simonkoson
9df4868191
- 修正 DeepSeek 模型名 deepseek-chat → deepseek-v4-pro - 摘要块：修复 **粗体** markdown 渲染、左右块可滚动、左块固定粉红色系 - 新增 prompt4（内容摘要卡）+ prompt5（诊断报告）+ 三处 prompt5 优化 - 新增 004 迁移（episodes +content_digest JSONB）、导入脚本、摘要卡生成脚本 - 更新 CLAUDE.md 状态栏/进度/交接备注/关键决策 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
332 lines
12 KiB
Python
332 lines
12 KiB
Python
"""
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收视分析 API — 提供收视走势和指标卡数据
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端点：
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GET /api/analytics/years → 有收视数据的年份列表（去重降序）
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GET /api/analytics/episodes?year=2026 → 指定年份所有期次的收视数据 + 年度目标
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"""
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import json
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import hashlib
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from pathlib import Path
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from datetime import datetime
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from fastapi import APIRouter, Depends, Query
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from pydantic import BaseModel
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from sqlalchemy import extract
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from sqlalchemy import distinct
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from sqlmodel import Session, select
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from openai import OpenAI
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from app.core.config import settings
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from app.core.deps import require_role
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from app.db.session import get_session
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from app.models.episode import Episode
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from app.models.yearly_target import YearlyTarget
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from app.models.user import UserRole
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router = APIRouter(prefix="/api/analytics", tags=["收视分析"])
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# 诊断报告缓存（内存，重启清空）
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_report_cache = {}
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# prompt5 文件路径
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_PROJECT_ROOT = Path(__file__).parent.parent.parent.parent
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_PROMPT5_PATH = _PROJECT_ROOT / "ai-labeling" / "prompts" / "prompt5_diagnosis_report.md"
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def _require_read():
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"""三角色都可读"""
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return require_role(UserRole.zhipianren, UserRole.zebian, UserRole.biandao)
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@router.get("/years")
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def get_available_years(
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session: Session = Depends(get_session),
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current_user=Depends(_require_read()),
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):
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"""返回有收视数据的年份列表（去重，降序）。"""
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statement = (
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select(distinct(extract("year", Episode.air_date).label("year")))
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.where(Episode.audience_share.is_not(None))
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.order_by(extract("year", Episode.air_date).desc())
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)
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result = session.exec(statement).all()
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# extract 返回 float，转 int
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return [int(y) for y in result]
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@router.get("/episodes")
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def get_analytics_episodes(
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year: int | None = Query(None, description="年份，不传则取最近有数据的年份"),
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session: Session = Depends(get_session),
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current_user=Depends(_require_read()),
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):
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"""返回指定年份所有期次的收视数据（按 air_date 升序），附带该年年度目标。
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返回格式：
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{
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"year": 2026,
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"yearly_target": { "base_target": 0.6448, "stretch_target": 0.8989 } | null,
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"episodes": [ { id, episode_number, program_name, air_date, editor_name_snapshot,
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audience_share, audience_rating }, ... ]
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}
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"""
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# 如果没传 year，找最近有收视数据的年份
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if year is None:
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year_stmt = (
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select(extract("year", Episode.air_date).label("y"))
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.where(Episode.audience_share.is_not(None))
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.order_by(extract("year", Episode.air_date).desc())
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.limit(1)
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)
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latest_year = session.exec(year_stmt).first()
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if latest_year is None:
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return {"year": None, "yearly_target": None, "episodes": []}
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year = int(latest_year)
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# 查询该年份的期次（按 air_date 升序）
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ep_stmt = (
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select(Episode)
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.where(extract("year", Episode.air_date) == year)
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.order_by(Episode.air_date.asc())
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)
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episodes = session.exec(ep_stmt).all()
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# 查询该年份的年度目标
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target_stmt = select(YearlyTarget).where(YearlyTarget.year == year)
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target = session.exec(target_stmt).first()
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# 组装返回
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ep_list = [
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{
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"id": ep.id,
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"episode_number": ep.episode_number,
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"program_name": ep.program_name,
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"air_date": ep.air_date.isoformat() if ep.air_date else None,
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"editor_name_snapshot": ep.editor_name_snapshot,
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"audience_share": ep.audience_share,
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"audience_rating": ep.audience_rating,
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"program_format": ep.program_format,
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"narrative_structure": ep.narrative_structure,
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"opening_hook": ep.opening_hook,
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"equipment_domain": ep.equipment_domain,
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}
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for ep in episodes
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]
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yearly_target = None
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if target:
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yearly_target = {
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"base_target": target.base_target,
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"stretch_target": target.stretch_target,
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}
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return {
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"year": year,
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"yearly_target": yearly_target,
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"episodes": ep_list,
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}
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# ── AI 诊断报告 ──
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class DiagnosisRequest(BaseModel):
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year: int
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ep_start: int
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ep_end: int
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force: bool = False
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def _build_user_message(episodes, base_target, stretch_target, avg_share, pass_count, max_ep, min_ep):
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"""组装给 DeepSeek 的 user message，格式对齐 prompt5 的输入规范。"""
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first_ep = episodes[0]
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last_ep = episodes[-1]
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count = len(episodes)
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# 判色函数
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def judge(share):
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if share >= stretch_target:
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return "优秀"
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elif share >= base_target:
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return "达标"
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else:
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return "待提升"
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# 摸高完成率
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stretch_pct = round(avg_share / stretch_target * 100, 1) if stretch_target > 0 else 0
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lines = []
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lines.append("请根据以下数据，撰写收视诊断分析报告。\n")
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# 分析范围
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lines.append("## 分析范围\n")
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lines.append(
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f"第{first_ep.episode_number}期《{first_ep.program_name}》至 "
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f"第{last_ep.episode_number}期《{last_ep.program_name}》（共{count}期），"
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f"{first_ep.air_date}至{last_ep.air_date}播出"
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)
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lines.append(f"年度目标：基础目标 {base_target}，摸高目标 {stretch_target}\n")
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# 整体统计
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lines.append("## 整体统计\n")
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lines.append(f"- 平均份额：{avg_share}（摸高完成率 {stretch_pct}%）")
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lines.append(f"- 达标期数：{pass_count}/{count}")
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lines.append(f"- 最高份额：{float(max_ep.audience_share)}（第{max_ep.episode_number}期《{max_ep.program_name}》）")
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lines.append(f"- 最低份额：{float(min_ep.audience_share)}（第{min_ep.episode_number}期《{min_ep.program_name}》）\n")
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# 逐期数据表格
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lines.append("## 逐期数据\n")
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lines.append("| 播出期号 | 节目名 | 份额 | 判定 | 题材类型 | 叙事结构 | 钩子强度 | 装备领域 |")
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lines.append("|---------|-------|------|------|---------|---------|---------|---------|")
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for ep in episodes:
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share = float(ep.audience_share)
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domain_str = "、".join(ep.equipment_domain) if ep.equipment_domain else "-"
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lines.append(
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f"| 第{ep.episode_number}期 | {ep.program_name} | {share} | {judge(share)} "
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f"| {ep.program_format or '-'} | {ep.narrative_structure or '-'} "
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f"| {ep.opening_hook or '-'} | {domain_str} |"
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)
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lines.append("")
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# 各期内容摘要卡
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lines.append("## 各期内容摘要卡\n")
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for ep in episodes:
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share = float(ep.audience_share)
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lines.append(f"### 第{ep.episode_number}期《{ep.program_name}》（份额 {share}）")
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digest = ep.content_digest
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if digest:
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lines.append(f"- 核心切口：{digest.get('核心切口', '-')}")
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# 叙事亮点可能是数组
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highlights = digest.get('叙事亮点', [])
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if isinstance(highlights, list):
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lines.append(f"- 叙事亮点：{'；'.join(highlights)}")
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else:
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lines.append(f"- 叙事亮点：{highlights}")
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lines.append(f"- 观众门槛：{digest.get('观众门槛', '-')}")
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# 话题性是嵌套结构
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topic = digest.get('话题性', {})
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if isinstance(topic, dict):
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lines.append(
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f"- 话题性：{topic.get('总评', '-')} — "
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f"大众认知度：{topic.get('大众认知度', '-')}；"
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f"降维切口：{topic.get('降维切口', '-')}；"
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f"惊奇密度：{topic.get('惊奇密度', '-')}"
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)
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else:
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lines.append(f"- 话题性：{topic}")
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# 潜在弱点可能是数组
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weaknesses = digest.get('潜在弱点', [])
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if isinstance(weaknesses, list):
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lines.append(f"- 潜在弱点：{'；'.join(weaknesses)}")
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else:
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lines.append(f"- 潜在弱点：{weaknesses}")
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lines.append(f"- 时效关联：{digest.get('时效关联', '-')}")
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else:
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lines.append("- （无文稿摘要）")
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lines.append("")
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return "\n".join(lines)
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@router.post("/diagnosis-report")
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def generate_diagnosis_report(
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req: DiagnosisRequest,
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session: Session = Depends(get_session),
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current_user=Depends(_require_read()),
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):
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"""生成 AI 诊断报告。同一范围缓存结果，force=True 时重新生成。"""
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cache_key = f"{req.year}_{req.ep_start}_{req.ep_end}"
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# 检查缓存
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if not req.force and cache_key in _report_cache:
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return _report_cache[cache_key]
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# 1. 查询所选范围的 episodes
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ep_stmt = (
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select(Episode)
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.where(extract("year", Episode.air_date) == req.year)
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.where(Episode.episode_number >= req.ep_start)
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.where(Episode.episode_number <= req.ep_end)
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.where(Episode.audience_share.is_not(None))
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.order_by(Episode.episode_number.asc())
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)
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episodes = session.exec(ep_stmt).all()
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if not episodes:
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return {"error": "所选范围内没有收视数据"}
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# 2. 查年度目标
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target_stmt = select(YearlyTarget).where(YearlyTarget.year == req.year)
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target = session.exec(target_stmt).first()
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if not target:
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return {"error": f"{req.year}年没有设置年度目标"}
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base_target = float(target.base_target)
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stretch_target = float(target.stretch_target)
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# 3. 计算统计数据
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shares = [float(ep.audience_share) for ep in episodes]
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avg_share = round(sum(shares) / len(shares), 4) if shares else 0
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pass_count = sum(1 for s in shares if s >= base_target)
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max_ep = max(episodes, key=lambda e: float(e.audience_share))
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min_ep = min(episodes, key=lambda e: float(e.audience_share))
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# 三档判定
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if avg_share >= stretch_target:
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tier = "excellent"
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elif avg_share >= base_target:
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tier = "on_target"
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else:
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tier = "danger"
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# 4. 组装 user message
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user_message = _build_user_message(episodes, base_target, stretch_target, avg_share, pass_count, max_ep, min_ep)
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# 5. 读 system prompt
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system_prompt = _PROMPT5_PATH.read_text(encoding="utf-8")
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# 6. 调 DeepSeek
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if not settings.DEEPSEEK_API_KEY:
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return {"error": "DEEPSEEK_API_KEY 未配置"}
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client = OpenAI(
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api_key=settings.DEEPSEEK_API_KEY,
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base_url="https://api.deepseek.com",
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)
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response = client.chat.completions.create(
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model="deepseek-v4-pro",
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_message},
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],
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temperature=0.3,
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)
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report_markdown = response.choices[0].message.content
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# 7. 组装返回
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result = {
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"tier": tier,
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"avg_share": avg_share,
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"episode_count": len(episodes),
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"pass_count": pass_count,
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"highest": {
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"ep": max_ep.episode_number,
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"name": max_ep.program_name,
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"share": float(max_ep.audience_share),
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},
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"lowest": {
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"ep": min_ep.episode_number,
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"name": min_ep.program_name,
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"share": float(min_ep.audience_share),
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},
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"report_markdown": report_markdown,
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"generated_at": datetime.now().isoformat(),
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"model": "deepseek-v4-pro",
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"disclaimer": "本报告基于已入库的收视数据、节目标签及内容摘要生成，未纳入同时段竞品、社会热点等外部因素。分析结论难免挂一漏万，仅供栏目内部讨论参考，不构成节目决策依据。",
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}
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# 8. 缓存
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_report_cache[cache_key] = result
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return result
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