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Complete working pipeline from Fountain script to validated scene JSON: - Schemas (Pydantic): all 7 layers defined upfront - Fountain parser + normalizer (Layer 1) - AI scene extractor with prompt contracts (Layer 2) - Schema validator + scene-specific semantic validator - Structured JSON logging per layer/scene execution - Versioned output writer (never overwrites) - Retry engine with 4-level failure escalation - Stop condition evaluator (per-unit + global halts) - Diff/drift detector for re-run comparison - CLI entry point with --dry-run, --scene, --test, --force - 3 test scripts (dialogue-heavy, action-heavy, nonstandard) - Expected output files for regression testing Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
"""Scene-specific validation — post-schema semantic checks for Layer 2 output."""
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from dataclasses import dataclass
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from typing import Literal
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from difflib import SequenceMatcher
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from src.schemas.scene_array import SceneArray
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@dataclass
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class ValidationWarning:
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type: Literal[
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"duplicate_scene_number",
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"similar_character_names",
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"scene_count_deviation",
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"empty_characters",
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"unknown_values",
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]
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scene_number: int | None
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message: str
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def validate_scenes(scene_array: SceneArray, heading_count: int) -> list[ValidationWarning]:
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"""Run semantic validation checks on extracted scenes.
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Args:
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scene_array: Validated SceneArray from Layer 2.
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heading_count: Number of scene_heading elements from Layer 1 output.
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Returns:
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List of warnings. Empty list means all checks passed.
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"""
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warnings: list[ValidationWarning] = []
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# 1. Duplicate scene numbers (already caught by SceneArray validator, but double-check)
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seen_numbers: dict[int, int] = {}
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for scene in scene_array.scenes:
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if scene.scene_number in seen_numbers:
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warnings.append(ValidationWarning(
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type="duplicate_scene_number",
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scene_number=scene.scene_number,
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message=f"Scene number {scene.scene_number} appears more than once",
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))
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seen_numbers[scene.scene_number] = seen_numbers.get(scene.scene_number, 0) + 1
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# 2. Similar character names (possible duplicates)
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all_characters: set[str] = set()
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for scene in scene_array.scenes:
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all_characters.update(scene.characters_present)
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char_list = sorted(all_characters)
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for i, name_a in enumerate(char_list):
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for name_b in char_list[i + 1:]:
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ratio = SequenceMatcher(None, name_a.upper(), name_b.upper()).ratio()
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if ratio > 0.8 and name_a != name_b:
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warnings.append(ValidationWarning(
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type="similar_character_names",
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scene_number=None,
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message=f"Possible duplicate characters: '{name_a}' and '{name_b}' (similarity: {ratio:.0%})",
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))
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# 3. Scene count deviation
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extracted_count = len(scene_array.scenes)
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if heading_count > 0:
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deviation = abs(extracted_count - heading_count) / heading_count
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if deviation > 0.20:
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warnings.append(ValidationWarning(
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type="scene_count_deviation",
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scene_number=None,
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message=f"Extracted {extracted_count} scenes but Layer 1 found {heading_count} scene headings (deviation: {deviation:.0%})",
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))
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# 4. Empty characters_present
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for scene in scene_array.scenes:
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if not scene.characters_present:
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warnings.append(ValidationWarning(
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type="empty_characters",
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scene_number=scene.scene_number,
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message=f"Scene {scene.scene_number} has no characters listed",
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))
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# 5. UNKNOWN values
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for scene in scene_array.scenes:
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scene_dict = scene.model_dump()
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for key, value in scene_dict.items():
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if value == "UNKNOWN" or (isinstance(value, list) and "UNKNOWN" in value):
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warnings.append(ValidationWarning(
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type="unknown_values",
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scene_number=scene.scene_number,
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message=f"Scene {scene.scene_number} field '{key}' contains UNKNOWN",
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))
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return warnings
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