小鱼开发
bb08d0f586
主要变更： - 修复 /tasks/script 路由 404（去掉重复 prefix） - 开发模式自动认证兜底（无需登录即可测试流程） - Docker 基础设施独立化（共用 db/redis） - 前端 API 端口改为 8081 - 新增 TTS/语音克隆、视频粗剪、音频混音等智剪功能 - 删除智影专属模块（avatar、model_usage、qiniu 上传等）
681 lines
24 KiB
Python
681 lines
24 KiB
Python
"""
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脚本生成服务
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============
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"""
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import asyncio
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import logging
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import math
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import re
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import time
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from collections.abc import AsyncIterator
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from pathlib import Path
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from app.ai.model_router import get_model_router
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from app.ai.prompts import load_script_system, load_script_user_prompt, load_topic_prompt, TOPIC_PROMPT_MAP
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from app.schemas.script import ScriptGenerationEvent, ScriptShot
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from app.services.ai_response_utils import (
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safe_parse_ai_json_response,
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validate_and_normalize_shots,
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)
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from app.services.anytocopy_service import (
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AnyToCopyService,
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get_anytocopy_service,
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)
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logger = logging.getLogger(__name__)
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class ScriptService:
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"""脚本生成服务"""
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# 根据视频时长估算输出字符数（经验值）
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# 格式: {时长: (最小字符数, 最大字符数)}
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DURATION_ESTIMATES = {
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30: (800, 1200),
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45: (1200, 1600),
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60: (1500, 2000),
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90: (2000, 2800),
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}
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def __init__(self):
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self.prompts_dir = Path(__file__).parent.parent / "ai" / "prompts"
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def _estimate_total_chars(self, duration: int) -> int:
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"""
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根据时长估算总输出字符数
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Args:
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duration: 视频时长（秒）
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Returns:
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预估字符数
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"""
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# 找到最接近的预设
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closest_duration = min(self.DURATION_ESTIMATES.keys(), key=lambda x: abs(x - duration))
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min_chars, max_chars = self.DURATION_ESTIMATES[closest_duration]
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# 根据实际时长在区间内插值
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ratio = duration / closest_duration
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estimated = int(min_chars + (max_chars - min_chars) * ratio)
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logger.debug(f"时长 {duration}s 预估字符数: {estimated}")
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return estimated
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def _calculate_progress(
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self,
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current_chars: int,
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estimated_total: int,
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elapsed_time: float,
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min_expected_time: float = 5.0,
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) -> int:
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"""
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计算平滑进度（使用对数曲线）- 优化版，避免抖动
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设计思路：
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- 主要基于内容生成进度，时间只作为保底
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- 使用单调递增函数，确保进度只增不减
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- 前 20% 内容：进度到 30%（慢启动）
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- 中间 60% 内容：进度到 75%（稳定生成期）
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- 最后 20% 内容：进度到 85%（收尾阶段）
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Args:
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current_chars: 当前字符数
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estimated_total: 预估总字符数
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elapsed_time: 已过去时间（秒）
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min_expected_time: 最少预期的生成时间（避免太快跑完）
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Returns:
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进度百分比 (0-85)
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"""
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# 基于内容的进度（对数曲线）
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ratio = min(current_chars / estimated_total, 1.5) # 允许超出生成 50%
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# 对数曲线：前期慢，后期快
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if ratio <= 1.0:
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# 未完成或刚好完成：使用调整后的对数曲线，最高到 85%
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progress_ratio = math.log(1 + ratio * 2) / math.log(3) * 0.85
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else:
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# 已超出生成：从 85% 线性增长，最多到 95%（预留空间给后续阶段）
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progress_ratio = 0.85 + min((ratio - 1) * 0.2, 0.1)
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# 基于时间的保底进度（只在内容很少时生效，避免生成太快时进度条没动）
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# 使用平滑的保底函数，只在前期（前3秒）和内容很少时生效
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time_progress = 0
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if current_chars < estimated_total * 0.3 and elapsed_time < min_expected_time:
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# 内容生成少于30%且时间少于5秒时，提供保底进度
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time_progress = min(elapsed_time / min_expected_time * 0.15, 0.15)
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# 取较大值，但主要依赖 progress_ratio，time_progress 只作为早期保底
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final_ratio = max(progress_ratio, time_progress)
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# 生成阶段最高到 85%
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return min(int(final_ratio * 100), 85)
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def _load_prompt(self, name: str) -> str:
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"""加载 Prompt 模板"""
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prompt_file = self.prompts_dir / f"{name}.txt"
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if prompt_file.exists():
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return prompt_file.read_text(encoding="utf-8")
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return ""
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@staticmethod
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def _extract_json(content: str) -> str:
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"""
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从 Markdown 代码块中提取 JSON，或返回原始内容
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支持格式：
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- ```json {...} ```
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- ``` {...} ```
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- 纯 JSON 文本
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"""
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if not content:
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return ""
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content = content.strip()
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# 匹配 ```json ... ``` 或 ``` ... ```
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pattern = r"```(?:json)?\s*([\s\S]*?)\s*```"
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matches = re.findall(pattern, content)
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if matches:
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# 取最后一个匹配（避免前面有示例代码）
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return matches[-1].strip()
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# 如果没有代码块，返回原始内容
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return content
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async def generate_script(
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self,
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topic: str,
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duration: int,
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script_type: str,
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model: str | None = None,
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) -> list[ScriptShot]:
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"""
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同步生成脚本
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Args:
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topic: 创作主题（预设主题名或自定义输入/视频链接）
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duration: 视频时长（秒）
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script_type: 脚本类型
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model: 指定模型
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Returns:
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分镜列表
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"""
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# 1. 判断是否为预设主题
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is_preset_topic = topic in TOPIC_PROMPT_MAP
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# 2. 根据类型决定处理方式
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actual_topic = topic
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if not is_preset_topic:
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# 非预设主题：检测并提取视频链接中的文案
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anytocopy = get_anytocopy_service()
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extract_result = await anytocopy.extract_text_from_input(topic)
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if extract_result["error"]:
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logger.warning(f"视频文案提取失败: {extract_result['error']}")
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# 提取失败但不中断，使用原始输入
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if extract_result["is_video_url"]:
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logger.info(f"检测到视频链接，提取文案长度: {len(extract_result['extracted_text'])}")
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# 使用提取的文案作为创作主题
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actual_topic = extract_result["extracted_text"] or topic
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# 3. 获取 model_router
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model_router = await get_model_router()
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# 4. 加载 Prompt
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# 系统提示词：预设主题用专用提示词，否则用通用提示词
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system_prompt = load_topic_prompt(topic) if is_preset_topic else load_script_system()
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# 用户提示词
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user_prompt = load_script_user_prompt(
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topic=topic,
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duration=duration,
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)
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logger.info(f"同步生成脚本: topic={topic}, is_preset={is_preset_topic}, duration={duration}")
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logger.info(f"同步生成脚本: topic={topic[:20]}, duration={duration}")
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# 调用 AI 生成
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result = await model_router.generate(
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prompt=user_prompt,
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system_prompt=system_prompt,
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model_id=model,
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task_type="script",
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temperature=0.7,
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)
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# 检查返回内容
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if not result.content or not result.content.strip():
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logger.error("AI 返回内容为空")
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raise ValueError("AI 返回内容为空，请检查模型配置或重试")
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logger.info(f"AI 返回内容长度: {len(result.content)} 字符")
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# 使用安全的 JSON 解析
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success, parsed_data, error_msg = safe_parse_ai_json_response(result.content)
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if not success:
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logger.error(f"JSON 解析失败: {error_msg}")
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logger.error(f"原始内容: {result.content[:500]!r}")
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raise ValueError(error_msg or "AI 返回格式错误，无法解析为 JSON")
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# 验证并标准化分镜数据
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try:
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shots_data = validate_and_normalize_shots(parsed_data)
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if not shots_data:
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raise ValueError("AI 返回的分镜数据为空或格式不正确")
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# 转换为 ScriptShot 对象
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shots = [ScriptShot(**shot) for shot in shots_data]
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logger.info(f"成功解析 {len(shots)} 个分镜")
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return shots
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except Exception as e:
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logger.error(f"分镜数据标准化失败: {e}")
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raise ValueError(f"分镜数据处理失败: {str(e)}")
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async def generate_script_stream(
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self,
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topic: str,
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duration: int,
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script_type: str,
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model: str | None = None,
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) -> AsyncIterator[ScriptGenerationEvent]:
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"""
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流式生成脚本（SSE）- 优化版
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支持预设主题和视频链接自动提取文案。
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进度设计：
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- 0-5%: start（初始化）
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- 5-15%: analyzing（分析主题，含视频文案提取）
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- 15-85%: generating（AI 生成，平滑对数曲线增长）
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- 85-92%: validating（JSON 验证）
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- 92-98%: parsing（解析分镜）
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- 98-100%: complete（完成）
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"""
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model_router = await get_model_router()
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start_time = time.time()
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# 1. 判断是否为预设主题
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is_preset_topic = topic in TOPIC_PROMPT_MAP
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# 2. 非预设主题时，检测并提取视频链接中的文案
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original_topic = topic
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anytocopy = get_anytocopy_service()
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extracted_info = None # 保存提取的视频信息
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actual_topic = topic
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# 检查是否为视频链接（非预设主题才检测）
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if not is_preset_topic and (
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AnyToCopyService.is_video_url(topic) or AnyToCopyService.extract_url_from_text(topic)
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):
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yield ScriptGenerationEvent(
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type="analyzing",
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progress=5,
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message="检测到视频链接，正在提取文案...",
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)
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extract_result = await anytocopy.extract_text_from_input(topic)
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if extract_result["error"]:
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logger.warning(f"视频文案提取失败: {extract_result['error']}")
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yield ScriptGenerationEvent(
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type="analyzing",
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progress=8,
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message="视频文案提取失败，使用原始输入继续生成...",
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)
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elif extract_result["is_video_url"]:
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extracted_text = extract_result["extracted_text"]
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logger.info(f"视频文案提取成功，长度: {len(extracted_text)}")
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topic = extracted_text or topic
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# 保存提取的视频信息（只要有 video_info 就返回）
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video_info = extract_result.get("video_info")
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if video_info:
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extracted_info = {
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"title": video_info.title,
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"content": video_info.content,
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"text_content": video_info.text_content,
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"platform": video_info.platform,
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"duration": video_info.duration,
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"original_url": original_topic,
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}
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yield ScriptGenerationEvent(
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type="analyzing",
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progress=10,
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message=f"视频文案提取成功，共 {len(extracted_text)} 字符",
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)
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try:
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# 加载 Prompt
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# 系统提示词：预设主题用专用提示词，否则用通用提示词
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system_prompt = load_topic_prompt(topic) if is_preset_topic else load_script_system()
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# 用户提示词
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user_prompt = load_script_user_prompt(
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topic=topic,
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duration=duration,
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)
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logger.info(f"流式生成脚本: topic={topic}, is_preset={is_preset_topic}, duration={duration}")
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# 1. 开始阶段（0-5%）
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yield ScriptGenerationEvent(
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type="start",
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progress=2,
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message="准备生成脚本...",
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)
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# 2. 分析阶段（5-15%）
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yield ScriptGenerationEvent(
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type="analyzing",
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progress=10,
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message="分析创作要点",
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)
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# 估算总长度（根据时长）
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estimated_total = self._estimate_total_chars(duration)
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# 3. 生成阶段（15-55%）- 降低占比，给后续步骤留更多空间
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yield ScriptGenerationEvent(
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type="generating",
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progress=15,
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message="正在创作脚本...",
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)
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full_content = ""
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last_progress = 15
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last_update_time = start_time
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update_interval = 0.5 # 最少 500ms 更新一次
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chunk_count = 0
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logger.info(f"开始流式生成: topic={topic[:20]}, duration={duration}")
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async for chunk in model_router.generate_stream_with_progress(
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prompt=user_prompt,
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system_prompt=system_prompt,
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model_id=model,
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task_type="script",
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temperature=0.7,
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):
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chunk_count += 1
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if chunk["type"] == "chunk":
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chunk_content = chunk.get("content", "")
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if not chunk_content:
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logger.warning(f"收到空 chunk，序号: {chunk_count}")
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continue
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full_content += chunk_content
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current_chars = len(full_content)
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elapsed = time.time() - start_time
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# 计算平滑进度（对数曲线，最高到55%）
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base_progress = self._calculate_progress(
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current_chars=current_chars,
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estimated_total=estimated_total,
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elapsed_time=elapsed,
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)
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# 将原来的 15-85 映射到 15-55
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progress = 15 + int((base_progress - 15) * 40 / 70)
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# 限制更新频率，但确保每次有变化都上报（最小 2% 变化）
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current_time = time.time()
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if progress > last_progress and (
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progress - last_progress >= 2
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or current_time - last_update_time >= update_interval
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):
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yield ScriptGenerationEvent(
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type="generating",
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progress=progress,
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message="正在创作脚本...",
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)
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last_progress = progress
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last_update_time = current_time
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elif chunk["type"] == "usage":
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prompt_tokens = chunk.get("prompt_tokens", 0)
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completion_tokens = chunk.get("completion_tokens", 0)
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logger.info(
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f"Token 使用: prompt={prompt_tokens}, completion={completion_tokens}"
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)
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logger.info(f"流式生成结束: 共 {chunk_count} 个 chunk, {len(full_content)} 字符")
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# 4. 验证阶段（55-70%）
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actual_chars = len(full_content)
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logger.info(f"生成完成: {actual_chars} 字符 (预估: {estimated_total})")
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yield ScriptGenerationEvent(
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type="validating",
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progress=60,
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message="验证脚本格式...",
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)
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await asyncio.sleep(0.5)
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yield ScriptGenerationEvent(
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type="validating",
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progress=65,
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message="检查数据完整性...",
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)
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await asyncio.sleep(0.5)
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yield ScriptGenerationEvent(
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type="validating",
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progress=70,
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message="验证通过",
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)
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await asyncio.sleep(0.5)
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# 5. 解析阶段（70-80%）
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yield ScriptGenerationEvent(
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type="parsing",
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progress=75,
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message="解析分镜内容...",
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)
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# 检查内容是否为空
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if not full_content or not full_content.strip():
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logger.error("AI 返回内容为空")
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yield ScriptGenerationEvent(
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type="error",
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progress=0,
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message="AI 返回内容为空，请检查模型配置或重试",
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)
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return
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# 记录原始内容（调试用）
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logger.info(f"AI 原始输出: {full_content[:500]}...")
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# 使用安全的 JSON 解析
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success, parsed_data, error_msg = safe_parse_ai_json_response(full_content)
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if not success:
|
||
logger.error(f"JSON 解析失败: {error_msg}")
|
||
logger.error(f"原始内容前500字符: {full_content[:500]!r}")
|
||
|
||
# 给前端更详细的错误信息
|
||
error_detail = error_msg or "无法解析 AI 返回的内容"
|
||
if not full_content or not full_content.strip():
|
||
error_detail = "AI 返回内容为空，请检查模型配置或重试"
|
||
|
||
yield ScriptGenerationEvent(
|
||
type="error",
|
||
progress=0,
|
||
message=f"脚本解析失败: {error_detail}",
|
||
)
|
||
return
|
||
|
||
# 验证并标准化分镜数据
|
||
try:
|
||
shots_data = validate_and_normalize_shots(parsed_data)
|
||
|
||
if not shots_data:
|
||
logger.error("标准化后分镜列表为空")
|
||
yield ScriptGenerationEvent(
|
||
type="error",
|
||
progress=0,
|
||
message="AI 返回的分镜数据为空或格式不正确",
|
||
)
|
||
return
|
||
|
||
# 转换为 ScriptShot 对象
|
||
shots = [ScriptShot(**shot) for shot in shots_data]
|
||
|
||
# 6. 完成阶段（80-100%）- 细分为多个步骤，让用户感知进度
|
||
yield ScriptGenerationEvent(
|
||
type="finalizing",
|
||
progress=80,
|
||
message=f"整理 {len(shots)} 个分镜...",
|
||
)
|
||
|
||
await asyncio.sleep(0.5)
|
||
|
||
yield ScriptGenerationEvent(
|
||
type="finalizing",
|
||
progress=85,
|
||
message="优化镜头顺序...",
|
||
)
|
||
|
||
await asyncio.sleep(0.5)
|
||
|
||
yield ScriptGenerationEvent(
|
||
type="finalizing",
|
||
progress=90,
|
||
message="检查时长分配...",
|
||
)
|
||
|
||
await asyncio.sleep(0.5)
|
||
|
||
yield ScriptGenerationEvent(
|
||
type="finalizing",
|
||
progress=95,
|
||
message="准备完成...",
|
||
)
|
||
|
||
await asyncio.sleep(0.5)
|
||
|
||
yield ScriptGenerationEvent(
|
||
type="complete",
|
||
progress=100,
|
||
message=f"成功生成 {len(shots)} 个分镜",
|
||
result=shots,
|
||
extracted_info=extracted_info,
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"分镜数据标准化失败: {e}")
|
||
yield ScriptGenerationEvent(
|
||
type="error",
|
||
progress=0,
|
||
message=f"分镜数据处理失败: {str(e)}",
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.exception("脚本生成失败")
|
||
yield ScriptGenerationEvent(
|
||
type="error",
|
||
progress=0,
|
||
message=f"生成失败: {str(e)}",
|
||
)
|
||
|
||
async def polish_content(
|
||
self,
|
||
content: str,
|
||
polish_type: str = "voiceover",
|
||
shot_type: str = "segment",
|
||
) -> str:
|
||
"""
|
||
润色内容
|
||
|
||
Args:
|
||
content: 待润色内容
|
||
polish_type: 润色类型，可选 "scene"（画面描述）或 "voiceover"（配音文案）
|
||
shot_type: 镜头类型，可选 "segment"（分镜）或 "empty_shot"（空镜），仅用于画面润色
|
||
|
||
Returns:
|
||
润色后的内容
|
||
"""
|
||
# 获取 model_router
|
||
model_router = await get_model_router()
|
||
|
||
# 从文件加载提示词模板
|
||
if polish_type == "scene":
|
||
# 画面润色需要根据镜头类型选择不同提示词
|
||
if shot_type == "empty_shot":
|
||
prompt_template = self._load_prompt("polish/scene_empty_shot")
|
||
else:
|
||
prompt_template = self._load_prompt("polish/scene_segment")
|
||
|
||
# 如果特定类型的提示词不存在，回退到通用 scene 提示词
|
||
if not prompt_template:
|
||
prompt_template = self._load_prompt("polish/scene")
|
||
else:
|
||
# 配音文案润色
|
||
prompt_template = self._load_prompt("polish/voiceover")
|
||
|
||
if not prompt_template:
|
||
# 最终回退
|
||
prompt_template = "请润色以下内容：\n\n{content}"
|
||
|
||
prompt = prompt_template.format(content=content)
|
||
|
||
result = await model_router.generate(
|
||
prompt=prompt,
|
||
task_type="polish",
|
||
temperature=0.5,
|
||
max_tokens=300,
|
||
)
|
||
|
||
return result.content.strip()
|
||
|
||
async def check_model_health(self) -> dict:
|
||
"""检查模型健康状态"""
|
||
model_router = await get_model_router()
|
||
health_results = await model_router.health_check()
|
||
|
||
models = []
|
||
available_count = 0
|
||
recommended = None
|
||
|
||
for provider_id, health in health_results.items():
|
||
model_info = {
|
||
"id": health.id,
|
||
"name": health.name,
|
||
"is_available": health.is_available,
|
||
"response_time": health.response_time,
|
||
"last_error": health.last_error,
|
||
}
|
||
models.append(model_info)
|
||
|
||
if health.is_available:
|
||
available_count += 1
|
||
if recommended is None or health.response_time < recommended.get(
|
||
"response_time", float("inf")
|
||
):
|
||
recommended = model_info
|
||
|
||
total = len(models)
|
||
|
||
return {
|
||
"status": "healthy" if available_count > 0 else "unhealthy",
|
||
"models": models,
|
||
"recommended_model": recommended,
|
||
"total_models": total,
|
||
"available_models": available_count,
|
||
}
|
||
|
||
async def test_model(self, model_id: str | None = None) -> dict:
|
||
"""测试指定模型连接"""
|
||
model_router = await get_model_router()
|
||
|
||
import time
|
||
|
||
start_time = time.time()
|
||
|
||
try:
|
||
result = await model_router.generate(
|
||
prompt="你好",
|
||
model_id=model_id,
|
||
max_tokens=5,
|
||
)
|
||
|
||
response_time = (time.time() - start_time) * 1000
|
||
|
||
return {
|
||
"success": True,
|
||
"model": result.model,
|
||
"response_time": round(response_time, 2),
|
||
"checked_at": time.strftime("%Y-%m-%dT%H:%M:%S"),
|
||
}
|
||
|
||
except Exception as e:
|
||
return {
|
||
"success": False,
|
||
"model": model_id or "default",
|
||
"error": str(e),
|
||
"checked_at": time.strftime("%Y-%m-%dT%H:%M:%S"),
|
||
}
|
||
|
||
|
||
# 全局单例
|
||
_script_service: ScriptService | None = None
|
||
|
||
|
||
def get_script_service() -> ScriptService:
|
||
"""获取 ScriptService 单例"""
|
||
global _script_service
|
||
if _script_service is None:
|
||
_script_service = ScriptService()
|
||
return _script_service
|