File size: 11,554 Bytes
fe7106b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | from __future__ import annotations
from pathlib import Path
from typing import Any
from renderer.core.config import Settings
from renderer.core.models import TaskResult
from renderer.core.utils import now, safe_filename, write_json
from renderer.studio.capabilities import (
AI_ASSISTANTS,
AI_EDITING_FEATURES,
IMAGE_GENERATION_PROVIDERS,
MUSIC_PROVIDERS,
VIDEO_GENERATION_PROVIDERS,
VOICE_PROVIDERS,
)
class StudioTaskProcessor:
"""Manifest-producing async handlers for optional AI providers and studio automation."""
def __init__(self, settings: Settings | None = None, log=None) -> None:
self.settings = settings or Settings()
self.settings.ensure_dirs()
self._logs: list[str] = []
self._log = log
def caption_generate(self, payload: dict[str, Any], job_id: str) -> TaskResult:
text = str(payload.get("text") or payload.get("transcript") or "")
captions = payload.get("events") if isinstance(payload.get("events"), list) else _captions_from_text(text)
manifest = {
"type": "caption_generation",
"status": "ready",
"engine": payload.get("engine", "whisper"),
"media": payload.get("media") or payload.get("audio"),
"template": payload.get("template", "capcut"),
"language": payload.get("language"),
"features": {
"word_timestamps": bool(payload.get("word_timestamps", True)),
"sentence_timestamps": True,
"emoji_insertion": bool(payload.get("emoji_insertion", False)),
"speaker_detection": bool(payload.get("speaker_detection", False)),
"karaoke": bool(payload.get("karaoke", True)),
"animated": bool(payload.get("animated", True)),
},
"captions": captions,
}
output = self._json_artifact(job_id, "captions", manifest)
return self._result(output, {"task": "caption_generate", "caption_count": len(captions)})
def music_generate(self, payload: dict[str, Any], job_id: str) -> TaskResult:
provider = _provider(payload.get("provider"), MUSIC_PROVIDERS, "musicgen")
prompt = str(payload.get("prompt") or payload.get("style") or "background music")
duration = float(payload.get("duration", 30))
manifest = {
"type": "music_generation",
"status": "provider_required",
"provider": provider,
"prompt": prompt,
"style": payload.get("style", "background_music"),
"duration": duration,
"bpm": payload.get("bpm"),
"license": payload.get("license", "user_configured"),
"next_step": "Configure provider credentials or connect this manifest to a local MusicGen runner.",
}
output = self._json_artifact(job_id, "music_request", manifest)
return self._result(output, {"task": "music_generate", "provider": provider, "duration": duration})
def voice_generate(self, payload: dict[str, Any], job_id: str) -> TaskResult:
provider = _provider(payload.get("provider"), VOICE_PROVIDERS, "kokoro")
text = str(payload.get("text") or "")
manifest = {
"type": "voice_generation",
"status": "provider_required",
"provider": provider,
"text": text,
"voice": payload.get("voice", "default"),
"emotion": payload.get("emotion"),
"speed": float(payload.get("speed", 1.0)),
"pitch": float(payload.get("pitch", 1.0)),
"clone_reference": payload.get("clone_reference"),
"multi_speaker": payload.get("speakers", []),
"next_step": "Configure the selected TTS backend to render audio for this manifest.",
}
output = self._json_artifact(job_id, "voice_request", manifest)
return self._result(output, {"task": "voice_generate", "provider": provider, "characters": len(text)})
def image_generate(self, payload: dict[str, Any], job_id: str) -> TaskResult:
provider = _provider(payload.get("provider"), IMAGE_GENERATION_PROVIDERS, "flux")
manifest = {
"type": "image_generation",
"status": "provider_required",
"provider": provider,
"prompt": payload.get("prompt", ""),
"negative_prompt": payload.get("negative_prompt", ""),
"mode": payload.get("mode", "text_to_image"),
"control_image": payload.get("control_image"),
"source_image": payload.get("source_image"),
"size": payload.get("size", "1024x1024"),
"features": {
"background_replacement": bool(payload.get("background_replacement", False)),
"object_removal": bool(payload.get("object_removal", False)),
"upscaling": bool(payload.get("upscaling", False)),
},
}
output = self._json_artifact(job_id, "image_request", manifest)
return self._result(output, {"task": "image_generate", "provider": provider})
def video_generate(self, payload: dict[str, Any], job_id: str) -> TaskResult:
provider = _provider(payload.get("provider"), VIDEO_GENERATION_PROVIDERS, "ltx_video")
manifest = {
"type": "video_generation",
"status": "provider_required",
"provider": provider,
"prompt": payload.get("prompt", ""),
"mode": payload.get("mode", "text_to_video"),
"image": payload.get("image"),
"duration": float(payload.get("duration", 5)),
"fps": int(payload.get("fps", 24)),
"size": payload.get("size", "1280x720"),
"next_step": "Connect Wan, LTX Video, Hunyuan Video, or Veo credentials/runtime to execute this request.",
}
output = self._json_artifact(job_id, "video_request", manifest)
return self._result(output, {"task": "video_generate", "provider": provider})
def ai_tool(self, tool: str, payload: dict[str, Any], job_id: str) -> TaskResult:
tool = _canonical(tool)
if tool not in AI_EDITING_FEATURES:
raise ValueError(f"Unsupported AI editing tool: {tool}")
manifest = {
"type": "ai_editing",
"tool": tool,
"status": "ready",
"media": payload.get("media"),
"project_id": payload.get("project_id"),
"platform": payload.get("platform", "tiktok"),
"result": _ai_result(tool, payload),
"created_at": now(),
}
output = self._json_artifact(job_id, tool, manifest)
return self._result(output, {"task": tool})
def assistant_tool(self, tool: str, payload: dict[str, Any], job_id: str) -> TaskResult:
tool = _canonical(tool)
if tool not in AI_ASSISTANTS:
raise ValueError(f"Unsupported assistant: {tool}")
text = str(payload.get("topic") or payload.get("transcript") or payload.get("prompt") or "")
manifest = {
"type": "assistant",
"tool": tool,
"status": "ready",
"input": text,
"platform": payload.get("platform", "general"),
"result": _assistant_result(tool, text, payload),
"created_at": now(),
}
output = self._json_artifact(job_id, tool, manifest)
return self._result(output, {"task": tool, "characters": len(text)})
def _json_artifact(self, job_id: str, name: str, payload: dict[str, Any]) -> Path:
output = self.settings.exports_dir / f"{job_id}_{safe_filename(name)}.json"
write_json(output, payload)
self._message(f"Wrote {name} manifest")
return output
def _result(self, output: Path, metrics: dict[str, Any]) -> TaskResult:
return TaskResult(output_path=output, commands=[], metrics=metrics, logs=list(self._logs))
def _message(self, message: str) -> None:
self._logs.append(message)
if self._log:
self._log(message)
def _provider(value: Any, supported: list[str], default: str) -> str:
provider = _canonical(str(value or default))
return provider if provider in supported else default
def _canonical(value: str) -> str:
return value.strip().lower().replace("-", "_").replace(" ", "_")
def _captions_from_text(text: str) -> list[dict[str, Any]]:
if not text:
return []
words = text.split()
chunks: list[list[str]] = []
while words:
chunks.append(words[:8])
words = words[8:]
captions = []
cursor = 0.0
for chunk in chunks:
duration = max(1.2, len(chunk) * 0.34)
captions.append({"start": round(cursor, 2), "end": round(cursor + duration, 2), "text": " ".join(chunk)})
cursor += duration
return captions
def _ai_result(tool: str, payload: dict[str, Any]) -> dict[str, Any]:
platform = str(payload.get("platform") or "tiktok")
if tool == "auto_highlight_detection":
return {"highlights": [{"start": 0, "end": 8, "reason": "opening hook"}]}
if tool == "auto_scene_detection":
return {"scenes": [{"start": 0, "end": 5, "label": "intro"}, {"start": 5, "end": 12, "label": "body"}]}
if tool in {"auto_reframe", "auto_crop", "auto_platform_optimization"}:
return {"platform": platform, "safe_zone": "vertical_center", "aspect_ratio": "9:16"}
if tool == "auto_viral_score":
return {"score": 74, "signals": ["short duration", "caption-ready", platform]}
if tool == "auto_hook_detection":
return {"hook": str(payload.get("transcript") or payload.get("text") or "")[:120], "score": 68}
if tool == "auto_thumbnail_selection":
return {"frames": [{"timestamp": 2.0, "score": 82}, {"timestamp": 6.5, "score": 75}]}
return {"plan": f"{tool} plan generated", "confidence": "heuristic", "platform": platform}
def _assistant_result(tool: str, text: str, payload: dict[str, Any]) -> dict[str, Any]:
subject = text.strip() or "your video"
short = " ".join(subject.split()[:12])
if tool == "script_writer":
return {"script": f"Hook: {short}\nValue: show the clearest proof.\nCTA: invite viewers to take the next step."}
if tool == "hook_generator":
return {"hooks": [f"Stop scrolling if you care about {short}", f"Nobody tells you this about {short}"]}
if tool == "title_generator":
return {"titles": [short.title(), f"How {short.title()} Changes Everything"]}
if tool == "description_generator":
return {"description": f"{subject}\n\nBuilt with Ava2lon Studio AI."}
if tool == "hashtag_generator":
tags = [word.strip(".,!?").lower() for word in subject.split() if len(word.strip(".,!?")) > 3]
return {"hashtags": ["#" + tag for tag in tags[:8]] or ["#video", "#creator"]}
if tool == "storyboard_generator":
return {"beats": [{"scene": 1, "goal": "hook"}, {"scene": 2, "goal": "proof"}, {"scene": 3, "goal": "CTA"}]}
if tool == "b_roll_planner":
return {"shots": [{"type": "close_up", "description": short}, {"type": "screen_recording", "description": "show the result"}]}
if tool == "thumbnail_prompt_generator":
return {"prompt": f"High contrast thumbnail for {short}, expressive face, bold text, clean background"}
if tool == "seo_optimizer":
return {"keywords": [word.strip(".,!?").lower() for word in subject.split()[:10]], "score": 72}
return {"result": subject, "options": payload}
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