feat: 品牌升级(知冶→战知) + 应用工具广场重构 + 新增工具集成
品牌升级： - 全站品牌从"知冶"更名为"战知" - 更换 favicon、侧边栏 logo、登录页 logo - 更新登录页标语和首页欢迎语 应用广场重构： - 从后端数据库驱动改为前端静态配置，按分类 tab 展示 - 新增工具卡片 UI，支持 logo 图片和 emoji 图标 新增工具部署： - Stirling PDF (端口18080) - PDF 处理工具箱 - Excalidraw (端口18081) - 手绘风格白板，集成 AI 绘图 - TrWebOCR (端口18083) - 中文离线 OCR - LibreTranslate (端口18084) - 中英翻译引擎 - PPTist (端口18085) - 在线 PPT 编辑器 - PPTist AI 后端 (端口18086) - 对接 deepseek-v3 生成大纲/PPT/写作 - Excalidraw AI 代理 (端口18082) - 对接 deepseek-v3 生成 Mermaid 图 其他： - 智能场景仅保留"选题推荐" - vite 代理配置增加 /pdf/ 和 /draw/ 路由 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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scripts/pptist-ai-backend.py
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scripts/pptist-ai-backend.py
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"""
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PPTist AI 后端服务
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接收 PPTist 的 AIPPT 请求，调用 deepseek-v3 生成大纲和PPT内容
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端口: 18086
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"""
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from fastapi import FastAPI, Request
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import StreamingResponse
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import httpx
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import json
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app = FastAPI()
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_methods=["*"],
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allow_headers=["*"],
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)
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LLM_API = "http://10.102.24.75:3000/v1/chat/completions"
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LLM_KEY = "sk-BlQIGRrotbVDWE5mXCPBFjVWIvJ83hldzz67xInNwzVo7pPb"
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LLM_MODEL = "deepseek-v3"
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OUTLINE_SYSTEM_PROMPT = """你是一个专业的PPT大纲生成助手。用户会给你一个主题，你需要生成一份内容丰富、结构清晰的PPT大纲。
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要求：
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1. 使用Markdown格式，用 # 表示演示文稿标题，## 表示章节标题，### 表示该章节下的内容页标题
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2. 必须包含6个主要章节（##）
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3. 每个章节下必须有2-3个内容页（###）
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4. 每个内容页标题要具体、有信息量，不要太笼统
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5. 内容要专业、逻辑清晰、层层递进
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6. 直接输出Markdown内容，不要有多余的解释
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示例格式：
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# 人工智能技术发展
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## 人工智能概述
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### 定义与发展历史
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### 核心技术与分支
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## 机器学习基础
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### 监督学习与无监督学习
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### 深度学习与神经网络
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### 常用算法对比
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..."""
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AIPPT_SYSTEM_PROMPT = """你是一个专业的PPT内容生成助手。用户会给你一份PPT大纲，你需要根据大纲生成完整、丰富、详实的PPT内容。
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你必须严格按照以下JSON数组格式输出，不要输出任何其他内容。
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结构规则：
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1. 第一页：cover类型，包含演示标题和副标题描述
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2. 第二页：contents类型，列出所有章节名称
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3. 每个章节：先一个transition过渡页，然后每个###子内容对应一个content页
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4. 每个content页必须包含4个items，每个item有title和text
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5. text内容必须详实具体，每条20-40字，包含实质性的数据、观点或分析，不要空泛
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6. 最后一页：end类型
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输出格式（JSON数组，每个元素是一页slide）：
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[
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{"type": "cover", "data": {"title": "演示标题", "text": "一句话描述演示主题和价值"}},
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{"type": "contents", "data": {"items": ["章节1名称", "章节2名称", "章节3名称", "章节4名称", "章节5名称", "章节6名称"]}},
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{"type": "transition", "data": {"title": "章节标题", "text": "本章将介绍什么内容，一句话概述"}},
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{"type": "content", "data": {"title": "内容页标题", "items": [
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{"title": "要点1标题", "text": "要点1的详细说明，包含具体信息、数据或分析"},
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{"title": "要点2标题", "text": "要点2的详细说明"},
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{"title": "要点3标题", "text": "要点3的详细说明"},
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{"title": "要点4标题", "text": "要点4的详细说明"}
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]}},
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{"type": "end"}
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]
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重要：
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- 生成的总页数应在20-25页之间
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- 只输出JSON数组，不要markdown代码块，不要任何解释
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- 确保JSON格式完全正确，可被直接解析"""
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async def stream_llm(messages, temperature=0.7, max_tokens=4096):
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"""流式调用LLM并逐块返回文本"""
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async with httpx.AsyncClient(timeout=120) as client:
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async with client.stream(
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"POST",
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LLM_API,
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json={
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"model": LLM_MODEL,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": True,
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},
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headers={"Authorization": f"Bearer {LLM_KEY}"},
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) as resp:
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async for line in resp.aiter_lines():
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if line.startswith("data: "):
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data = line[6:]
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if data.strip() == "[DONE]":
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break
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try:
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chunk = json.loads(data)
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delta = chunk["choices"][0].get("delta", {})
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content = delta.get("content", "")
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if content:
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yield content
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except (json.JSONDecodeError, KeyError, IndexError):
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continue
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@app.post("/tools/aippt_outline")
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async def aippt_outline(request: Request):
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data = await request.json()
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content = data.get("content", "")
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language = data.get("language", "中文")
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print(f"[AIPPT] Outline request: {content}, language: {language}")
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user_prompt = f"请用{language}为以下主题生成PPT大纲：\n\n{content}"
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async def generate():
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async for text in stream_llm(
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[
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{"role": "system", "content": OUTLINE_SYSTEM_PROMPT},
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{"role": "user", "content": user_prompt},
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],
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temperature=0.7,
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max_tokens=2048,
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):
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yield text
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return StreamingResponse(generate(), media_type="text/event-stream")
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@app.post("/tools/aippt")
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async def aippt(request: Request):
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data = await request.json()
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content = data.get("content", "")
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language = data.get("language", "中文")
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style = data.get("style", "通用")
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print(f"[AIPPT] Generate request, style: {style}, language: {language}")
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user_prompt = f"请根据以下大纲生成PPT内容，风格为「{style}」，语言为{language}：\n\n{content}"
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async def generate():
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"""流式收集LLM输出，按JSON对象级别缓冲，每检测到一个完整对象就立即发送"""
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buf = ""
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brace_depth = 0
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in_string = False
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escape_next = False
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found_array = False # 是否已经跳过了数组开头的 [
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try:
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async for chunk in stream_llm(
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[
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{"role": "system", "content": AIPPT_SYSTEM_PROMPT},
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{"role": "user", "content": user_prompt},
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],
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temperature=0.7,
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max_tokens=8192,
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):
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for ch in chunk:
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# 跳过数组开头的 [ 和结尾的 ] 以及逗号分隔符
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if not found_array:
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if ch == '[':
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found_array = True
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continue
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if brace_depth == 0 and ch in (' ', '\n', '\r', '\t', ',', ']'):
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continue
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buf += ch
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if escape_next:
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escape_next = False
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continue
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if ch == '\\' and in_string:
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escape_next = True
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continue
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if ch == '"' and not escape_next:
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in_string = not in_string
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continue
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if in_string:
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continue
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if ch == '{':
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brace_depth += 1
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elif ch == '}':
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brace_depth -= 1
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if brace_depth == 0:
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# 得到一个完整的JSON对象
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try:
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obj = json.loads(buf)
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yield json.dumps(obj, ensure_ascii=False) + "\n"
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print(f"[AIPPT] Sent slide: {obj.get('type', '?')}")
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except json.JSONDecodeError as e:
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print(f"[AIPPT] JSON parse error: {e}, buf: {buf[:100]}")
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buf = ""
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except Exception as e:
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print(f"[AIPPT] Error: {e}")
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yield json.dumps({"type": "cover", "data": {"title": "生成失败", "text": str(e)[:100]}}) + "\n"
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yield json.dumps({"type": "end"}) + "\n"
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return StreamingResponse(generate(), media_type="text/event-stream")
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WRITING_COMMANDS = {
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"rewrite": "请改写以下文本，保持原意但使用不同的表达方式：",
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"expand": "请扩写以下文本，补充更多细节和内容：",
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"abbreviate": "请缩写以下文本，保留核心要点，使其更简洁：",
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"polish": "请润色以下文本，使其更流畅、专业：",
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"translate": "请将以下文本翻译为英文：",
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}
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@app.post("/tools/ai_writing")
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async def ai_writing(request: Request):
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data = await request.json()
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content = data.get("content", "")
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command = data.get("command", "rewrite")
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print(f"[AIPPT] Writing request, command: {command}")
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instruction = WRITING_COMMANDS.get(command, WRITING_COMMANDS["rewrite"])
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user_prompt = f"{instruction}\n\n{content}"
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async def generate():
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async for text in stream_llm(
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[
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{"role": "system", "content": "你是一个专业的文字助手，帮助用户改写、扩写、缩写、润色或翻译文本。直接输出结果，不要有额外解释。"},
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{"role": "user", "content": user_prompt},
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],
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temperature=0.7,
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max_tokens=2048,
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):
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yield text
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return StreamingResponse(generate(), media_type="text/event-stream")
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=18086)
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