755 lines
30 KiB
Python
755 lines
30 KiB
Python
from __future__ import annotations
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import json
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import re
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from dataclasses import dataclass
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from types import SimpleNamespace
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from typing import Any, Literal
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import httpx
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.config import settings
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from app.models.model_config import ModelConfig
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from app.services.upload_video_asset_service import resolve_upload_video_path
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from app.services.resource_signed_url_service import build_resource_signed_url
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from app.enums.common import LogEventStatusEnum, LogSourceEnum
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from app.enums.shot_replicate import ModuleCodeEnum, ShotReplicateLogEventEnum, ShotReplicateRemoteActionEnum
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from app.services.operation_log_service import log_ai_model_event
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from app.utils.id_gen import generate_id
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AnalysisMode = Literal["full_breakdown", "summary_only"]
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@dataclass(slots=True)
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class ShotVideoAnalysisResult:
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result: dict[str, Any]
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raw_response: dict[str, Any]
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usage: dict[str, Any]
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def _timeout_seconds() -> int:
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return int(getattr(settings, "SHOT_ANALYSIS_TIMEOUT_SECONDS", 3600) or 3600)
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def _video_fps() -> float:
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return float(getattr(settings, "SHOT_ANALYSIS_VIDEO_FPS", 1.0) or 1.0)
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def _split_min_seconds() -> float:
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return float(getattr(settings, "SHOT_SPLIT_MIN_SECONDS", 1) or 1)
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def _split_max_seconds() -> float:
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return float(getattr(settings, "SHOT_SPLIT_MAX_SECONDS", 120) or 120)
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def _resolve_local_file_path(file_url: str) -> str:
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if file_url.startswith("/uploads/") or file_url.startswith("uploads/"):
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return str(resolve_upload_video_path(file_url))
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return file_url
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def build_file_url_or_data_uri(file_url: str, fallback_mime: str = "video/mp4") -> str:
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if file_url.startswith(("http://", "https://", "data:")):
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return file_url
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file_url_sign = build_resource_signed_url(resource_url=file_url, expire_seconds=86400)
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return f"{settings.BASE_URL}{file_url_sign}"
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# file_path = _resolve_local_file_path(file_url)
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# path = Path(file_path)
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# if not path.exists():
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# raise FileNotFoundError(f"视频文件不存在: {file_path}")
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#
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# max_mb = float(getattr(settings, "SHOT_ANALYSIS_MAX_LOCAL_VIDEO_MB", 45) or 45)
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# size_mb = path.stat().st_size / 1024 / 1024
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# if size_mb > max_mb:
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# raise ValueError(f"本地视频文件过大: {size_mb:.2f} MB，当前限制 {max_mb:g} MB")
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#
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# mime = mimetypes.guess_type(str(path))[0] or fallback_mime
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# with open(path, "rb") as f:
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# b64 = base64.b64encode(f.read()).decode("utf-8")
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# return f"data:{mime};base64,{b64}"
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async def build_user_message(user_text: str, video_url: str, db=None) -> tuple[dict[str, Any], dict[str, Any], str]:
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from app.utils.media import media_to_base64, get_llm_media_as_base64
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if await get_llm_media_as_base64(db):
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real_url = await media_to_base64(video_url, "video/mp4")
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else:
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real_url = build_file_url_or_data_uri(video_url)
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content_parts = [
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{
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"type": "video_url",
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"video_url": {
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"url": real_url,
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},
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},
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{"type": "text", "text": user_text},
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]
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log_content_parts = [
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{
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"type": "video_url",
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"video_url": {
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"url": video_url,
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},
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},
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{"type": "text", "text": user_text},
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]
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return {"role": "user", "content": content_parts}, {"role": "user", "content": log_content_parts}, real_url
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def build_video_analysis_system_prompt(*, mode: AnalysisMode) -> str:
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if mode == "summary_only":
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return """
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你是专业的短视频内容分析师、广告素材拆解师。
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你的任务：
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1. 根据用户提供的视频附件，分析这个视频片段的整体内容。
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2. 判断视频分类。
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3. 判断视频可能面向的受众人群。
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4. 必须输出严格 JSON 对象。
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5. 不输出 Markdown。
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6. 不输出解释文字。
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7. 不返回 null，未知内容填“无”。
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顶级字段只能包含：
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- 原视频内容
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- 原视频分类
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- 原视频受众人群
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- 拆镜内容剖析
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summary_only 模式下“拆镜内容剖析”必须返回空数组。
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安全规则：
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1. 不要识别视频中人物身份。
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2. 不要猜测真实姓名、联系方式、账号身份。
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3. 如果视频是游戏录屏，只分析画面内容、玩法内容、玩家情绪表达、受众，不要编造不存在的剧情。
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4. 如果视频包含广告内容，可以分析广告品类、目标用户、转化意图，但不要编造品牌信息。
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""".strip()
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return f"""
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你是专业的短视频内容分析师、广告素材拆解师、视频分镜分析师。
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你的任务：
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1. 根据用户提供的视频附件，分析原视频整体内容。
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2. 判断原视频分类。
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3. 判断原视频可能面向的受众人群。
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4. 对视频进行拆镜内容剖析。
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5. 必须输出严格 JSON 对象。
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6. 不输出 Markdown。
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7. 不输出解释文字。
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8. 不返回 null，未知内容填“无”。
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顶级字段只能包含：
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- 原视频内容
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- 原视频分类
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- 原视频受众人群
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- 拆镜内容剖析
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拆镜内容剖析必须是数组。
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每个拆镜片段必须包含：
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- 拆镜开始秒
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- 拆镜结束秒
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- 拆镜时间节点
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- 对应时间节点内的内容
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- 分类
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- 受众人群
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拆镜时间规则：
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1. 拆镜开始秒必须是数字，例如 0、15、26。
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2. 拆镜结束秒必须是数字，例如 15、26、31。
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3. 拆镜时间节点必须由拆镜开始秒和拆镜结束秒组成，例如“0-15秒”。
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4. 禁止输出“-15秒”这种缺少开始秒的时间节点。
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5. 禁止输出“15-秒”这种缺少结束秒的时间节点。
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6. 每个拆镜片段时长不能低于 {_split_min_seconds():g} 秒。
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7. 每个拆镜片段时长不能高于 {_split_max_seconds():g} 秒。
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8. 如果某段内容不足 {_split_min_seconds():g} 秒，不要单独拆出来。
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9. 如果单个连续内容超过 {_split_max_seconds():g} 秒，需要按语义变化继续拆分。
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10. 如果没有明显镜头变化、场景变化、人物动作变化、剧情变化、字幕重点变化或语义变化，不要强行剖析。
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11. 如果无法可靠拆镜，则“拆镜内容剖析”返回空数组。
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12. 拆镜时间必须从 0 秒或视频中实际可识别的开始时间开始，不允许出现负数。
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13. 拆镜结束秒必须大于拆镜开始秒。
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14. 拆镜片段必须按时间顺序排列。
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安全规则：
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1. 不要识别视频中人物身份。
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2. 不要猜测真实姓名、联系方式、账号身份。
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3. 如果视频是游戏录屏，只分析画面内容、玩法内容、玩家情绪表达、受众，不要编造不存在的剧情。
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4. 如果视频包含广告内容，可以分析广告品类、目标用户、转化意图，但不要编造品牌信息。
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""".strip()
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def build_video_analysis_user_text(*, mode: AnalysisMode) -> str:
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if mode == "summary_only":
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payload = {
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"任务": "请根据上传的视频片段附件，返回这个视频片段的内容分析 JSON。",
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"输出JSON格式": {
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"原视频内容": "概括这个视频片段整体内容，描述主要画面、主体、场景、动作、剧情或信息点",
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"原视频分类": "判断视频类型，例如：游戏视频、产品广告视频、剧情视频、口播讲解视频、教程视频、生活记录视频等",
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"原视频受众人群": "判断该片段更适合的人群",
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"拆镜内容剖析": [],
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},
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"返回要求": ["只返回 JSON 对象", "不要返回 Markdown", "不要返回解释文字", "不要返回代码块", "不要返回 null，未知填无"],
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}
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return json.dumps(payload, ensure_ascii=False)
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payload = {
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"任务": "请根据上传的视频附件，返回原视频内容分析和拆镜内容剖析 JSON。",
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"输出JSON格式": {
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"原视频内容": "概括原视频整体内容，描述主要画面、人物/主体、场景、动作、剧情或信息点",
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"原视频分类": "判断视频类型，例如：游戏视频、产品广告视频、剧情视频、口播讲解视频、教程视频、生活记录视频、直播切片视频、图文快闪视频等",
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"原视频受众人群": "判断该视频更适合的人群，例如：游戏玩家、年轻娱乐用户、潜在购买用户、同城社交用户等",
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"拆镜内容剖析": [
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{
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"拆镜开始秒": 0,
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"拆镜结束秒": 15,
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"拆镜时间节点": "0-15秒",
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"对应时间节点内的内容": "描述这个时间片段内发生了什么",
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"分类": "判断这个片段的内容分类，例如：开场吸引、冲突铺垫、玩法展示、卖点展示、情绪爆发、行动引导、结果展示等",
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"受众人群": "判断这个片段主要吸引的人群",
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}
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],
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},
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"拆镜规则": [
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f"每个拆镜片段时长必须大于等于 {_split_min_seconds():g} 秒",
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f"每个拆镜片段时长必须小于等于 {_split_max_seconds():g} 秒",
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"拆镜开始秒必须是数字",
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"拆镜结束秒必须是数字",
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"拆镜开始秒不能是负数",
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"拆镜结束秒必须大于拆镜开始秒",
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"拆镜时间节点必须等于：拆镜开始秒-拆镜结束秒秒",
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"禁止输出“-15秒”",
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"禁止输出“15-秒”",
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"如果无法判断拆镜节点，拆镜内容剖析返回空数组",
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],
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"返回要求": ["只返回 JSON 对象", "不要返回 Markdown", "不要返回解释文字", "不要返回代码块", "不要返回 null，未知填无"],
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}
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return json.dumps(payload, ensure_ascii=False)
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def strip_json_code_fence(text: str) -> str:
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text = text.strip()
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if text.startswith("```"):
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text = re.sub(r"^```(?:json)?", "", text, flags=re.IGNORECASE).strip()
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text = re.sub(r"```$", "", text).strip()
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return text
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def parse_model_json(content: str) -> dict[str, Any]:
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cleaned = strip_json_code_fence(content)
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data = json.loads(cleaned)
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if not isinstance(data, dict):
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raise ValueError(f"模型返回 JSON 不是对象类型: {type(data).__name__}")
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return data
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def get_message_content_or_raise(data: dict[str, Any]) -> str:
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choices = data.get("choices") or []
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if not choices:
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raise RuntimeError(f"模型响应没有 choices: {json.dumps(data, ensure_ascii=False)}")
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choice = choices[0]
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finish_reason = choice.get("finish_reason")
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if finish_reason == "length":
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usage = data.get("usage", {})
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raise RuntimeError(f"模型输出被长度限制截断，finish_reason={finish_reason}, usage={json.dumps(usage, ensure_ascii=False)}")
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message = choice.get("message") or {}
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content = message.get("content", "")
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if not content:
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raise RuntimeError(f"模型响应 content 为空: {json.dumps(data, ensure_ascii=False)}")
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return content.strip()
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def fill_none_with_wu(value: Any) -> Any:
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if value is None:
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return "无"
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if isinstance(value, str):
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return value if value.strip() else "无"
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if isinstance(value, list):
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return [fill_none_with_wu(item) for item in value]
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if isinstance(value, dict):
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return {k: fill_none_with_wu(v) for k, v in value.items()}
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return value
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def first_present(data: dict[str, Any], keys: list[str]) -> Any:
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for key in keys:
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if key in data:
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return data.get(key)
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return None
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def parse_number(value: Any) -> float | None:
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if isinstance(value, (int, float)):
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return float(value)
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text = str(value or "").strip()
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if not text:
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return None
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m = re.search(r"-?\d+(?:\.\d+)?", text)
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return float(m.group(0)) if m else None
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def parse_time_value_to_seconds(value: str) -> float | None:
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value = str(value or "").strip()
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if not value:
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return None
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m = re.match(r"^(\d+(?:\.\d+)?)\s*(?:秒|s)?$", value, flags=re.IGNORECASE)
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if m:
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return float(m.group(1))
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parts = value.split(":")
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if len(parts) in (2, 3) and all(re.match(r"^\d+(?:\.\d+)?$", p.strip()) for p in parts):
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nums = [float(p.strip()) for p in parts]
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if len(nums) == 2:
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minute, second = nums
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return minute * 60 + second
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hour, minute, second = nums
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return hour * 3600 + minute * 60 + second
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return None
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def parse_time_node_to_range(time_node: str) -> tuple[float, float] | None:
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text = str(time_node or "").strip()
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if not text:
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return None
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text = text.replace("—", "-").replace("–", "-").replace("－", "-")
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text = text.replace("到", "-").replace("至", "-").replace("~", "-").replace("～", "-")
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text = text.replace("第", "").replace("时间段", "").replace("：", ":")
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m = re.search(r"(\d{1,2}:\d{1,2}(?::\d{1,2})?)\s*-\s*(\d{1,2}:\d{1,2}(?::\d{1,2})?)", text)
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if m:
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start = parse_time_value_to_seconds(m.group(1))
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end = parse_time_value_to_seconds(m.group(2))
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if start is not None and end is not None and end > start:
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return start, end
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m = re.search(r"(\d+(?:\.\d+)?)\s*(?:秒|s)?\s*-\s*(\d+(?:\.\d+)?)\s*(?:秒|s)?", text, flags=re.IGNORECASE)
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if m:
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start = float(m.group(1))
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end = float(m.group(2))
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if end > start:
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return start, end
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m = re.search(r"^\s*-\s*(\d+(?:\.\d+)?)\s*(?:秒|s)?\s*$", text, flags=re.IGNORECASE)
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if m:
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end = float(m.group(1))
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if end > 0:
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return 0.0, end
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return None
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def format_second(value: float) -> int | float:
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checked = float(value)
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return int(checked) if checked.is_integer() else round(checked, 2)
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def normalize_time_node_by_range(start: float, end: float) -> str:
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return f"{format_second(start)}-{format_second(end)}秒"
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def ensure_result_schema(result: dict[str, Any]) -> dict[str, Any]:
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final_result = {
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"原视频内容": result.get("原视频内容", "无"),
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"原视频分类": result.get("原视频分类", "无"),
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"原视频受众人群": result.get("原视频受众人群", "无"),
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"拆镜内容剖析": result.get("拆镜内容剖析", []),
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}
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for key in ("原视频内容", "原视频分类", "原视频受众人群"):
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if not isinstance(final_result[key], str):
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final_result[key] = json.dumps(final_result[key], ensure_ascii=False)
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if not isinstance(final_result["拆镜内容剖析"], list):
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final_result["拆镜内容剖析"] = []
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return final_result
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def filter_and_normalize_breakdown(result: dict[str, Any], *, mode: AnalysisMode = "full_breakdown") -> dict[str, Any]:
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if mode == "summary_only":
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result["拆镜内容剖析"] = []
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return result
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||
breakdown = result.get("拆镜内容剖析")
|
||
if not isinstance(breakdown, list):
|
||
result["拆镜内容剖析"] = []
|
||
return result
|
||
|
||
normalized_items: list[dict[str, Any]] = []
|
||
for item in breakdown:
|
||
if not isinstance(item, dict):
|
||
continue
|
||
raw_start = first_present(item, ["拆镜开始秒", "开始秒", "起始秒", "开始时间", "起始时间", "start", "start_second", "start_seconds"])
|
||
raw_end = first_present(item, ["拆镜结束秒", "结束秒", "结束时间", "end", "end_second", "end_seconds"])
|
||
start = parse_number(raw_start)
|
||
end = parse_number(raw_end)
|
||
time_node = str(first_present(item, ["拆镜时间节点", "时间节点", "时间段", "镜头时间", "time_node", "time_range"]) or "").strip()
|
||
if start is None or end is None:
|
||
parsed_range = parse_time_node_to_range(time_node)
|
||
if parsed_range is None:
|
||
continue
|
||
start, end = parsed_range
|
||
if start is None or end is None or start < 0 or end < 0 or end <= start:
|
||
continue
|
||
duration = end - start
|
||
if duration < _split_min_seconds() or duration > _split_max_seconds():
|
||
continue
|
||
content = first_present(item, ["对应时间节点内的内容", "内容", "画面内容", "片段内容", "镜头内容", "content"]) or "无"
|
||
category = first_present(item, ["分类", "片段分类", "内容分类", "镜头分类", "category"]) or "无"
|
||
audience = first_present(item, ["受众人群", "目标受众", "片段受众", "镜头受众", "audience"]) or "无"
|
||
normalized_items.append({
|
||
"拆镜开始秒": format_second(start),
|
||
"拆镜结束秒": format_second(end),
|
||
"拆镜时间节点": normalize_time_node_by_range(start, end),
|
||
"对应时间节点内的内容": str(content or "无"),
|
||
"分类": str(category or "无"),
|
||
"受众人群": str(audience or "无"),
|
||
})
|
||
normalized_items.sort(key=lambda x: float(x.get("拆镜开始秒", 0)))
|
||
result["拆镜内容剖析"] = normalized_items
|
||
return result
|
||
|
||
|
||
async def _select_model_config(db: AsyncSession) -> ModelConfig | None:
|
||
result = await db.execute(
|
||
select(ModelConfig)
|
||
.where(ModelConfig.is_active == True, ModelConfig.deleted_at.is_(None))
|
||
.order_by(ModelConfig.priority.desc())
|
||
.limit(1)
|
||
)
|
||
return result.scalar_one_or_none()
|
||
|
||
|
||
def _int_usage(value: Any) -> int:
|
||
try:
|
||
return int(value or 0)
|
||
except Exception:
|
||
return 0
|
||
|
||
|
||
|
||
def _safe_response_json(response: httpx.Response | None) -> Any:
|
||
if response is None:
|
||
return None
|
||
try:
|
||
return response.json()
|
||
except Exception:
|
||
return {"raw_text": response.text}
|
||
|
||
|
||
def _extract_remote_error(data: Any) -> tuple[str | None, str | None, str | None, str | None]:
|
||
"""Return remote_request_id, remote_code, remote_message, remote_param."""
|
||
if isinstance(data, dict):
|
||
err = data.get("error") if isinstance(data.get("error"), dict) else data
|
||
remote_code = err.get("code") or err.get("type") if isinstance(err, dict) else None
|
||
remote_message = err.get("message") if isinstance(err, dict) else None
|
||
remote_param = err.get("param") if isinstance(err, dict) else None
|
||
remote_request_id = err.get("request_id") or data.get("request_id") if isinstance(err, dict) else data.get("request_id")
|
||
if not remote_request_id and isinstance(remote_message, str):
|
||
match = re.search(r"Request id:\s*([a-zA-Z0-9_.:-]+)", remote_message, flags=re.IGNORECASE)
|
||
if match:
|
||
remote_request_id = match.group(1)
|
||
return (
|
||
str(remote_request_id) if remote_request_id else None,
|
||
str(remote_code) if remote_code else None,
|
||
str(remote_message) if remote_message else None,
|
||
str(remote_param) if remote_param else None,
|
||
)
|
||
return None, None, str(data) if data is not None else None, None
|
||
|
||
|
||
def _log_shot_ai_model_event(
|
||
*,
|
||
event_type: str,
|
||
event_status: str,
|
||
config: ModelConfig,
|
||
trace_id: str,
|
||
user_id: str | None,
|
||
task_set_id: str | None,
|
||
segment_id: str | None,
|
||
mode: AnalysisMode,
|
||
request_data: dict[str, Any] | None = None,
|
||
response_data: Any = None,
|
||
token_usage: dict[str, Any] | None = None,
|
||
http_status: int | None = None,
|
||
remote_request_id: str | None = None,
|
||
remote_code: str | None = None,
|
||
remote_message: str | None = None,
|
||
remote_param: str | None = None,
|
||
message: str | None = None,
|
||
error: str | None = None,
|
||
extra_detail: dict[str, Any] | None = None,
|
||
) -> None:
|
||
action = (
|
||
ShotReplicateRemoteActionEnum.ANALYZE_ORIGINAL_VIDEO.value
|
||
if mode == "full_breakdown"
|
||
else ShotReplicateRemoteActionEnum.ANALYZE_CUSTOM_SEGMENT_VIDEO.value
|
||
)
|
||
detail = dict(extra_detail or {})
|
||
detail.update({
|
||
"analysis_mode": mode,
|
||
"remote_code": remote_code,
|
||
"remote_message": remote_message,
|
||
"remote_param": remote_param,
|
||
})
|
||
log_ai_model_event(
|
||
event_type=event_type,
|
||
event_status=event_status,
|
||
source=LogSourceEnum.REMOTE_API.value,
|
||
module=ModuleCodeEnum.SHOT_REPLICATE.value,
|
||
trace_id=trace_id,
|
||
user_id=user_id,
|
||
project_id=task_set_id,
|
||
step_id=segment_id,
|
||
remote_action=action,
|
||
remote_request_id=remote_request_id,
|
||
model_config_id=str(config.id),
|
||
model_config_name=config.name,
|
||
model_name=config.model_name,
|
||
provider=config.provider,
|
||
api_base=config.api_base,
|
||
http_status=http_status,
|
||
request=request_data,
|
||
response=response_data,
|
||
token_usage=token_usage,
|
||
message=message,
|
||
detail=detail,
|
||
error=error,
|
||
)
|
||
|
||
async def analyze_video_for_shot_split(
|
||
db: AsyncSession,
|
||
video_url: str,
|
||
*,
|
||
user_id: str | None = None,
|
||
mode: AnalysisMode = "full_breakdown",
|
||
task_set_id: str | None = None,
|
||
segment_id: str | None = None,
|
||
trace_id: str | None = None,
|
||
) -> ShotVideoAnalysisResult:
|
||
"""调用模型完成拆镜/片段分析。
|
||
|
||
模型配置统一从 model_configs 表选择当前启用且 priority 最高的配置；
|
||
不再读取 SHOT_ANALYSIS_API_BASE / SHOT_ANALYSIS_API_KEY / SHOT_ANALYSIS_MODEL_NAME，
|
||
也不再 fallback 到 SEEDANCE_*，避免拆镜分析走错通道。
|
||
"""
|
||
trace_id = trace_id or generate_id()
|
||
config_row = await _select_model_config(db)
|
||
if not config_row:
|
||
raise RuntimeError("拆镜分析模型未配置：请先在 model_configs 表启用可用模型")
|
||
# 外部调用前转换为纯数据快照，随后释放数据库事务，避免一小时 HTTP 请求期间 idle in transaction。
|
||
config = SimpleNamespace(
|
||
id=str(config_row.id),
|
||
name=str(config_row.name or ""),
|
||
provider=str(config_row.provider or ""),
|
||
api_base=str(config_row.api_base or ""),
|
||
api_key=str(config_row.api_key or ""),
|
||
model_name=str(config_row.model_name or ""),
|
||
max_tokens=getattr(config_row, "max_tokens", None),
|
||
temperature=getattr(config_row, "temperature", None),
|
||
)
|
||
if not str(config.api_key or "").strip():
|
||
raise RuntimeError(f"拆镜分析模型 API Key 为空: model_config_id={config.id}")
|
||
if not str(config.api_base or "").strip():
|
||
raise RuntimeError(f"拆镜分析模型 API Base 为空: model_config_id={config.id}")
|
||
if not str(config.model_name or "").strip():
|
||
raise RuntimeError(f"拆镜分析模型名称为空: model_config_id={config.id}")
|
||
|
||
system_prompt = build_video_analysis_system_prompt(mode=mode)
|
||
user_text = build_video_analysis_user_text(mode=mode)
|
||
user_message, log_user_message, real_video_url = await build_user_message(user_text, video_url, db)
|
||
|
||
request_data: dict[str, Any] = {
|
||
"model": config.model_name,
|
||
"messages": [
|
||
{"role": "system", "content": system_prompt},
|
||
user_message,
|
||
],
|
||
"max_tokens": int(getattr(settings, "SHOT_ANALYSIS_MAX_TOKENS", 5000) or getattr(config, "max_tokens", 5000) or 5000),
|
||
"temperature": float(getattr(settings, "SHOT_ANALYSIS_TEMPERATURE", 0.1) or getattr(config, "temperature", 0.1) or 0.1),
|
||
"response_format": {"type": "json_object"},
|
||
}
|
||
log_request_data: dict[str, Any] = {
|
||
**request_data,
|
||
"messages": [
|
||
{"role": "system", "content": system_prompt},
|
||
log_user_message,
|
||
],
|
||
"model_config_id": config.id,
|
||
"model_config_name": config.name,
|
||
"provider": config.provider,
|
||
"analysis_mode": mode,
|
||
"video_url": video_url,
|
||
"signed_video_url": real_video_url,
|
||
"video_fps": _video_fps(),
|
||
"timeout_seconds": _timeout_seconds(),
|
||
}
|
||
|
||
url = f"{str(config.api_base).rstrip('/')}/chat/completions"
|
||
# 释放模型配置和媒体开关查询产生的事务；HTTP 调用期间不占用数据库连接。
|
||
await db.rollback()
|
||
|
||
_log_shot_ai_model_event(
|
||
event_type=(
|
||
ShotReplicateLogEventEnum.ANALYSIS_REMOTE_API_STARTED.value
|
||
if mode == "full_breakdown"
|
||
else ShotReplicateLogEventEnum.SEGMENT_ANALYSIS_REMOTE_API_STARTED.value
|
||
),
|
||
event_status=LogEventStatusEnum.STARTED.value,
|
||
config=config,
|
||
trace_id=trace_id,
|
||
user_id=user_id,
|
||
task_set_id=task_set_id,
|
||
segment_id=segment_id,
|
||
mode=mode,
|
||
request_data={**log_request_data, "api_url": url},
|
||
message="拆镜视频分析模型请求开始",
|
||
)
|
||
|
||
try:
|
||
async with httpx.AsyncClient(timeout=_timeout_seconds()) as client:
|
||
response = await client.post(
|
||
url,
|
||
headers={"Authorization": f"Bearer {config.api_key}", "Content-Type": "application/json"},
|
||
json=request_data,
|
||
)
|
||
except Exception as exc:
|
||
_log_shot_ai_model_event(
|
||
event_type=(
|
||
ShotReplicateLogEventEnum.ANALYSIS_REMOTE_API_FAILED.value
|
||
if mode == "full_breakdown"
|
||
else ShotReplicateLogEventEnum.SEGMENT_ANALYSIS_REMOTE_API_FAILED.value
|
||
),
|
||
event_status=LogEventStatusEnum.FAILED.value,
|
||
config=config,
|
||
trace_id=trace_id,
|
||
user_id=user_id,
|
||
task_set_id=task_set_id,
|
||
segment_id=segment_id,
|
||
mode=mode,
|
||
request_data={**log_request_data, "api_url": url},
|
||
message="拆镜视频分析模型请求异常",
|
||
error=str(exc),
|
||
extra_detail={"exception_type": type(exc).__name__},
|
||
)
|
||
raise
|
||
|
||
response_data = _safe_response_json(response)
|
||
remote_request_id, remote_code, remote_message, remote_param = _extract_remote_error(response_data)
|
||
if response.status_code >= 400:
|
||
_log_shot_ai_model_event(
|
||
event_type=(
|
||
ShotReplicateLogEventEnum.ANALYSIS_REMOTE_API_FAILED.value
|
||
if mode == "full_breakdown"
|
||
else ShotReplicateLogEventEnum.SEGMENT_ANALYSIS_REMOTE_API_FAILED.value
|
||
),
|
||
event_status=LogEventStatusEnum.FAILED.value,
|
||
config=config,
|
||
trace_id=trace_id,
|
||
user_id=user_id,
|
||
task_set_id=task_set_id,
|
||
segment_id=segment_id,
|
||
mode=mode,
|
||
request_data={**log_request_data, "api_url": url},
|
||
response_data=response_data,
|
||
http_status=response.status_code,
|
||
remote_request_id=remote_request_id,
|
||
remote_code=remote_code,
|
||
remote_message=remote_message,
|
||
remote_param=remote_param,
|
||
message="拆镜视频分析模型请求失败",
|
||
error=f"HTTP {response.status_code}: {response.text}",
|
||
)
|
||
raise RuntimeError(f"视频拆镜分析 API 请求失败: HTTP {response.status_code}: {response.text}")
|
||
|
||
try:
|
||
raw = response.json()
|
||
except Exception as exc:
|
||
_log_shot_ai_model_event(
|
||
event_type=ShotReplicateLogEventEnum.ANALYSIS_RESPONSE_PARSE_FAILED.value,
|
||
event_status=LogEventStatusEnum.FAILED.value,
|
||
config=config,
|
||
trace_id=trace_id,
|
||
user_id=user_id,
|
||
task_set_id=task_set_id,
|
||
segment_id=segment_id,
|
||
mode=mode,
|
||
request_data={**log_request_data, "api_url": url},
|
||
response_data={"raw_text": response.text},
|
||
http_status=response.status_code,
|
||
message="拆镜视频分析模型响应 JSON 解析失败",
|
||
error=str(exc),
|
||
)
|
||
raise
|
||
|
||
try:
|
||
content = get_message_content_or_raise(raw)
|
||
result = parse_model_json(content)
|
||
except Exception as exc:
|
||
event_type = ShotReplicateLogEventEnum.ANALYSIS_RESPONSE_EMPTY.value if "content 为空" in str(exc) else ShotReplicateLogEventEnum.ANALYSIS_RESPONSE_PARSE_FAILED.value
|
||
_log_shot_ai_model_event(
|
||
event_type=event_type,
|
||
event_status=LogEventStatusEnum.FAILED.value,
|
||
config=config,
|
||
trace_id=trace_id,
|
||
user_id=user_id,
|
||
task_set_id=task_set_id,
|
||
segment_id=segment_id,
|
||
mode=mode,
|
||
request_data={**log_request_data, "api_url": url},
|
||
response_data=raw,
|
||
http_status=response.status_code,
|
||
message="拆镜视频分析模型内容解析失败",
|
||
error=str(exc),
|
||
)
|
||
raise
|
||
|
||
result = fill_none_with_wu(result)
|
||
result = ensure_result_schema(result)
|
||
result = filter_and_normalize_breakdown(result, mode=mode)
|
||
|
||
usage = raw.get("usage") or {}
|
||
token_usage = {
|
||
"input_tokens": _int_usage(usage.get("prompt_tokens") or usage.get("input_tokens")),
|
||
"output_tokens": _int_usage(usage.get("completion_tokens") or usage.get("output_tokens")),
|
||
"total_tokens": _int_usage(usage.get("total_tokens")),
|
||
"finish_reason": ((raw.get("choices") or [{}])[0] or {}).get("finish_reason"),
|
||
"model_config_id": config.id,
|
||
"model_config_name": config.name,
|
||
"model": config.model_name,
|
||
"provider": config.provider,
|
||
"video_fps": _video_fps(),
|
||
"split_min_seconds": _split_min_seconds(),
|
||
"split_max_seconds": _split_max_seconds(),
|
||
"analysis_mode": mode,
|
||
"trace_id": trace_id,
|
||
"log_request": log_request_data,
|
||
}
|
||
if not token_usage["total_tokens"]:
|
||
token_usage["total_tokens"] = token_usage["input_tokens"] + token_usage["output_tokens"]
|
||
|
||
token_usage.update({
|
||
"model_config_id": config.id,
|
||
"model_config_name": config.name,
|
||
"model_provider": config.provider,
|
||
"model_name": config.model_name,
|
||
})
|
||
|
||
_log_shot_ai_model_event(
|
||
event_type=(
|
||
ShotReplicateLogEventEnum.ANALYSIS_REMOTE_API_SUCCESS.value
|
||
if mode == "full_breakdown"
|
||
else ShotReplicateLogEventEnum.SEGMENT_ANALYSIS_REMOTE_API_SUCCESS.value
|
||
),
|
||
event_status=LogEventStatusEnum.SUCCESS.value,
|
||
config=config,
|
||
trace_id=trace_id,
|
||
user_id=user_id,
|
||
task_set_id=task_set_id,
|
||
segment_id=segment_id,
|
||
mode=mode,
|
||
request_data={**log_request_data, "api_url": url},
|
||
response_data=raw,
|
||
token_usage=token_usage,
|
||
http_status=response.status_code,
|
||
message="拆镜视频分析模型请求成功",
|
||
)
|
||
|
||
return ShotVideoAnalysisResult(result=result, raw_response=raw, usage=token_usage)
|
||
|