项目重命名: ReelForge => Pixelle-Video
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puke
179
pixelle_video/services/narration_generator.py
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179
pixelle_video/services/narration_generator.py
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"""
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Narration generation service
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Supports two content sources:
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1. Topic: Generate narrations from a topic/theme
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2. Content: Extract/refine narrations from user-provided content
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"""
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import json
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import re
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from typing import List, Optional, Literal
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from loguru import logger
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from pixelle_video.models.storyboard import StoryboardConfig, ContentMetadata
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from pixelle_video.prompts import (
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build_topic_narration_prompt,
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build_content_narration_prompt,
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)
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class NarrationGeneratorService:
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"""Narration generation service"""
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def __init__(self, pixelle_video_core):
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"""
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Initialize
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Args:
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pixelle_video_core: PixelleVideoCore instance (for calling llm)
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"""
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self.core = pixelle_video_core
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async def generate_narrations(
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self,
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config: StoryboardConfig,
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source_type: Literal["topic", "content"],
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content_metadata: Optional[ContentMetadata] = None,
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topic: Optional[str] = None,
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content: Optional[str] = None,
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) -> List[str]:
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"""
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Generate storyboard narrations from different sources
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Args:
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config: Storyboard configuration
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source_type: Type of content source ("topic" or "content")
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content_metadata: Content metadata (optional, not currently used)
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topic: Topic/theme (required if source_type="topic")
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content: User-provided content (required if source_type="content")
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Returns:
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List of narration texts
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Raises:
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ValueError: If parameters don't match source_type or narration count mismatch
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json.JSONDecodeError: If unable to parse LLM response as JSON
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Examples:
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# Generate from topic
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>>> narrations = await service.generate_narrations(
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... config=config,
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... source_type="topic",
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... topic="如何提高学习效率"
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... )
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# Generate from user content
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>>> narrations = await service.generate_narrations(
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... config=config,
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... source_type="content",
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... content="Today I want to share three useful tips..."
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... )
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"""
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# 1. Build prompt based on source_type
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if source_type == "topic":
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if topic is None:
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raise ValueError("topic is required when source_type='topic'")
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logger.info(f"Generating topic narrations for: {topic}")
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prompt = build_topic_narration_prompt(
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topic=topic,
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n_storyboard=config.n_storyboard,
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min_words=config.min_narration_words,
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max_words=config.max_narration_words
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)
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else: # content
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if content is None:
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raise ValueError("content is required when source_type='content'")
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logger.info(f"Generating narrations from user content ({len(content)} chars)")
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prompt = build_content_narration_prompt(
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content=content,
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n_storyboard=config.n_storyboard,
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min_words=config.min_narration_words,
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max_words=config.max_narration_words
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)
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# 2. Call LLM (using self.core.llm)
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response = await self.core.llm(
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prompt=prompt,
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temperature=0.8, # Higher temperature for more creativity
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max_tokens=2000
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)
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logger.debug(f"LLM response: {response[:200]}...")
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# 3. Parse JSON
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try:
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result = self._parse_json(response)
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narrations = result["narrations"]
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except json.JSONDecodeError as e:
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logger.error(f"Failed to parse LLM response as JSON: {e}")
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logger.error(f"Response: {response}")
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raise
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except KeyError:
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logger.error("Response JSON missing 'narrations' key")
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logger.error(f"Response: {response}")
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raise ValueError("Invalid response format")
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# 4. Validate count (take first N if got more)
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if len(narrations) > config.n_storyboard:
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logger.warning(
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f"Got {len(narrations)} narrations, taking first {config.n_storyboard}"
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)
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narrations = narrations[:config.n_storyboard]
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elif len(narrations) < config.n_storyboard:
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raise ValueError(
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f"Expected at least {config.n_storyboard} narrations, "
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f"got only {len(narrations)}"
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)
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# 5. Validate word count for each narration
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for i, text in enumerate(narrations):
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word_count = len(text)
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if word_count < config.min_narration_words:
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logger.warning(
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f"Narration {i} too short: {word_count} chars "
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f"(min: {config.min_narration_words})"
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)
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logger.info(f"Generated {len(narrations)} narrations successfully")
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return narrations
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def _parse_json(self, text: str) -> dict:
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"""
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Parse JSON from text, with fallback to extract JSON from markdown code blocks
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Args:
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text: Text containing JSON
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Returns:
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Parsed JSON dict
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"""
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# Try direct parsing first
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try:
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return json.loads(text)
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except json.JSONDecodeError:
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pass
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# Try to extract JSON from markdown code block
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json_pattern = r'```(?:json)?\s*([\s\S]+?)\s*```'
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match = re.search(json_pattern, text, re.DOTALL)
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if match:
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try:
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return json.loads(match.group(1))
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except json.JSONDecodeError:
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pass
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# Try to find any JSON object in the text
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json_pattern = r'\{[^{}]*"narrations"\s*:\s*\[[^\]]*\][^{}]*\}'
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match = re.search(json_pattern, text, re.DOTALL)
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if match:
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try:
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return json.loads(match.group(0))
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except json.JSONDecodeError:
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pass
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# If all fails, raise error
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raise json.JSONDecodeError("No valid JSON found", text, 0)
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