):
|
||||
r = subprocess.run(
|
||||
["ffprobe", "-v", "error", "-show_entries", "format=duration", "-of", "default=noprint_wrappers=1:nokey=1", str(path)],
|
||||
capture_output=True, text=True,
|
||||
)
|
||||
try:
|
||||
return float(r.stdout.strip())
|
||||
except ValueError:
|
||||
return 0.0
|
||||
|
||||
|
||||
def render_scene(s, image, audio, out, aspect):
|
||||
w, h = (1080, 1920) if aspect == "9:16" else (1920, 1080)
|
||||
dur = max(float(s["clip_seconds"]), media_duration(audio) + 0.6)
|
||||
frames = int(dur * 30)
|
||||
vf = (
|
||||
f"[0:v]scale={w * 2}:{h * 2}:force_original_aspect_ratio=increase,crop={w * 2}:{h * 2},"
|
||||
f"zoompan=z='min(zoom+0.0004,1.15)':x='iw/2-(iw/zoom/2)':y='ih/2-(ih/zoom/2)':d={frames}:s={w}x{h}:fps=30,"
|
||||
f"format=yuv420p[v];[1:a]apad,atrim=0:{dur:.2f},asetpts=N/SR/TB[a]"
|
||||
)
|
||||
cmd = [
|
||||
"ffmpeg", "-y", "-loop", "1", "-i", str(image), "-i", str(audio),
|
||||
"-filter_complex", vf, "-map", "[v]", "-map", "[a]",
|
||||
"-c:v", "libx264", "-preset", "medium", "-crf", "20",
|
||||
"-c:a", "aac", "-b:a", "160k", "-t", f"{dur:.2f}", str(out),
|
||||
]
|
||||
r = subprocess.run(cmd, capture_output=True, text=True)
|
||||
if r.returncode != 0:
|
||||
logger.error("ffmpeg output:\n%s", r.stderr[-3000:])
|
||||
raise RuntimeError("Could not render scene video")
|
||||
logger.info("Rendered %s (%.1fs)", out.name, dur)
|
||||
|
||||
|
||||
def make_clip_local(s, scene, script, path, kids):
|
||||
image = path.with_suffix(".png")
|
||||
audio = path.with_suffix(".wav")
|
||||
style = script["style"].strip()
|
||||
prompt = f"{scene['visual']} {style}{KIDS_IMAGE_SUFFIX if kids else ''}"
|
||||
logger.debug("Image prompt: %s", prompt)
|
||||
generate_image(s, prompt, image)
|
||||
tts_speak(s, scene["narration"], audio)
|
||||
render_scene(s, image, audio, path, s["aspect_ratio"])
|
||||
if not s["keep_clips"]:
|
||||
audio.unlink(missing_ok=True)
|
||||
return image
|
||||
|
||||
|
||||
def make_clip_veo(s, scene, script, path, kids):
|
||||
from google import genai
|
||||
from google.genai import types
|
||||
client = genai.Client(api_key=s["gemini_api_key"])
|
||||
orientation = "Vertical" if s["aspect_ratio"] == "9:16" else "Widescreen"
|
||||
prompt = (
|
||||
f"{scene['visual']} Style: {script['style']}. {orientation} short-form video. "
|
||||
f"A narrator's voiceover says: \"{scene['narration']}\" "
|
||||
f"No on-screen text, captions, or subtitles.{KIDS_VEO_SUFFIX if kids else ''}"
|
||||
)
|
||||
logger.debug("Veo prompt: %s", prompt)
|
||||
op = client.models.generate_videos(
|
||||
model=s["veo_model"],
|
||||
@@ -775,6 +1068,7 @@ def generate_clip(client, s, prompt, path):
|
||||
client.files.download(file=v.video)
|
||||
v.video.save(str(path))
|
||||
logger.info("Saved clip %s", path.name)
|
||||
return None
|
||||
|
||||
|
||||
def concat_clips(clips, out, aspect):
|
||||
@@ -840,7 +1134,8 @@ def upload_video(s, path, script, tags):
|
||||
|
||||
|
||||
def run_job(s):
|
||||
if not s["gemini_api_key"]:
|
||||
engine = s["video_engine"] if s["video_engine"] in ENGINES else "local"
|
||||
if engine == "veo" and not s["gemini_api_key"]:
|
||||
raise RuntimeError("Gemini API key is not set in Settings")
|
||||
if not shutil.which("ffmpeg"):
|
||||
raise RuntimeError("ffmpeg was not found on PATH")
|
||||
@@ -848,7 +1143,8 @@ def run_job(s):
|
||||
if kids and not s["vision_model"].strip():
|
||||
raise RuntimeError("Made for kids requires a vision model in Settings (e.g. llama3.2-vision)")
|
||||
prepare_storage(s)
|
||||
logger.info("Profile: %s | Niche: %s | Made for kids: %s | Upload category: %s", s["name"], s["channel_niche"] or "none", kids, s["upload_category_id"])
|
||||
ensure_models(s, engine)
|
||||
logger.info("Profile: %s | Engine: %s | Niche: %s | Made for kids: %s | Upload category: %s", s["name"], engine, s["channel_niche"] or "none", kids, s["upload_category_id"])
|
||||
if kids:
|
||||
logger.info(
|
||||
"Kids compliance active: script rules, word filters, LLM review (%s), frame vision checks, made-for-kids flag%s",
|
||||
@@ -892,41 +1188,56 @@ def run_job(s):
|
||||
finalized = False
|
||||
try:
|
||||
script, tags, compliance = produce_script(s, src)
|
||||
compliance["engine"] = engine
|
||||
update_history(job_id, title=script["title"], notes="; ".join(compliance.get("reviewer_notes", []))[:400])
|
||||
write_json(job_dir / "script.json", {"profile": s["name"], "source": src, "script": script, "tags": tags})
|
||||
client = genai.Client(api_key=s["gemini_api_key"])
|
||||
orientation = "Vertical" if s["aspect_ratio"] == "9:16" else "Widescreen"
|
||||
suffix = KIDS_VEO_SUFFIX if kids else ""
|
||||
clips = []
|
||||
extras = []
|
||||
vision_log = []
|
||||
n = len(script["scenes"])
|
||||
for i, scene in enumerate(script["scenes"], 1):
|
||||
check()
|
||||
set_stage(f"generating clip {i}/{n}")
|
||||
set_stage(f"generating scene {i}/{n}")
|
||||
path = job_dir / f"clip_{i:02d}.mp4"
|
||||
prompt = (
|
||||
f"{scene['visual']} Style: {script['style']}. {orientation} short-form video. "
|
||||
f"A narrator's voiceover says: \"{scene['narration']}\" "
|
||||
f"No on-screen text, captions, or subtitles.{suffix}"
|
||||
)
|
||||
for attempt in range(1, 4):
|
||||
try:
|
||||
generate_clip(client, s, prompt, path)
|
||||
if kids:
|
||||
set_stage(f"vision check clip {i}/{n}")
|
||||
kids_vision_check(s, path)
|
||||
vision_log.append({"clip": i, "attempt": attempt, "result": "passed"})
|
||||
break
|
||||
if engine == "local":
|
||||
extra = make_clip_local(s, scene, script, path, kids)
|
||||
else:
|
||||
extra = make_clip_veo(s, scene, script, path, kids)
|
||||
except Cancelled:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.warning("Clip %d attempt %d failed: %s", i, attempt, e)
|
||||
logger.warning("Scene %d attempt %d failed: %s", i, attempt, e)
|
||||
path.unlink(missing_ok=True)
|
||||
if attempt == 3:
|
||||
raise
|
||||
stop_event.wait(10)
|
||||
continue
|
||||
if kids:
|
||||
set_stage(f"vision check scene {i}/{n}")
|
||||
try:
|
||||
kids_vision_check(s, path)
|
||||
vision_log.append({"clip": i, "attempt": attempt, "result": "passed"})
|
||||
except Cancelled:
|
||||
raise
|
||||
except VisionUnavailable as e:
|
||||
if s["kids_manual_review"]:
|
||||
logger.warning("Scene %d: %s. Continuing, check this video manually before publishing", i, e)
|
||||
vision_log.append({"clip": i, "attempt": attempt, "result": f"skipped: {e}"[:300]})
|
||||
else:
|
||||
raise RuntimeError(f"{e}. Turn on manual review or pick a working vision model")
|
||||
except Exception as e:
|
||||
logger.warning("Scene %d attempt %d rejected: %s", i, attempt, e)
|
||||
vision_log.append({"clip": i, "attempt": attempt, "result": str(e)[:300]})
|
||||
path.unlink(missing_ok=True)
|
||||
if attempt == 3:
|
||||
raise
|
||||
stop_event.wait(20)
|
||||
stop_event.wait(5)
|
||||
continue
|
||||
if extra:
|
||||
extras.append(extra)
|
||||
break
|
||||
clips.append(path)
|
||||
if kids:
|
||||
compliance["vision_checks"] = vision_log
|
||||
@@ -949,7 +1260,7 @@ def run_job(s):
|
||||
write_json(job_dir / "compliance.json", compliance)
|
||||
finalized = True
|
||||
if not s["keep_clips"]:
|
||||
for c in clips:
|
||||
for c in clips + extras:
|
||||
c.unlink(missing_ok=True)
|
||||
set_stage("moving files")
|
||||
location = finish_files(s, job_dir, job_id)
|
||||
@@ -1019,6 +1330,7 @@ def api_status():
|
||||
"running": bool(runner and runner.is_alive()),
|
||||
"stage": status["stage"],
|
||||
"profile": s["name"],
|
||||
"engine": s["video_engine"],
|
||||
"made_for_kids": s["made_for_kids"],
|
||||
"published_today": published_today(s["profile_id"]),
|
||||
"videos_per_day": s["videos_per_day"],
|
||||
@@ -1041,6 +1353,11 @@ def get_settings():
|
||||
"active": g["active_profile"],
|
||||
"profiles": list_profiles(),
|
||||
"storage_resolved": str(resolve_storage(g["storage_dir"])),
|
||||
"models_resolved": str(resolve_sub(g, "models_dir", "models")),
|
||||
"temp_resolved": str(resolve_sub(g, "temp_dir", "tmp")),
|
||||
"installed_models": ollama_tags(g),
|
||||
"suggested_text_models": SUGGESTED_TEXT_MODELS,
|
||||
"suggested_vision_models": SUGGESTED_VISION_MODELS,
|
||||
}
|
||||
|
||||
|
||||
@@ -1061,10 +1378,16 @@ async def post_settings(request: Request):
|
||||
if k in PROFILE_DEFAULTS:
|
||||
p[k] = coerce(PROFILE_DEFAULTS, k, v)
|
||||
g["storage_dir"] = g["storage_dir"].strip().strip('"') or "videos"
|
||||
if g["video_engine"] not in ENGINES:
|
||||
g["video_engine"] = "local"
|
||||
if g["tts_engine"] not in TTS_ENGINES:
|
||||
g["tts_engine"] = "auto"
|
||||
p["source_language"] = p["source_language"].lower()[:5]
|
||||
if p["kids_llm_review"] not in REVIEW_MODES:
|
||||
p["kids_llm_review"] = "advisory"
|
||||
root = validate_storage(g["storage_dir"])
|
||||
writable(resolve_sub(g, "models_dir", "models"))
|
||||
writable(resolve_sub(g, "temp_dir", "tmp"))
|
||||
except (TypeError, ValueError) as e:
|
||||
return JSONResponse({"detail": str(e)}, status_code=400)
|
||||
if not p["name"]:
|
||||
@@ -1149,6 +1472,20 @@ def api_stop():
|
||||
return {"ok": True}
|
||||
|
||||
|
||||
@app.post("/api/models/pull")
|
||||
async def api_pull_model(request: Request):
|
||||
model = str((await request.json()).get("model", "")).strip()
|
||||
if not model:
|
||||
return JSONResponse({"detail": "No model name given"}, status_code=400)
|
||||
g = load_global()
|
||||
try:
|
||||
pulled = ollama_pull(g, model)
|
||||
except Exception as e:
|
||||
logger.exception("Model pull failed")
|
||||
return JSONResponse({"detail": str(e)}, status_code=400)
|
||||
return {"ok": True, "pulled": pulled, "models": ollama_tags(g)}
|
||||
|
||||
|
||||
@app.get("/api/trending")
|
||||
def api_trending():
|
||||
try:
|
||||
|
||||
+5
-4
@@ -4,7 +4,7 @@ set -euo pipefail
|
||||
cd "$(dirname "$0")"
|
||||
|
||||
TEXT_MODEL="${TEXT_MODEL:-llama3.1:8b}"
|
||||
VISION_MODEL="${VISION_MODEL:-llama3.2-vision}"
|
||||
VISION_MODEL="${VISION_MODEL:-moondream}"
|
||||
OLLAMA_URL="http://localhost:11434"
|
||||
OS="$(uname -s)"
|
||||
PYTHON=""
|
||||
@@ -135,13 +135,13 @@ fi
|
||||
|
||||
info "Updating .gitignore"
|
||||
touch .gitignore
|
||||
for entry in data/ videos/ client_secret.json __pycache__/ '*.pyc' .venv/; do
|
||||
for entry in data/ videos/ models/ tmp/ client_secret.json __pycache__/ '*.pyc' .venv/; do
|
||||
if ! grep -qxF "$entry" .gitignore; then
|
||||
echo "$entry" >> .gitignore
|
||||
fi
|
||||
done
|
||||
|
||||
chmod +x run.sh 2>/dev/null || true
|
||||
chmod +x run.sh install-local.sh 2>/dev/null || true
|
||||
|
||||
info "Checking setup"
|
||||
if [ -f client_secret.json ]; then
|
||||
@@ -149,7 +149,8 @@ if [ -f client_secret.json ]; then
|
||||
else
|
||||
warn "client_secret.json is missing. Download it from Google Cloud and place it next to app.py"
|
||||
fi
|
||||
echo "After the app starts, paste your Gemini API key in Settings"
|
||||
echo "For the free local video engine, run ./install-local.sh next"
|
||||
echo "Models can also be added later from the Settings dropdowns"
|
||||
|
||||
info "Install complete"
|
||||
echo "Start the app any time with: ./run.sh"
|
||||
|
||||
+79
-23
@@ -17,7 +17,9 @@ main{padding:20px;max-width:1100px;margin:0 auto}
|
||||
.row{display:flex;gap:12px;align-items:center;flex-wrap:wrap}
|
||||
.btn{border:none;border-radius:8px;padding:10px 18px;font-size:15px;cursor:pointer;color:#fff;background:#3949ab;text-decoration:none;display:inline-block}
|
||||
.btn:disabled{opacity:.4;cursor:default}
|
||||
.btn.go{background:var(--ok);font-size:20px;padding:14px 40px}
|
||||
.btn.go{background:var(--ok);font-size:20px;padding:14px 40px;min-width:150px}
|
||||
.btn.go.running{background:var(--accent)}
|
||||
.btn.go.busy{background:#e65100}
|
||||
.btn.stop{background:var(--accent)}
|
||||
.btn.secondary{background:#394150}
|
||||
.stat{min-width:150px}
|
||||
@@ -57,9 +59,9 @@ a{color:#64b5f6}
|
||||
<section id="dash" class="tab active">
|
||||
<div class="panel row">
|
||||
<button id="go" class="btn go">GO</button>
|
||||
<button id="stop" class="btn stop">Stop</button>
|
||||
<div class="stat"><div class="k">Active profile</div><select id="prof-dash" class="prof-select"></select></div>
|
||||
<div class="stat"><div class="k">Status</div><div class="v" id="st-stage">idle</div></div>
|
||||
<div class="stat"><div class="k">Engine</div><div class="v" id="st-engine"></div></div>
|
||||
<div class="stat"><div class="k">Published today</div><div class="v" id="st-today">0</div></div>
|
||||
<div class="stat"><div class="k">Channel</div><div class="v" id="st-channel">Not linked</div></div>
|
||||
<span class="badge" id="kids-badge">Made for kids: compliance checks on</span>
|
||||
@@ -70,9 +72,8 @@ a{color:#64b5f6}
|
||||
<div class="stat"><div class="k">After publishing</div><div class="v" id="st-after"></div></div>
|
||||
</div>
|
||||
<div class="panel row">
|
||||
<a class="btn secondary" href="/auth/start">Link YouTube channel to this profile</a>
|
||||
<button class="btn secondary" id="unlink">Unlink</button>
|
||||
<button class="btn secondary" id="preview">Preview trending</button>
|
||||
<button class="btn secondary" id="link">Link YouTube Channel</button>
|
||||
<button class="btn secondary" id="preview">Preview Trending Videos</button>
|
||||
<span class="hint" id="preview-msg"></span>
|
||||
</div>
|
||||
<div class="panel" id="trending-panel" style="display:none"><h3>Trending</h3><table id="trending"></table></div>
|
||||
@@ -117,20 +118,33 @@ const FIELDS=[
|
||||
["trending_count","Videos to scan","number","Max 50","p"],
|
||||
["videos_per_day","Videos per day","number","0 runs continuously","p"],
|
||||
["minutes_between_videos","Minutes between videos","number","0 for no gap","p"],
|
||||
["","Storage (shared by all profiles)","header","",""],
|
||||
["storage_dir","Storage folder","wide","Full path on a large disk, e.g. /Volumes/BigDisk/ShortsVideos. Jobs are built in output/ and moved to published/ after upload","g"],
|
||||
["","Folders (shared by all profiles)","header","",""],
|
||||
["storage_dir","Storage folder","wide","Full path on a large disk, e.g. /Volumes/iDrive/shorts-autopilot. Jobs are built in output/ and moved to published/","g"],
|
||||
["models_dir","Models folder","wide","Where AI models download to (several GB). Blank puts them in the storage folder","g"],
|
||||
["temp_dir","Temp folder","wide","Scratch space used while rendering. Blank puts it in the storage folder","g"],
|
||||
["delete_after_publish","Delete videos after publishing","checkbox","Removes the job folder once the upload succeeds instead of moving it to published","g"],
|
||||
["keep_clips","Keep individual clips","checkbox","Keeps the 8 second clips alongside final.mp4","g"],
|
||||
["","Generation (shared by all profiles)","header","",""],
|
||||
["keep_clips","Keep individual clips","checkbox","Keeps the scene clips, images, and narration alongside final.mp4","g"],
|
||||
["","Video engine (shared by all profiles)","header","",""],
|
||||
["video_engine","Video engine","select:local=Local (free: AI images, narration, motion)|veo=Veo (paid Google API)","Local runs entirely on this machine at no cost. Veo makes true AI video but needs billing","g"],
|
||||
["sd_model","Image model","text","Hugging Face model id, e.g. stabilityai/sdxl-turbo or stabilityai/stable-diffusion-xl-base-1.0","g"],
|
||||
["sd_steps","Image steps","number","4 for turbo models, 20 to 30 for standard models","g"],
|
||||
["sd_guidance","Image guidance","number","0 for turbo models, 7 for standard models","g"],
|
||||
["sd_width","Image width","number","512 suits turbo. Larger is slower","g"],
|
||||
["sd_height","Image height","number","896 suits turbo vertical","g"],
|
||||
["tts_engine","Narration engine","select:auto=Auto|say=macOS say|espeak=espeak-ng|piper=Piper","Auto picks the best available for this machine","g"],
|
||||
["tts_voice","Narration voice","text","macOS: Samantha, Alex, Daniel. espeak: en-us","g"],
|
||||
["tts_rate","Narration speed","number","macOS words per minute (170). espeak uses a similar scale","g"],
|
||||
["piper_model","Piper voice file","wide","Full path to a .onnx voice, only needed for Piper","g"],
|
||||
["gemini_api_key","Gemini API key (Veo)","password","Only needed for the Veo engine. Billing must be enabled","g"],
|
||||
["veo_model","Veo model","text","e.g. veo-3.1-fast-generate-preview","g"],
|
||||
["","Text and shared options","header","",""],
|
||||
["ollama_url","Ollama URL","text","","g"],
|
||||
["ollama_model","Ollama model","text","Any pulled model, e.g. llama3.1:8b, or llama3.2:3b on 8 GB Macs","g"],
|
||||
["vision_model","Ollama vision model","text","Required for made-for-kids profiles. Run: ollama pull llama3.2-vision","g"],
|
||||
["ollama_model","Ollama model","model:text","Writes scripts and tags. Models not installed yet are downloaded automatically","g"],
|
||||
["vision_model","Ollama vision model","model:vision","Checks generated scenes on made-for-kids profiles. moondream suits 16 GB machines","g"],
|
||||
["ollama_timeout","Ollama timeout (seconds)","number","Max wait per Ollama request before retrying","g"],
|
||||
["gemini_api_key","Gemini API key (Veo)","password","Billing must be enabled","g"],
|
||||
["veo_model","Veo model","text","e.g. veo-3.1-fast-generate-preview. Update if Google renames or retires the model","g"],
|
||||
["aspect_ratio","Aspect ratio","select:9:16|16:9","","g"],
|
||||
["target_seconds","Target length (seconds)","number","Split into clips of the length below","g"],
|
||||
["clip_seconds","Seconds per clip","number","Veo clips are 8 seconds","g"],
|
||||
["target_seconds","Target length (seconds)","number","Split into scenes of the length below","g"],
|
||||
["clip_seconds","Seconds per scene","number","8 gives 7 scenes in a 60 second Short","g"],
|
||||
["error_cooldown_minutes","Wait after error (minutes)","number","","g"]
|
||||
];
|
||||
const $=s=>document.querySelector(s);
|
||||
@@ -150,17 +164,41 @@ const s=scope==="g"?data.global:data.profile;
|
||||
let input;
|
||||
if(type.startsWith("select:")){input=`<select id="f-${k}">${type.slice(7).split("|").map(o=>{const [v,l]=opt(o);return `<option value="${esc(v)}" ${String(s[k])===v?"selected":""}>${esc(l)}</option>`}).join("")}</select>`}
|
||||
else if(type==="checkbox"){input=`<input type="checkbox" id="f-${k}" ${s[k]?"checked":""} style="width:auto">`}
|
||||
else if(type.startsWith("model:")){
|
||||
const kind=type.slice(6);
|
||||
const installed=data.installed_models||[];
|
||||
const suggested=(kind==="vision"?data.suggested_vision_models:data.suggested_text_models)||[];
|
||||
const cur=String(s[k]||"");
|
||||
const seen=new Set();
|
||||
const opts=[];
|
||||
for(const m of installed){if(seen.has(m))continue;seen.add(m);opts.push(`<option value="${esc(m)}" ${cur===m?"selected":""}>${esc(m)}</option>`)}
|
||||
for(const m of suggested){if(seen.has(m)||seen.has(m+":latest"))continue;seen.add(m);opts.push(`<option value="${esc(m)}" ${cur===m?"selected":""}>${esc(m)} (not installed)</option>`)}
|
||||
if(cur&&!seen.has(cur))opts.unshift(`<option value="${esc(cur)}" selected>${esc(cur)} (not installed)</option>`);
|
||||
input=`<div class="row" style="gap:6px"><select id="f-${k}" style="flex:1">${opts.join("")}</select><button type="button" class="btn secondary pull" data-for="${k}" style="padding:8px 12px;font-size:13px">Pull now</button></div>`}
|
||||
else{input=`<input type="${type==="wide"?"text":type}" id="f-${k}" value="${esc(s[k])}">`}
|
||||
let extra="";
|
||||
if(k==="storage_dir")extra=`<div class="hint">Resolves to: ${esc(data.storage_resolved)}</div>`;
|
||||
if(k==="models_dir")extra=`<div class="hint">Resolves to: ${esc(data.models_resolved)}</div>`;
|
||||
if(k==="temp_dir")extra=`<div class="hint">Resolves to: ${esc(data.temp_resolved)}</div>`;
|
||||
const d=document.createElement("div");if(type==="wide")d.className="wide";
|
||||
d.innerHTML=`<label>${label}</label>${input}${hint?`<div class="hint">${esc(hint)}</div>`:""}${extra}`;g.appendChild(d)}
|
||||
syncKidsFields()}
|
||||
function syncKidsFields(){
|
||||
document.querySelectorAll("button.pull").forEach(b=>b.onclick=async()=>{
|
||||
const sel=$("#f-"+b.dataset.for);const model=sel.value;
|
||||
b.disabled=true;const was=b.textContent;b.textContent="Pulling...";
|
||||
try{await api("/api/models/pull",{method:"POST",body:JSON.stringify({model})});await loadSettings()}
|
||||
catch(e){alert(e.message);b.disabled=false;b.textContent=was}});
|
||||
syncFields()}
|
||||
function syncFields(){
|
||||
const kids=$("#f-made_for_kids");
|
||||
if(!kids)return;
|
||||
if(kids){
|
||||
for(const id of ["#f-kids_manual_review","#f-kids_llm_review"]){const el=$(id);if(!el)continue;el.disabled=!kids.checked;el.closest("div").style.opacity=kids.checked?1:.5}
|
||||
kids.onchange=syncKidsFields}
|
||||
kids.onchange=syncFields}
|
||||
const eng=$("#f-video_engine");
|
||||
if(eng){
|
||||
const local=eng.value==="local";
|
||||
for(const id of ["#f-sd_model","#f-sd_steps","#f-sd_guidance","#f-sd_width","#f-sd_height","#f-tts_engine","#f-tts_voice","#f-tts_rate","#f-piper_model"]){const el=$(id);if(!el)continue;el.disabled=!local;el.closest("div").style.opacity=local?1:.5}
|
||||
for(const id of ["#f-gemini_api_key","#f-veo_model"]){const el=$(id);if(!el)continue;el.disabled=local;el.closest("div").style.opacity=local?.5:1}
|
||||
eng.onchange=syncFields}}
|
||||
async function saveSettings(){
|
||||
const body={global:{},profile:{}};
|
||||
for(const [k,,type,,scope] of FIELDS){if(type==="header")continue;const el=$("#f-"+k);body[scope==="g"?"global":"profile"][k]=type==="checkbox"?el.checked:type==="number"?Number(el.value):el.value}
|
||||
@@ -170,6 +208,7 @@ async function refresh(){
|
||||
try{
|
||||
const s=await api("/api/status");
|
||||
$("#st-stage").textContent=s.stage;
|
||||
$("#st-engine").textContent=s.engine==="local"?"Local (free)":"Veo (paid)";
|
||||
$("#st-today").textContent=s.published_today+(s.videos_per_day>0?" / "+s.videos_per_day:"");
|
||||
$("#st-channel").textContent=s.channel||"Not linked";
|
||||
$("#st-storage").textContent=s.storage;
|
||||
@@ -177,7 +216,8 @@ $("#st-free").textContent=s.storage_free_gb===null?"not created yet":s.storage_f
|
||||
$("#st-free").style.color=s.storage_free_gb!==null&&s.storage_free_gb<10?"#ffb74d":"";
|
||||
$("#st-after").textContent=s.delete_after_publish?"Delete files":"Move to published";
|
||||
$("#kids-badge").style.display=s.made_for_kids?"inline-block":"none";
|
||||
$("#go").disabled=s.running;$("#stop").disabled=!s.running;
|
||||
const lb=$("#link");lb.textContent=s.channel?"Unlink YouTube Channel":"Link YouTube Channel";lb.dataset.linked=s.channel?"1":"";
|
||||
setButton(s.running);
|
||||
$("#jobs").innerHTML="<tr><th>Started</th><th>Profile</th><th>Source</th><th>Title</th><th>Status</th><th>Video</th></tr>"+s.history.map(h=>`<tr><td>${esc(h.started)}</td><td>${esc(h.profile)}</td><td><a target="_blank" href="https://youtube.com/watch?v=${esc(h.source_id)}">${esc(h.source_title)}</a></td><td>${esc(h.title)}</td><td><span class="pill ${esc(h.status)}">${h.status==="review"?"private, needs review":esc(h.status)}</span>${h.notes?`<div class="hint notes">Reviewer notes: ${esc(h.notes)}</div>`:""}${h.error?`<div class="hint">${esc(h.error)}</div>`:""}${h.location?`<div class="hint">${esc(h.location)}</div>`:""}</td><td>${h.youtube_id?`<a target="_blank" href="https://studio.youtube.com/video/${esc(h.youtube_id)}/edit">studio</a> <a target="_blank" href="https://youtube.com/shorts/${esc(h.youtube_id)}">open</a>`:""}</td></tr>`).join("")
|
||||
}catch(e){}}
|
||||
let since=0;
|
||||
@@ -197,9 +237,25 @@ $("#del-profile").onclick=async()=>{
|
||||
const sel=$("#prof-set");const label=sel.options[sel.selectedIndex]?.text||"";
|
||||
if(!confirm(`Delete profile "${label}" and its linked channel token? Videos on disk are kept.`))return;
|
||||
try{await api("/api/profiles/delete",{method:"POST",body:JSON.stringify({id:sel.value})});await loadSettings();refresh()}catch(e){alert(e.message)}};
|
||||
$("#go").onclick=async()=>{await api("/api/start",{method:"POST"});refresh()};
|
||||
$("#stop").onclick=async()=>{await api("/api/stop",{method:"POST"});refresh()};
|
||||
$("#unlink").onclick=async()=>{if(confirm("Unlink the YouTube channel from this profile?")){await api("/auth/unlink",{method:"POST"});await loadSettings();refresh()}};
|
||||
function setButton(running){
|
||||
const b=$("#go");
|
||||
if(b.dataset.busy==="1")return;
|
||||
b.classList.toggle("running",running);
|
||||
b.textContent=running?"STOP":"GO";
|
||||
b.disabled=false}
|
||||
$("#go").onclick=async()=>{
|
||||
const b=$("#go");const running=b.classList.contains("running");
|
||||
b.dataset.busy="1";b.disabled=true;b.classList.remove("running");b.classList.add("busy");
|
||||
b.textContent=running?"Stopping...":"Starting...";
|
||||
try{await api(running?"/api/stop":"/api/start",{method:"POST"})}catch(e){alert(e.message)}
|
||||
b.dataset.busy="";b.classList.remove("busy");
|
||||
setButton(!running);
|
||||
refresh()};
|
||||
$("#link").onclick=async()=>{
|
||||
if($("#link").dataset.linked==="1"){
|
||||
if(!confirm("Unlink the YouTube channel from this profile?"))return;
|
||||
await api("/auth/unlink",{method:"POST"});await loadSettings();refresh()}
|
||||
else{window.location.href="/auth/start"}};
|
||||
$("#save").onclick=saveSettings;
|
||||
$("#clear").onclick=()=>{$("#log").innerHTML=""};
|
||||
$("#preview").onclick=async()=>{
|
||||
|
||||
Reference in New Issue
Block a user