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124 lines
4.7 KiB
Python
124 lines
4.7 KiB
Python
"""Animation block – Manim-rendered math animation.
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Calls :class:`deeptutor.agents.math_animator.pipeline.MathAnimatorPipeline`
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to generate a video clip explaining the chapter. The payload exposes the
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rendered artifact URL(s) plus a short summary.
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This block requires the optional ``math-animator`` extras (LaTeX, ffmpeg,
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manim, …). When those packages are missing the generator raises
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:class:`GenerationFailure` with a clear install hint.
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"""
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from __future__ import annotations
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import importlib.util
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import logging
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from typing import Any
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from ..models import BlockType, SourceAnchor
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from .base import BlockContext, BlockGenerator, GenerationFailure
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logger = logging.getLogger(__name__)
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class AnimationGenerator(BlockGenerator):
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block_type = BlockType.ANIMATION
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async def _generate(
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self, ctx: BlockContext
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) -> tuple[dict[str, Any], list[SourceAnchor], dict[str, Any]]:
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if importlib.util.find_spec("manim") is None:
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raise GenerationFailure(
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"AnimationGenerator requires the optional math-animator extras. "
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"Install with `pip install -e '.[math-animator]'` "
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"or `pip install -r requirements/math-animator.txt`."
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)
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params = ctx.block.params
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chapter_title = params.get("chapter_title", ctx.chapter.title)
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chapter_summary = params.get("chapter_summary", ctx.chapter.summary)
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objectives = params.get("objectives") or ctx.chapter.learning_objectives
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focus = str(params.get("focus") or "")
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quality = str(params.get("quality") or "medium")
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style_hint = str(params.get("style_hint") or "")
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history_lines: list[str] = []
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if chapter_summary:
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history_lines.append(f"Chapter summary: {chapter_summary}")
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if objectives:
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history_lines.append("Learning objectives:")
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for obj in objectives:
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history_lines.append(f"- {obj}")
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history_context = "\n".join(history_lines)
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focus_clause = f" focusing on {focus}" if focus else ""
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user_input = (
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f"Create a short Manim animation that walks through the core "
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f'derivation of "{chapter_title}"{focus_clause}. Aim for a '
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"clear, step-by-step explanation a learner can follow."
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)
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try:
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from deeptutor.agents.math_animator.pipeline import MathAnimatorPipeline
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from deeptutor.agents.math_animator.request_config import (
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MathAnimatorRequestConfig,
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)
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from deeptutor.services.llm.config import get_llm_config
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llm_config = get_llm_config()
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request_config = MathAnimatorRequestConfig(
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output_mode="video",
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quality=quality if quality in ("low", "medium", "high") else "medium",
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style_hint=style_hint,
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)
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pipeline = MathAnimatorPipeline(
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api_key=llm_config.api_key,
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base_url=llm_config.base_url,
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api_version=llm_config.api_version,
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language=ctx.language,
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)
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turn_id = f"book-{ctx.book_id}-{ctx.block.id}"
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result = await pipeline.run(
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turn_id=turn_id,
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user_input=user_input,
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history_context=history_context,
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request_config=request_config,
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attachments=[],
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)
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except Exception as exc:
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logger.warning(f"AnimationGenerator failed: {exc}", exc_info=True)
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raise GenerationFailure(f"animation generation failed: {exc}") from exc
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render_result = result["render_result"]
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summary_payload = result["summary"]
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analysis = result["analysis"]
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artifacts = [artifact.model_dump() for artifact in render_result.artifacts]
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primary = next(
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(
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a
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for a in artifacts
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if a.get("type") == "video" or "video" in (a.get("content_type") or "")
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),
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artifacts[0] if artifacts else None,
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)
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return (
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{
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"render_type": "video",
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"artifacts": artifacts,
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"video_url": (primary or {}).get("url", ""),
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"filename": (primary or {}).get("filename", ""),
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"summary": getattr(summary_payload, "summary_text", "") or "",
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"key_points": list(getattr(summary_payload, "key_points", []) or []),
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"description": getattr(analysis, "learning_goal", "") or "",
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},
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[],
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{
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"retry_attempts": render_result.retry_attempts,
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"quality": request_config.quality,
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},
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)
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__all__ = ["AnimationGenerator"]
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