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@ -1,14 +1,11 @@
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import re
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import asyncio
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import socket
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import uvicorn
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from fastapi import FastAPI, Body
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import StreamingResponse, PlainTextResponse
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import socket
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from openai import OpenAI
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import markdown_to_json
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import json
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import asyncio
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from AiService.MarkdownToJsonConverter import MarkdownToJsonConverter
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@ -23,136 +20,14 @@ client = OpenAI(
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)
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def markdown_to_dict(markdown_content):
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"""
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将 Markdown 内容转换为 Python 字典
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"""
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# 将 Markdown 转换为 JSON 字符串
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json_content = markdown_to_json.jsonify(markdown_content)
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# 解码 Unicode 转义
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json_content = json_content.encode('utf-8').decode('unicode_escape')
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# 将 JSON 字符串转换为字典
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return json.loads(json_content)
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def extract_level1(json_dict):
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"""
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提取一级目录，生成指定格式的 JSON 对象列表
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"""
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# 获取第一个一级目录的名称
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level1_title = next(iter(json_dict.keys()), None)
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if level1_title:
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return [{"type": "cover", "data": {"title": level1_title, "text": ""}}]
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return []
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def extract_level2_and_level3(json_dict, level1_title=None):
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"""
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提取指定一级目录下的二级目录及其三级目录内容，生成指定格式的 JSON 对象列表
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"""
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# 如果没有指定一级目录，则使用第一个一级目录
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if level1_title is None:
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level1_title = next(iter(json_dict.keys()), None)
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if level1_title and level1_title in json_dict:
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result = []
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for level2_title, level2_content in json_dict[level1_title].items():
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result.append({"type": "transition", "data": {"title": level2_title, "text": level2_title}})
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# 输出三级目录内容
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if isinstance(level2_content, dict):
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for level3_title, level3_items in level2_content.items():
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# 确保 level3_items 是列表
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if isinstance(level3_items, list):
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items = []
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for item in level3_items:
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# 如果 item 是字符串，直接作为 title 和 text
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if isinstance(item, str):
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items.append({"title": item, "text": item})
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# 如果 item 是字典，提取 title 和 text
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elif isinstance(item, dict):
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items.append({
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"title": item.get("title", ""),
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"text": item.get("text", "")
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})
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else:
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# 如果 level3_items 不是列表，直接作为 title 和 text
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items = [{"title": str(level3_items), "text": str(level3_items)}]
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result.append({
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"type": "content",
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"data": {
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"title": level3_title,
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"text": level3_title,
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"items": items
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}
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})
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return result
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return []
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def extract_contents(json_dict, level1_title=None):
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"""
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提取所有二级目录名称，生成目录部分的 JSON 对象
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"""
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# 如果没有指定一级目录，则使用第一个一级目录
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if level1_title is None:
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level1_title = next(iter(json_dict.keys()), None)
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if level1_title and level1_title in json_dict:
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# 获取所有二级目录名称
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level2_titles = list(json_dict[level1_title].keys())
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return {"type": "contents", "data": {"items": level2_titles}}
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return {"type": "contents", "data": {"items": []}}
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def expand_text_with_ai(json_dict):
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"""
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调用 AI 扩写每个有 title 属性的节点，生成一句话描述并放到 text 属性上
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"""
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# 递归处理字典
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def process_dict(d):
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if isinstance(d, dict):
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for key, value in d.items():
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if key == "title" and isinstance(value, str):
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# 基于全文和当前 title 生成一句话描述
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prompt = f"基于以下内容，为标题 '{value}' 生成一句话描述，言简意赅，不要使用标点符号：\n{json.dumps(json_dict, ensure_ascii=False)}"
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# 调用 AI 生成描述
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=[
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{"role": "system", "content": "你是一个教学经验丰富的基础教育教师"},
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{"role": "user", "content": prompt}
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],
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temperature=0.7
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)
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# 提取生成的描述
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description = response.choices[0].message.content.strip()
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# 去掉标点符号
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description = re.sub(r'[^\w\s]', '', description)
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# 将描述赋值给 text 属性
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d["text"] = description
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elif isinstance(value, dict):
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process_dict(value)
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elif isinstance(value, list):
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for item in value:
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process_dict(item)
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elif isinstance(d, list):
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for item in d:
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process_dict(item)
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# 处理整个字典
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process_dict(json_dict)
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return json_dict
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def convertMarkdownToJson(markdown_content):
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# 创建转换器实例
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converter = MarkdownToJsonConverter(client)
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# 转换 Markdown 为 JSON
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for item in converter.convert_markdown_to_json(markdown_content): # 使用普通 for 循环
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yield item
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yield f"{item}\n" # 每行数据后添加换行符
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# 添加结束标记
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yield '{"type": "end" }'
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yield '{"type": "end" }\n' # 结束标记后也添加换行符
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# 获取本机所有 IPv4 地址
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