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122 lines
4.6 KiB
Python
122 lines
4.6 KiB
Python
"""Figure block – static visual figure (svg / chartjs / mermaid).
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Wraps :class:`deeptutor.agents.visualize.pipeline.VisualizePipeline` with
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``render_mode="figure"`` so the LLM picks the best static rendering for the
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chapter, but never falls back to interactive HTML (handled by the
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``interactive`` block type).
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Like the chat capability, the draft is checked by the deterministic local
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``validate_visualization``; only on failure do we spend one targeted repair
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call. If the code still fails after repair we raise ``GenerationFailure`` so
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the book engine can retry, instead of baking a broken figure into the book.
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"""
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from __future__ import annotations
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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 FigureGenerator(BlockGenerator):
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block_type = BlockType.FIGURE
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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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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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variant = str(params.get("variant") or "diagram")
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focus = str(params.get("focus") 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 {variant} figure for the chapter "
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f'"{chapter_title}"{focus_clause}. The figure should help a '
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"learner build intuition about the core relationships covered above."
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)
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try:
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from deeptutor.agents.visualize.models import ReviewResult
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from deeptutor.agents.visualize.pipeline import VisualizePipeline
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from deeptutor.agents.visualize.utils import validate_visualization
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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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pipeline = VisualizePipeline(
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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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analysis = await pipeline.run_analysis(
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user_input=user_input,
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history_context=history_context,
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render_mode="figure",
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)
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code = await pipeline.run_code_generation(
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user_input=user_input,
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history_context=history_context,
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analysis=analysis,
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)
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ok, validation_error = validate_visualization(code, analysis.render_type)
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if ok:
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review = ReviewResult(
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optimized_code=code,
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changed=False,
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review_notes="Passed local validation.",
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)
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else:
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review = await pipeline.run_repair(
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user_input=user_input,
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analysis=analysis,
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code=code,
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error=validation_error,
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)
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except Exception as exc:
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logger.warning(f"FigureGenerator failed: {exc}", exc_info=True)
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raise GenerationFailure(f"figure generation failed: {exc}") from exc
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final_code = review.optimized_code or code
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render_type = analysis.render_type
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final_ok, residual_error = validate_visualization(final_code, render_type)
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if not final_ok:
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raise GenerationFailure(f"figure failed validation after repair: {residual_error}")
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lang_tag = {
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"svg": "svg",
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"mermaid": "mermaid",
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"chartjs": "javascript",
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}.get(render_type, "svg")
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return (
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{
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"render_type": render_type,
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"code": {"language": lang_tag, "content": final_code},
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"description": analysis.description,
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"chart_type": analysis.chart_type,
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},
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[],
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{
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"review_changed": review.changed,
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"review_notes": review.review_notes,
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},
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)
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__all__ = ["FigureGenerator"]
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