File size: 3,768 Bytes
cac0037 621cf5d cac0037 a07daae cac0037 41b3e98 621cf5d a07daae 621cf5d a07daae cac0037 a07daae cac0037 | 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 | """High-level command-center orchestration."""
from __future__ import annotations
from copy import deepcopy
from uuid import uuid4
from .catalog import ADAPTER_CATALOG, active_stack
from .grounding import inspect_outfit
from .lore import build_lore_beats, build_video_plan
from .schema import CreativeRequest, GenerationRun, HumanCheckpoint
from .taste import refine_prompt
from .wardrobe import build_outfit_graph
def build_command_center_run(
prompt: str,
mode: str = "Strict",
video_preset: str = "Wan2.2 I2V",
adult_mode: bool = False,
creator_controls: dict | None = None,
reference_metadata: list[dict] | None = None,
) -> GenerationRun:
"""
Assemble a complete generation run by processing a creative prompt through refinement, outfit generation, inspection, and planning stages, then recommend approval or revision via a human checkpoint.
Parameters:
prompt: The creative prompt to generate from.
mode: Operation mode (e.g., "Strict", "Frontier"); determines checkpoint requirements.
video_preset: Video generation preset identifier.
adult_mode: Whether to enable adult content handling.
creator_controls: Optional dictionary of creator-specified controls (e.g., wardrobe preferences).
reference_metadata: Optional list of reference metadata dictionaries with id, basename, or url_hash keys.
Returns:
A GenerationRun containing the refined prompt, generated outfit, inspection results, model stack, adapters, video plan, lore components, and a human checkpoint with approval recommendation based on a computed trust score.
"""
controls = deepcopy(creator_controls or {})
references = deepcopy(reference_metadata or [])
reference_ids: list[str] = []
for item in references:
if not isinstance(item, dict):
continue
value = item.get("id") or item.get("basename") or item.get("url_hash")
if value is not None and str(value):
reference_ids.append(str(value))
request = CreativeRequest(
prompt=prompt,
adult_mode=adult_mode,
references=reference_ids,
creator_controls=controls,
reference_metadata=references,
)
refined = refine_prompt(prompt, adult_mode=adult_mode)
outfit = build_outfit_graph(refined.refined, adult_mode=adult_mode, controls=controls.get("wardrobe") if isinstance(controls, dict) else None)
inspection = inspect_outfit(outfit)
lore = build_lore_beats(refined.refined)
video = build_video_plan(video_preset)
stack = active_stack(adult_mode)
adapters = [adapter for adapter in ADAPTER_CATALOG if adult_mode or not adapter.adult_only][:4]
trust_score = round((refined.score + outfit.score + (0.86 if inspection.status == "pass" else 0.72)) / 3, 2)
required_actions = []
if adult_mode:
required_actions.append("Confirm 18+ session scope and keep exports partitioned")
if inspection.drift_flags:
required_actions.extend(inspection.drift_flags)
if mode.lower().startswith("frontier"):
required_actions.append("Frontier mode requires human checkpoint before video render")
checkpoint = HumanCheckpoint(
checkpoint_id=f"nw-{uuid4().hex[:8]}",
recommendation="approve_candidate" if trust_score >= 0.78 else "revise_before_generation",
trust_score=trust_score,
required_actions=required_actions or ["Review candidate thumbnails before promotion"],
)
return GenerationRun(
request=request,
refined_prompt=refined,
outfit=outfit,
model_stack=stack,
adapters=adapters,
inspection=inspection,
lore=lore,
video=video,
checkpoint=checkpoint,
)
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