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+#!/usr/bin/env python3
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+"""build_report.py — 生成拉片 HTML 报告(暗色主题/手机自适应/相对路径).
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+
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+用法:
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+ python3 build_report.py --workdir /tmp/lapian --title "拉片分析:听show丽江酒吧"
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+前置(可缺失, 缺了自动降级):
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+ meta.json / transcript.json / frames.json / comments.json
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+产物: workdir/report.html + workdir/analysis.json
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+特性:
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+ - 分段: 优先 LLM(编导方法论点评), 失败回退规则启发式, 永不中断
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+ - 词频/情绪: 纯本地 n-gram + 情绪词典(无第三方依赖)
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+ - 模板渲染: 内置 mini-Jinja(stdlib), 无需安装 jinja2
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+"""
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+import argparse
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+import datetime
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+import json
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+import os
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+import re
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+import sys
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+import urllib.request
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+
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+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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+from common import e as esc, fmt_ts # noqa: E402
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+
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+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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+TEMPLATE = os.path.join(ROOT, "templates", "report.html.j2")
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+TEXT_MODEL = os.environ.get("FEME_TEXT_MODEL", "deepseek-v4-flash")
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+API_BASE = os.environ.get("FEME_API_BASE", "https://api.fmode.cn/v1")
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+
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+TAG_CLS = {"薛辉": "tag-blue", "郑经说": "tag-purple", "三把刀": "tag-green",
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+ "小希": "tag-yellow", "南门": "tag-yellow", "志楠": "tag-green"}
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+POS_KW = ("同感 真实 说得对 认同 共鸣 喜欢 爱 赞 通透 治愈 舒服 自由 幸福 羡慕 "
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+ "向往 懂生活 回家 回云南 想去 泪目 豁达 乐观").split()
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+NEG_KW = ("不认同 杠 胡说 假的 喷 讨厌 反对 无语 尬 难看 翻车 骗 贩卖焦虑 内卷 累 苦"
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+ ).split()
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+SENT_STOP = "的了是在和就有不人我都这也你们他们啊吧吗呢把被将还又再便而或如果所以因为但是"
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+LLM_PROMPT = """你是短视频编导顾问(方法体系: 薛辉·观点前置/排比/画面感/收束升华; 郑经说·人群分层/功利型内容; 三把刀·流量文案vs变现文案; 编导小希·前三秒画面钩子)。对下面这条口播视频逐段拉片。
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+
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+视频数据: 时长{dur}s, 点赞{digg}, 评论{cmt}, 收藏{coll}, 分享{share}
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+文案全文: {fulltext}
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+
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+逐段台词(带时间戳):
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+{segment_lines}
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+
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+已抽帧画面参考:
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+{frame_lines}
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+
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+严格输出 JSON(不要markdown围栏):
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+{{"segments":[{{"i":0,"func":"起/承/转/合/收 之一","techniques":["导师·技法名"],"comment":"60-90字点评: 句式/节奏/画面匹配/情绪价值, 点名用了什么技法","reuse":"可直接套用的句式模板, 含{{占位符}}"}}],
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+"suggestions":[{{"title":"建议标题","body":"40字内可执行动作"}}]}}
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+要求: segments 数量与输入逐段台词一一对应; techniques 每段1-3个, 用"导师·技法"格式; suggestions 3条。"""
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+
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+
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+# ----------------------------------------------------------- mini-Jinja ----
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+_TOKEN = re.compile(r"({{.*?}}|{%.*?%})", re.S)
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+
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+
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+def _lookup(val, key):
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+ if isinstance(val, dict):
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+ return val.get(key, "")
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+ if isinstance(val, (list, tuple)):
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+ if key == "length":
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+ return len(val)
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+ try:
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+ return val[int(key)]
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+ except (ValueError, IndexError):
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+ return ""
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+ return getattr(val, key, "")
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+
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+
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+def _resolve(expr, ctx):
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+ expr = expr.strip()
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+ if re.fullmatch(r"-?\d+(\.\d+)?", expr):
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+ return float(expr)
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+ if expr in ("true", "True"):
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+ return True
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+ if expr in ("false", "False", "none", "None"):
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+ return False
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+ parts = expr.split(".")
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+ val = ctx.get(parts[0], "")
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+ for p in parts[1:]:
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+ val = _lookup(val, p)
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+ return val
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+
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+
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+def _parse(tokens, i):
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+ nodes = []
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+ while i < len(tokens):
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+ t = tokens[i]
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+ if t.startswith("{%"):
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+ stmt = t[2:-2].strip()
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+ kw, _, rest = stmt.partition(" ")
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+ if kw in ("endif", "endfor", "else"):
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+ return nodes, i
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+ if kw == "if":
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+ branches, cond = [], rest.strip()
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+ while True:
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+ body, i = _parse(tokens, i + 1)
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+ branches.append((cond, body))
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+ tk = tokens[i]
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+ tstmt = tk[2:-2].strip()
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+ tkw, _, trest = tstmt.partition(" ")
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+ if tkw == "elif":
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+ cond = trest.strip()
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+ continue
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+ if tkw == "else":
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+ body, i = _parse(tokens, i + 1)
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+ branches.append((None, body))
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+ i += 1 # consume endif
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+ break
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+ nodes.append(("if", branches))
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+ elif kw == "for":
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+ var, itexpr = rest.strip().split(" in ", 1)
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+ body, i = _parse(tokens, i + 1)
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+ i += 1 # consume endfor
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+ nodes.append(("for", var.strip(), itexpr.strip(), body))
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+ else:
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+ i += 1
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+ elif t.startswith("{{"):
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+ nodes.append(("expr", t[2:-2].strip()))
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+ i += 1
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+ else:
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+ nodes.append(("text", t))
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+ i += 1
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+ return nodes, i
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+
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+
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+def _exec(nodes, ctx, out):
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+ for node in nodes:
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+ if node[0] == "text":
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+ out.append(node[1])
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+ elif node[0] == "expr":
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+ v = _resolve(node[1], ctx)
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+ out.append("" if v is None else str(v))
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+ elif node[0] == "if":
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+ for cond, body in node[1]:
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+ if cond is None or _resolve(cond, ctx):
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+ _exec(body, ctx, out)
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+ break
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+ elif node[0] == "for":
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+ _, var, itexpr, body = node
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+ it = _resolve(itexpr, ctx)
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+ items = it if isinstance(it, (list, tuple)) else ([it] if it else [])
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+ n = len(items)
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+ for idx, item in enumerate(items, 1):
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+ sub = dict(ctx)
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+ sub[var] = item
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+ sub["loop"] = {"index": idx, "first": idx == 1, "last": idx == n}
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+ _exec(body, sub, out)
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+
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+
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+def render_template(template: str, ctx: dict) -> str:
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+ tokens = _TOKEN.split(template)
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+ nodes, _ = _parse(tokens, 0)
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+ out = []
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+ _exec(nodes, ctx, out)
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+ return "".join(out)
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+
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+
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+# ------------------------------------------------------------- analysis ----
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+def load_json(path):
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+ if os.path.exists(path):
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+ try:
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+ return json.load(open(path, encoding="utf-8"))
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+ except Exception:
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+ return None
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+ return None
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+
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+
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+def call_llm(prompt: str):
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+ payload = {"model": TEXT_MODEL, "temperature": 0.3, "max_tokens": 4000,
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+ "messages": [{"role": "user", "content": prompt}]}
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+ req = urllib.request.Request(
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+ f"{API_BASE}/chat/completions", data=json.dumps(payload).encode(),
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+ headers={"Content-Type": "application/json",
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+ "Authorization": f"Bearer {os.environ.get('FEME_NEWAPI_TOKEN') or os.environ.get('FMODE_API_KEY','')}"})
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+ with urllib.request.urlopen(req, timeout=180) as r:
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+ resp = json.loads(r.read())
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+ return resp["choices"][0]["message"]["content"]
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+
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+
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+def parse_llm_json(text: str) -> dict:
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+ text = text.strip()
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+ if text.startswith("```"):
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+ text = text.strip("`").lstrip("json").strip()
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+ try:
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+ return json.loads(text)
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+ except Exception:
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+ pass
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+ if "{" in text and "}" in text:
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+ try:
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+ return json.loads(text[text.index("{"): text.rindex("}") + 1])
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+ except Exception:
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+ pass
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+ return {}
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+
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+
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+def tag_cls(label: str) -> str:
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+ for k, cls in TAG_CLS.items():
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+ if k in label:
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+ return cls
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+ return "tag-blue"
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+
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+
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+def heuristic_techniques(i: int, n: int, text: str) -> list:
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+ tags = []
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+ if i == 0:
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+ tags.append("薛辉·观点前置")
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+ tags.append("小希·前三秒钩子")
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+ if re.search(r"(既不|又不|也不|不急着|不是[^。，]*,?不是)", text):
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+ tags.append("薛辉·排比")
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+ if re.search(r"(有人说|不是.*而是|别人.*我们|他们.*我们)", text):
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+ tags.append("薛辉·反击句式/对比")
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+ if re.search(r"(块钱|免费|不要钱|吃|喝|买|菜市场|院子)", text):
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+ tags.append("薛辉·画面感细节")
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+ if i == n - 1:
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+ tags.append("薛辉·收束升华")
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+ return tags or ["志楠·节奏控制"]
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+
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+
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+def fallback_comment(i, n, text) -> str:
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+ if i == 0:
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+ return "开场即抛出反常识观点, 承担前三秒留人任务; 句式短促, 信息密度高。"
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+ if i == n - 1:
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+ return "结尾回扣开头形成闭环, 用更高维度的定义完成价值升华。"
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+ return "中段延续主线, 用具体细节支撑观点, 保持节奏与信息密度。"
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+
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+
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+def chi_words(text: str, top: int = 8) -> list:
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+ """无分词词典 → 2/3-gram 近似, 去停用字边, 去包含重复。"""
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+ text = re.sub(r"[^\u4e00-\u9fff]", " ", text)
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+ grams = {}
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+ for w in text.split():
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+ for n in (2, 3):
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+ for k in range(len(w) - n + 1):
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+ g = w[k:k + n]
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+ grams[g] = grams.get(g, 0) + 1
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+ cands = [(g, c) for g, c in grams.items()
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+ if c >= 2 and g[0] not in SENT_STOP and g[-1] not in SENT_STOP]
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+ cands.sort(key=lambda x: -x[1])
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+ kept = []
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+ for g, c in cands:
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+ if not any(g in k for k, _ in kept):
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+ kept.append((g, c))
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+ if len(kept) >= top:
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+ break
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+ mx = kept[0][1] if kept else 1
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+ return [{"word": g, "count": c, "pct": max(6, round(c / mx * 100))}
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+ for g, c in kept]
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+
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+
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+def sentiment_split(text: str) -> list:
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+ sents = [s for s in re.split(r"[。！？；\n]", text) if s.strip()]
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+ pos = sum(1 for s in sents if any(k in s for k in POS_KW))
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+ neg = sum(1 for s in sents if any(k in s for k in NEG_KW))
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+ neu = max(len(sents) - pos - neg, 0)
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+ tot = max(pos + neg + neu, 1)
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+ rows = [("正面", pos, "var(--green)"), ("中性", neu, "var(--yellow)"),
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+ ("负面", neg, "var(--red)")]
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+ out, acc = [], 0
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+ for i, (label, v, color) in enumerate(rows):
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+ pct = round(v / tot * 100) if i < 2 else max(0, 100 - acc)
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+ acc += pct
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+ out.append({"label": label, "pct": pct, "color": color})
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+ return out
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+
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+
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+def comment_insight(text: str) -> str:
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+ if any(k in text for k in ("回去", "回云南", "回家", "社保")):
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+ return "在外游子的身份共鸣——最能带动转发"
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+ if any(k in text for k in ("同感", "真实", "说得对", "就是这样")):
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+ return "强认同型评论, 印证文案戳中人群"
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+ if any(k in text for k in ("不是", "而是", "——")):
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+ return "评论本身有文案意识, 可沉淀为选题素材"
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+ if any(k in text for k in ("但", "不过", "其实")):
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+ return "补充视角, 可作为下一条视频的回应点"
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+ return "氛围型互动"
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+
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+
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+def classify_comment(text: str) -> str:
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+ if any(k in text for k in NEG_KW):
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+ return "neg"
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+ if any(k in text for k in POS_KW):
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+ return "pos"
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+ return "neu"
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+
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+
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+def main():
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+ ap = argparse.ArgumentParser()
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+ ap.add_argument("--workdir", required=True)
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+ ap.add_argument("--title", default="")
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+ args = ap.parse_args()
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+ wd = args.workdir
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+
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+ meta = load_json(os.path.join(wd, "meta.json")) or {}
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+ tr = load_json(os.path.join(wd, "transcript.json")) or {}
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+ frames = load_json(os.path.join(wd, "frames.json")) or []
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+ comments = load_json(os.path.join(wd, "comments.json")) or []
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+
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+ data = tr.get("data") or {}
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+ segs = data.get("segments") or []
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+ fulltext = data.get("text") or "".join(s.get("text", "") for s in segs)
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+ if not segs and fulltext: # 无时间戳转写 → 整体一段
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+ segs = [{"bg": 0, "ed": int(meta.get("duration_s", 60) * 1000),
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+ "speaker": "0", "text": fulltext}]
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+ if not segs:
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+ raise SystemExit("[report] 没有转写内容, 先跑 transcribe.py")
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+ duration_s = meta.get("duration_s") or (segs[-1]["ed"] / 1000)
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+
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+ # --- LLM 逐段点评(失败回退启发式) ---
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+ llm_engine, llm_out = "规则启发式", {}
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+ try:
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+ seg_lines = "\n".join(
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+ f"[{fmt_ts(s['bg'])}-{fmt_ts(s['ed'])}] {s.get('text','')}" for s in segs)
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+ frame_lines = "\n".join(
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+ f"{f['t_label']}: {f.get('caption','')}" for f in frames) or "(无)"
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+ st = meta.get("stats") or {}
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+ prompt = LLM_PROMPT.format(
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+ dur=round(duration_s), digg=st.get("digg", "?"), cmt=st.get("comment", "?"),
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+ coll=st.get("collect", "?"), share=st.get("share", "?"),
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+ fulltext=fulltext[:1200], segment_lines=seg_lines, frame_lines=frame_lines)
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+ raw = call_llm(prompt)
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+ llm_out = parse_llm_json(raw)
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+ if llm_out.get("segments"):
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+ llm_engine = TEXT_MODEL
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+ print(f"[report] LLM 点评 {len(llm_out['segments'])} 段 ({llm_engine})")
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+ else:
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+ print("[report] LLM 输出无法解析, 回退规则启发式")
|
|
|
+ except Exception as ex:
|
|
|
+ print(f"[report] LLM 调用失败({ex}), 回退规则启发式")
|
|
|
+
|
|
|
+ llm_segs = {int(s.get("i", i)): s for i, s in enumerate(llm_out.get("segments") or [])}
|
|
|
+
|
|
|
+ # --- 组装分析段 ---
|
|
|
+ n = len(segs)
|
|
|
+ analysis_segs = []
|
|
|
+ for i, s in enumerate(segs):
|
|
|
+ t0, t1 = int(s.get("bg", 0)), int(s.get("ed", 0))
|
|
|
+ text = s.get("text", "")
|
|
|
+ L = llm_segs.get(i) or {}
|
|
|
+ techniques = [t for t in (L.get("techniques") or
|
|
|
+ heuristic_techniques(i, n, text)) if t]
|
|
|
+ frame = min(frames, key=lambda f: abs(f["t_ms"] - t0),
|
|
|
+ default=None) if frames else None
|
|
|
+ if frame and abs(frame["t_ms"] - t0) > 8000:
|
|
|
+ frame = None
|
|
|
+ analysis_segs.append({
|
|
|
+ "i": i, "bg": t0, "ed": t1,
|
|
|
+ "t_start": fmt_ts(t0), "t_end": fmt_ts(t1), "t_start_s": t0 // 1000,
|
|
|
+ "text": esc(text), "preview": esc(text[:24] + ("…" if len(text) > 24 else "")),
|
|
|
+ "techniques": [{"label": esc(t), "cls": tag_cls(t)} for t in techniques],
|
|
|
+ "func": esc(L.get("func") or ""),
|
|
|
+ "comment_html": esc(L.get("comment") or fallback_comment(i, n, text)),
|
|
|
+ "reuse": esc(L.get("reuse") or ""),
|
|
|
+ "frame": frame,
|
|
|
+ })
|
|
|
+
|
|
|
+ # --- 整体结构表 ---
|
|
|
+ stage_names = ["起", "承", "转", "合", "收"]
|
|
|
+ structure_rows = []
|
|
|
+ for idx, sg in enumerate(analysis_segs):
|
|
|
+ stage = sg["func"] or stage_names[min(int(idx / n * len(stage_names)),
|
|
|
+ len(stage_names) - 1)]
|
|
|
+ structure_rows.append({
|
|
|
+ "stage": esc(stage), "time": f"{sg['t_start']}—{sg['t_end']}",
|
|
|
+ "func": esc(re.sub(r"^.{0,3}[:：]", "", sg["func"]) if sg["func"]
|
|
|
+ else ("开场留人" if idx == 0 else
|
|
|
+ ("收束升华" if idx == n - 1 else "承接展开"))),
|
|
|
+ "techniques": " ".join(t["label"] for t in sg["techniques"]) or "—",
|
|
|
+ })
|
|
|
+ summary_html = esc(llm_out.get("structure_summary") or
|
|
|
+ f"{n}段完成全片, 各段技法如上; 逐段细节见上方可折叠卡片。")
|
|
|
+
|
|
|
+ # --- 词频/情绪 ---
|
|
|
+ wf = chi_words(fulltext)
|
|
|
+ sentiment = sentiment_split(fulltext)
|
|
|
+ word_count = len(re.sub(r"\s", "", fulltext))
|
|
|
+ wpm = round(word_count / max(duration_s / 60, 0.1))
|
|
|
+
|
|
|
+ # --- 评论区 ---
|
|
|
+ comment_stats, top_comments, comment_keywords, comment_total = [], [], [], 0
|
|
|
+ if comments:
|
|
|
+ cls_counts = {"pos": 0, "neu": 0, "neg": 0}
|
|
|
+ for c in comments:
|
|
|
+ cls_counts[classify_comment(c.get("text", ""))] += 1
|
|
|
+ comment_total = len(comments)
|
|
|
+ rows = [("正面认同", cls_counts["pos"], "var(--green)"),
|
|
|
+ ("中性补充", cls_counts["neu"], "var(--yellow)"),
|
|
|
+ ("争议", cls_counts["neg"], "var(--red)")]
|
|
|
+ acc = 0
|
|
|
+ for i, (label, v, color) in enumerate(rows):
|
|
|
+ pct = round(v / comment_total * 100) if i < 2 else max(0, 100 - acc)
|
|
|
+ acc += pct
|
|
|
+ comment_stats.append({"label": label, "pct": pct, "color": color})
|
|
|
+ top_comments = [{"nickname": esc(c.get("nickname", "匿名")),
|
|
|
+ "likes": c.get("likes", 0), "text": esc(c.get("text", "")),
|
|
|
+ "insight": esc(comment_insight(c.get("text", "")))}
|
|
|
+ for c in comments[:5]]
|
|
|
+ comment_keywords = chi_words(" ".join(c.get("text", "") for c in comments), top=6)
|
|
|
+
|
|
|
+ # --- 数据卡片 ---
|
|
|
+ st = meta.get("stats") or {}
|
|
|
+ stats_cards = [{"num": f"{duration_s:.0f}s", "label": "总时长"}]
|
|
|
+ if st.get("digg"):
|
|
|
+ stats_cards += [{"num": f"{st['digg']:,}", "label": "👍 点赞"},
|
|
|
+ {"num": f"{st['comment']:,}", "label": "💬 评论"},
|
|
|
+ {"num": f"{st['collect']:,}", "label": "🔖 收藏"},
|
|
|
+ {"num": f"{st['share']:,}", "label": "↗ 分享"},
|
|
|
+ {"num": f"{st['digg'] / max(st['comment'], 1):.0f}:1",
|
|
|
+ "label": "赞评比"}]
|
|
|
+ else:
|
|
|
+ stats_cards += [{"num": len(comments), "label": "评论采集"},
|
|
|
+ {"num": len(frames), "label": "抽帧"},
|
|
|
+ {"num": n, "label": "分段"},
|
|
|
+ {"num": word_count, "label": "总字数"},
|
|
|
+ {"num": wpm, "label": "字/分"}]
|
|
|
+
|
|
|
+ # --- 建议 ---
|
|
|
+ suggestions = [{"title": esc(s.get("title", f"建议{ i + 1 }")),
|
|
|
+ "body": esc(s.get("body", ""))}
|
|
|
+ for i, s in enumerate(llm_out.get("suggestions") or [])]
|
|
|
+ if not suggestions:
|
|
|
+ hw = wf[0]["word"] if wf else "核心词"
|
|
|
+ pos_pct = next((r["pct"] for r in (comment_stats or sentiment)
|
|
|
+ if r["label"] in ("正面认同", "正面")), 0)
|
|
|
+ suggestions = [
|
|
|
+ {"title": "强化记忆锚点", "body": f"高频词「{hw}」贯穿全片, 封面/标题/评论区置顶都应重复它, 形成账号记忆点。"},
|
|
|
+ {"title": "延续情绪线", "body": f"正面情绪约{pos_pct}%, 下一条保持同一情绪基调并升级一个具体场景细节。"},
|
|
|
+ {"title": "补强行动指令", "body": "结尾闭环后加一句轻互动引导(如「你家乡呢?」), 把共鸣转化为评论量。"},
|
|
|
+ ]
|
|
|
+
|
|
|
+ # --- 汇总上下文(所有动态值已转义) ---
|
|
|
+ author = meta.get("author") or "本地视频"
|
|
|
+ title = args.title or f"拉片分析:{author}"
|
|
|
+ today = datetime.date.today().strftime("%Y%m%d")
|
|
|
+ aweme_id = str(meta.get("aweme_id") or "local")
|
|
|
+ ctx = {
|
|
|
+ "meta": {
|
|
|
+ "title": esc(title),
|
|
|
+ "subtitle": esc(f"{(meta.get('desc') or '')[:42]} · {duration_s:.0f}秒 · "
|
|
|
+ f"{n}段 · 方法论对照拆解"),
|
|
|
+ "author": esc(author), "author_signature": esc(meta.get("author_signature", "")),
|
|
|
+ "tags": [esc(t) for t in (meta.get("tags") or [])],
|
|
|
+ "publish_time": esc(meta.get("create_time", "")),
|
|
|
+ "aweme_id": esc(aweme_id), "platform": esc(meta.get("platform", "douyin")),
|
|
|
+ "generated_at": datetime.datetime.now().strftime("%Y-%m-%d %H:%M"),
|
|
|
+ "llm_engine": esc(llm_engine),
|
|
|
+ "report_id": esc(f"lapian-{meta.get('platform','douyin')}-{today}-{aweme_id[:10]}"),
|
|
|
+ "word_count": word_count, "wpm": wpm,
|
|
|
+ "sentence_count": len([s for s in re.split(r"[。！？]", fulltext) if s.strip()]),
|
|
|
+ },
|
|
|
+ "stats_cards": stats_cards,
|
|
|
+ "segments": analysis_segs,
|
|
|
+ "structure_rows": structure_rows,
|
|
|
+ "summary_html": summary_html,
|
|
|
+ "wordfreq": wf,
|
|
|
+ "sentiment": sentiment,
|
|
|
+ "comments": bool(comments),
|
|
|
+ "comment_total": comment_total,
|
|
|
+ "comment_stats": comment_stats,
|
|
|
+ "top_comments": top_comments,
|
|
|
+ "comment_keywords": comment_keywords,
|
|
|
+ "suggestions": suggestions,
|
|
|
+ }
|
|
|
+
|
|
|
+ html = render_template(open(TEMPLATE, encoding="utf-8").read(), ctx)
|
|
|
+ out_html = os.path.join(wd, "report.html")
|
|
|
+ open(out_html, "w", encoding="utf-8").write(html)
|
|
|
+ json.dump({"meta": {k: v for k, v in ctx["meta"].items()},
|
|
|
+ "segments": [{k: (v if k != "text" else v) for k, v in sg.items()
|
|
|
+ if k not in ("frame",)} for sg in analysis_segs],
|
|
|
+ "suggestions": suggestions,
|
|
|
+ "llm_engine": llm_engine},
|
|
|
+ open(os.path.join(wd, "analysis.json"), "w", encoding="utf-8"),
|
|
|
+ ensure_ascii=False, indent=2, default=str)
|
|
|
+ print(f"[report] {out_html} ({os.path.getsize(out_html)/1024:.0f} KB)")
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ main()
|
skill-bot
3 settimane fa