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Browse files- README.md +220 -13
- app.py +24 -483
- assets/script.js +39 -0
- assets/style.css +474 -0
- requirements.txt +294 -3
- style.css +48 -0
- visualization.py +1 -1
README.md
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| 1 |
+
# LLM Educational Assistant Platform
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An educational assistant platform built on Gradio, utilizing large language models to help students better understand complex concepts. Through concept decomposition and visualization, it provides personalized learning experiences for students.
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+
## Features
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1. **User Profile Customization**
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- Input grade, subject, and learning needs
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- Receive personalized learning experiences and explanations
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+
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2. **Concept Decomposition and Visualization**
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- Break down complex problems into basic concepts
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- Visualize concept relationships through tree diagrams or network graphs
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- Intuitively display dependencies and connections between concepts
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+
3. **Interactive Learning Experience**
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+
- Click on any sub-concept to get detailed explanations
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- View targeted examples and exercises
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+
- Get recommendations for relevant learning resources
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+
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4. **Progress Saving and Caching**
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- Cache generated concept explanations to improve response speed
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- Save learning records for easy review and revision
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+
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+
## Installation and Running
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+
### Prerequisites
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- Python 3.7+
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- pip (Python package manager)
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+
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+
```angular2html
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conda create -n ai_new_dream python=3.11
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pip install -r requirements.txt
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export OPENAI_API_KEY="your-secret-api-key"
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```
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+
### Automatic Installation and Running
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+
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+
**Linux/Mac:**
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```bash
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# Add execution permission
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chmod +x run.sh
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# Run startup script
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./run.sh
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```
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**Windows:**
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```
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# Double-click to run, or run in command prompt
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run.bat
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```
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### Manual Installation and Running
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```bash
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# Create virtual environment
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python -m venv venv
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# Activate virtual environment
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# On Windows:
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venv\Scripts\activate
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# On Linux/Mac:
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source venv/bin/activate
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# Install dependencies
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pip install -r requirements.txt
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# Run application
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python app.py
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```
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## Project Structure
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```
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.
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├── app.py # Main application file
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├── prompts.py # LLM prompt templates
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├── llm_utils.py # LLM utility functions
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├── visualization.py # Concept graph visualization module
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| 81 |
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├── cache_utils.py # Caching utilities
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├── concept_handler.js # JavaScript for concept click handling
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├── requirements.txt # Project dependencies
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| 84 |
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├── run.sh # Linux/Mac startup script
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└── run.bat # Windows startup script
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```
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## Integrated LLM
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This application is integrated with the OpenAI API using the `gpt4omini` model. The implementation is in the `call_llm` function in the `llm_utils.py` file:
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```python
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def call_llm(prompt: str) -> str:
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"""Call the OpenAI API with gpt4omini model"""
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try:
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from openai import OpenAI
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client = OpenAI(api_key="YOUR_API_KEY")
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response = client.chat.completions.create(
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model="gpt4omini",
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messages=[
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{"role": "system", "content": "You are a helpful education assistant."},
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{"role": "user", "content": prompt}
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]
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)
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return response.choices[0].message.content
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except Exception as e:
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# Fallback to mock data if API call fails
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print(f"Error calling OpenAI API: {e}")
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# ...
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```
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If the API call fails, the system falls back to mock data to demonstrate functionality.
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## Customization and Extension
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- **Adding New Subjects**: Add new subject options in the `subject_input` options in `app.py`
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- **Adjusting Prompt Templates**: Modify the prompt templates in `prompts.py` to implement specific teaching styles or methods
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- **Enhancing Visualization**: Modify the visualization functions in `visualization.py` to implement richer concept graph representations
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+
- **Changing LLM Model**: To use a different model, update the `model` parameter in the `call_llm` function in `llm_utils.py`
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## Contributions and Feedback
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Questions, suggestions, or code contributions are welcome to help improve this educational assistant platform!
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## 教育LLM应用
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这是一个基于大语言模型的教育应用,旨在帮助学生分解和理解复杂的学术概念。
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### 功能特点
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- 根据学生的年级和学科自动调整内容难度
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- 将复杂问题分解为相互关联的子概念
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| 133 |
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- 生成可视化的知识图谱
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- 为每个概念提供详细解释、示例、学习资源和练习题
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### 如何运行
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#### 使用脚本运行(推荐)
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1. 确保您已安装Python 3.7或更高版本
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2. 在终端中导航到项目目录
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对于Mac/Linux用户:
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```bash
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chmod +x run.sh # 添加执行权限
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./run.sh # 运行脚本
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```
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对于Windows用户:
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```
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run.bat
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```
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#### 手动设置
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1. 创建并激活虚拟环境:
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```bash
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python -m venv venv
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source venv/bin/activate # Mac/Linux
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venv\Scripts\activate # Windows
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```
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2. 安装依赖:
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| 164 |
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```bash
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pip install -r requirements.txt
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```
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| 168 |
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3. 启动应用:
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| 169 |
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```bash
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python app.py
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```
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### 配置OpenAI API
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| 174 |
+
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| 175 |
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应用使用OpenAI API进行概念分解和解释。在`config.py`文件中设置您的API密钥:
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```python
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OPENAI_API_KEY = "您的API密钥" # 替换为您的实际API密钥
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| 179 |
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```
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+
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| 181 |
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您也可以调整其他配置参数:
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- `OPENAI_MODEL`: 要使用的OpenAI模型名称
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- `OPENAI_TIMEOUT`: API调用超时时间(秒)
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- `OPENAI_MAX_RETRIES`: 请求失败时的最大重试次数
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| 185 |
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- `DEBUG_MODE`: 是否启用调试输出
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- `USE_FALLBACK_DATA`: API失败时是否使用备用数据
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| 187 |
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- `CACHE_ENABLED`: 是否启用响应缓存
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### 系统架构
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应用由以下主要组件组成:
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1. **app.py** - 主应用文件,包含Gradio界面
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2. **llm_utils.py** - LLM调用和处理函数
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3. **visualization.py** - 知识图谱可视化
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4. **prompts.py** - LLM提示模板
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5. **cache_utils.py** - 响应缓存功能
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6. **config.py** - 应用配置
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7. **concept_handler.py** - 备用模拟数据(API失败时使用)
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### 自定义和扩展
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您可以通过以下方式自定义应用:
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1. **添加新学科**: 扩展prompts.py中的领域特定提示
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2. **调整提示模板**: 修改prompts.py中的系统和用户提示
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3. **增强可视化**: 在visualization.py中调整知识图谱的生成
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4. **更改模型**: 在config.py中指定不同的OpenAI模型
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### 故障排除
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如果遇到连接错误:
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1. 检查您的API密钥是否正确
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2. 确认您的网络连接正常
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3. 检查模型名称是否正确(例如:"gpt-4o-mini")
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4. 查看应用的调试输出(启用DEBUG_MODE)
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+
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### 贡献
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欢迎提交问题报告和拉取请求来改进这个项目。
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app.py
CHANGED
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from fastapi.responses import JSONResponse
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import json
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font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Microsoft YaHei", "WenQuanYi Micro Hei", sans-serif;
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color: #333;
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background-color: #f7f9fc;
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}
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/* Heading styles */
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h1 {
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color: #2c3e50;
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font-weight: 700;
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margin-bottom: 1rem;
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}
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h2, h3, h4 {
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color: #3498db;
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font-weight: 600;
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}
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/* Button styles */
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button.primary {
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background: linear-gradient(135deg, #3498db, #2980b9);
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border: none;
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box-shadow: 0 4px 6px rgba(52, 152, 219, 0.3);
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}
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button.secondary {
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background: #ecf0f1;
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color: #2980b9;
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border: 1px solid #bdc3c7;
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}
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button:hover {
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transform: translateY(-2px);
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box-shadow: 0 6px 8px rgba(0, 0, 0, 0.1);
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transition: all 0.3s ease;
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}
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/* Input fields enhancement */
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input, textarea, select {
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border: 1px solid #ddd;
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border-radius: 8px;
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padding: 10px;
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box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05);
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transition: all 0.3s ease;
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}
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input:focus, textarea:focus, select:focus {
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border-color: #3498db;
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box-shadow: 0 0 0 2px rgba(52, 152, 219, 0.25);
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}
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/* Tab styles */
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.tabs {
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border-bottom: 2px solid #e0e0e0;
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}
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.tab-selected {
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color: #3498db;
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border-bottom: 2px solid #3498db;
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}
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/* Concept card styles */
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.concept-card {
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transition: all 0.3s ease;
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border: 1px solid #e0e0e0;
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border-radius: 12px;
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padding: 16px;
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| 86 |
-
margin-bottom: 16px;
|
| 87 |
-
cursor: pointer;
|
| 88 |
-
background-color: #fff;
|
| 89 |
-
box-shadow: 0 2px 5px rgba(0,0,0,0.05);
|
| 90 |
-
}
|
| 91 |
-
|
| 92 |
-
.concept-card:hover {
|
| 93 |
-
box-shadow: 0 5px 15px rgba(0,0,0,0.1);
|
| 94 |
-
transform: translateY(-3px);
|
| 95 |
-
border-color: #bdc3c7;
|
| 96 |
-
}
|
| 97 |
-
|
| 98 |
-
.selected-card {
|
| 99 |
-
border-color: #3498db;
|
| 100 |
-
background-color: rgba(52, 152, 219, 0.05);
|
| 101 |
-
box-shadow: 0 0 0 2px rgba(52, 152, 219, 0.3);
|
| 102 |
-
}
|
| 103 |
-
|
| 104 |
-
.concept-title {
|
| 105 |
-
font-weight: bold;
|
| 106 |
-
margin-bottom: 8px;
|
| 107 |
-
color: #2c3e50;
|
| 108 |
-
font-size: 1.1em;
|
| 109 |
-
}
|
| 110 |
-
|
| 111 |
-
.concept-desc {
|
| 112 |
-
font-size: 0.95em;
|
| 113 |
-
color: #7f8c8d;
|
| 114 |
-
line-height: 1.5;
|
| 115 |
-
}
|
| 116 |
-
|
| 117 |
-
/* Knowledge graph styles */
|
| 118 |
-
#concept-graph {
|
| 119 |
-
background-color: #fff;
|
| 120 |
-
border-radius: 12px;
|
| 121 |
-
padding: 16px;
|
| 122 |
-
box-shadow: 0 2px 10px rgba(0,0,0,0.05);
|
| 123 |
-
}
|
| 124 |
-
|
| 125 |
-
#concept-graph img {
|
| 126 |
-
max-width: 100%;
|
| 127 |
-
height: auto;
|
| 128 |
-
border-radius: 8px;
|
| 129 |
-
box-shadow: 0 4px 15px rgba(0,0,0,0.1);
|
| 130 |
-
transition: all 0.3s ease;
|
| 131 |
-
}
|
| 132 |
-
|
| 133 |
-
#concept-graph img:hover {
|
| 134 |
-
box-shadow: 0 8px 25px rgba(0,0,0,0.15);
|
| 135 |
-
}
|
| 136 |
-
|
| 137 |
-
/* Example box styles */
|
| 138 |
-
.example-box {
|
| 139 |
-
background-color: #f1f8fe;
|
| 140 |
-
border-left: 4px solid #3498db;
|
| 141 |
-
padding: 15px;
|
| 142 |
-
margin: 15px 0;
|
| 143 |
-
border-radius: 0 8px 8px 0;
|
| 144 |
-
}
|
| 145 |
-
|
| 146 |
-
.example-box h4 {
|
| 147 |
-
margin-top: 0;
|
| 148 |
-
color: #2980b9;
|
| 149 |
-
}
|
| 150 |
-
|
| 151 |
-
/* Resource item styles */
|
| 152 |
-
.resource-item {
|
| 153 |
-
padding: 12px;
|
| 154 |
-
margin: 10px 0;
|
| 155 |
-
border-bottom: 1px dashed #e0e0e0;
|
| 156 |
-
transition: all 0.2s ease;
|
| 157 |
-
}
|
| 158 |
-
|
| 159 |
-
.resource-item:hover {
|
| 160 |
-
background-color: #f9f9f9;
|
| 161 |
-
}
|
| 162 |
-
|
| 163 |
-
/* Details and answers styles */
|
| 164 |
-
details {
|
| 165 |
-
margin: 10px 0;
|
| 166 |
-
padding: 10px;
|
| 167 |
-
border: 1px solid #e0e0e0;
|
| 168 |
-
border-radius: 8px;
|
| 169 |
-
background-color: #f9f9f9;
|
| 170 |
-
}
|
| 171 |
-
|
| 172 |
-
summary {
|
| 173 |
-
cursor: pointer;
|
| 174 |
-
color: #3498db;
|
| 175 |
-
font-weight: 600;
|
| 176 |
-
padding: 5px;
|
| 177 |
-
}
|
| 178 |
-
|
| 179 |
-
summary:hover {
|
| 180 |
-
color: #2980b9;
|
| 181 |
-
}
|
| 182 |
-
|
| 183 |
-
/* Layout container styles */
|
| 184 |
-
.container {
|
| 185 |
-
background-color: #fff;
|
| 186 |
-
border-radius: 12px;
|
| 187 |
-
padding: 20px;
|
| 188 |
-
box-shadow: 0 4px 6px rgba(0,0,0,0.05);
|
| 189 |
-
margin-bottom: 20px;
|
| 190 |
-
}
|
| 191 |
-
|
| 192 |
-
/* Responsive adjustments */
|
| 193 |
-
@media (max-width: 768px) {
|
| 194 |
-
.concept-card {
|
| 195 |
-
padding: 12px;
|
| 196 |
-
}
|
| 197 |
|
| 198 |
-
|
| 199 |
-
|
| 200 |
-
|
| 201 |
-
|
| 202 |
-
|
| 203 |
-
|
| 204 |
-
|
| 205 |
-
|
| 206 |
-
|
| 207 |
-
|
| 208 |
-
|
| 209 |
-
|
| 210 |
-
|
| 211 |
-
|
| 212 |
-
|
| 213 |
-
|
| 214 |
-
margin-bottom: 15px;
|
| 215 |
-
line-height: 1.6;
|
| 216 |
-
color: #2c3e50;
|
| 217 |
-
}
|
| 218 |
-
|
| 219 |
-
.answer-content h4 {
|
| 220 |
-
color: #3498db;
|
| 221 |
-
margin-top: 0;
|
| 222 |
-
margin-bottom: 10px;
|
| 223 |
-
}
|
| 224 |
-
|
| 225 |
-
.main-concept {
|
| 226 |
-
background-color: #e3f2fd;
|
| 227 |
-
padding: 10px 15px;
|
| 228 |
-
border-radius: 8px;
|
| 229 |
-
color: #1976d2;
|
| 230 |
-
font-size: 0.95em;
|
| 231 |
-
}
|
| 232 |
-
|
| 233 |
-
.main-concept strong {
|
| 234 |
-
color: #1565c0;
|
| 235 |
-
}
|
| 236 |
-
|
| 237 |
-
/* Answer section styles */
|
| 238 |
-
.answer-section {
|
| 239 |
-
margin-top: 20px;
|
| 240 |
-
background: white;
|
| 241 |
-
border-radius: 15px;
|
| 242 |
-
padding: 20px;
|
| 243 |
-
}
|
| 244 |
-
|
| 245 |
-
.answer-box {
|
| 246 |
-
background-color: #f8f9fa;
|
| 247 |
-
border-radius: 12px;
|
| 248 |
-
padding: 20px;
|
| 249 |
-
margin: 15px 0;
|
| 250 |
-
border: 1px solid #e9ecef;
|
| 251 |
-
box-shadow: 0 2px 4px rgba(0,0,0,0.05);
|
| 252 |
-
transition: all 0.3s ease;
|
| 253 |
-
}
|
| 254 |
-
|
| 255 |
-
.answer-box:hover {
|
| 256 |
-
box-shadow: 0 4px 8px rgba(0,0,0,0.1);
|
| 257 |
-
}
|
| 258 |
-
|
| 259 |
-
.answer-content {
|
| 260 |
-
margin-bottom: 15px;
|
| 261 |
-
line-height: 1.6;
|
| 262 |
-
color: #2c3e50;
|
| 263 |
-
font-size: 1.1em;
|
| 264 |
-
}
|
| 265 |
-
|
| 266 |
-
.main-concept {
|
| 267 |
-
background-color: #e3f2fd;
|
| 268 |
-
padding: 12px 16px;
|
| 269 |
-
border-radius: 8px;
|
| 270 |
-
color: #1976d2;
|
| 271 |
-
font-size: 0.95em;
|
| 272 |
-
margin-top: 15px;
|
| 273 |
-
border: 1px solid rgba(25, 118, 210, 0.1);
|
| 274 |
-
}
|
| 275 |
-
|
| 276 |
-
.main-concept strong {
|
| 277 |
-
color: #1565c0;
|
| 278 |
-
font-weight: 600;
|
| 279 |
-
}
|
| 280 |
-
|
| 281 |
-
/* Loading animation styles */
|
| 282 |
-
.loading {
|
| 283 |
-
padding: 20px;
|
| 284 |
-
text-align: center;
|
| 285 |
-
color: #666;
|
| 286 |
-
font-size: 1.1em;
|
| 287 |
-
position: relative;
|
| 288 |
-
}
|
| 289 |
-
|
| 290 |
-
.loading:after {
|
| 291 |
-
content: '...';
|
| 292 |
-
position: absolute;
|
| 293 |
-
animation: dots 1.5s steps(5, end) infinite;
|
| 294 |
-
}
|
| 295 |
-
|
| 296 |
-
@keyframes dots {
|
| 297 |
-
0%, 20% { content: '.'; }
|
| 298 |
-
40% { content: '..'; }
|
| 299 |
-
60% { content: '...'; }
|
| 300 |
-
80%, 100% { content: ''; }
|
| 301 |
-
}
|
| 302 |
-
|
| 303 |
-
.loading::before {
|
| 304 |
-
content: '';
|
| 305 |
-
display: block;
|
| 306 |
-
width: 30px;
|
| 307 |
-
height: 30px;
|
| 308 |
-
border: 3px solid #3498db;
|
| 309 |
-
border-top-color: transparent;
|
| 310 |
-
border-radius: 50%;
|
| 311 |
-
margin: 0 auto 10px;
|
| 312 |
-
animation: spin 1s linear infinite;
|
| 313 |
-
}
|
| 314 |
-
|
| 315 |
-
@keyframes spin {
|
| 316 |
-
to { transform: rotate(360deg); }
|
| 317 |
-
}
|
| 318 |
-
|
| 319 |
-
/* 概念详解面板样式 */
|
| 320 |
-
.concept-explanation {
|
| 321 |
-
padding: 20px;
|
| 322 |
-
background: white;
|
| 323 |
-
border-radius: 12px;
|
| 324 |
-
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
|
| 325 |
-
}
|
| 326 |
-
|
| 327 |
-
.explanation-content {
|
| 328 |
-
line-height: 1.6;
|
| 329 |
-
margin: 15px 0;
|
| 330 |
-
color: #2c3e50;
|
| 331 |
-
}
|
| 332 |
-
|
| 333 |
-
.examples-section {
|
| 334 |
-
margin-top: 20px;
|
| 335 |
-
}
|
| 336 |
-
|
| 337 |
-
.example-box {
|
| 338 |
-
background: #f8f9fa;
|
| 339 |
-
border-left: 4px solid #3498db;
|
| 340 |
-
padding: 15px;
|
| 341 |
-
margin: 15px 0;
|
| 342 |
-
border-radius: 0 8px 8px 0;
|
| 343 |
-
}
|
| 344 |
-
|
| 345 |
-
.example-problem {
|
| 346 |
-
margin-bottom: 10px;
|
| 347 |
-
color: #2c3e50;
|
| 348 |
-
}
|
| 349 |
-
|
| 350 |
-
.example-solution {
|
| 351 |
-
color: #34495e;
|
| 352 |
-
padding: 10px;
|
| 353 |
-
background: rgba(52, 152, 219, 0.05);
|
| 354 |
-
border-radius: 4px;
|
| 355 |
-
}
|
| 356 |
-
|
| 357 |
-
/* 加载动画样式 */
|
| 358 |
-
.loading {
|
| 359 |
-
text-align: center;
|
| 360 |
-
padding: 20px;
|
| 361 |
-
}
|
| 362 |
-
|
| 363 |
-
.loading-spinner {
|
| 364 |
-
border: 3px solid #f3f3f3;
|
| 365 |
-
border-top: 3px solid #3498db;
|
| 366 |
-
border-radius: 50%;
|
| 367 |
-
width: 30px;
|
| 368 |
-
height: 30px;
|
| 369 |
-
animation: spin 1s linear infinite;
|
| 370 |
-
margin: 0 auto 10px;
|
| 371 |
-
}
|
| 372 |
-
|
| 373 |
-
@keyframes spin {
|
| 374 |
-
0% { transform: rotate(0deg); }
|
| 375 |
-
100% { transform: rotate(360deg); }
|
| 376 |
-
}
|
| 377 |
-
|
| 378 |
-
.loading-text {
|
| 379 |
-
color: #666;
|
| 380 |
-
font-size: 0.9em;
|
| 381 |
-
}
|
| 382 |
-
|
| 383 |
-
/* 新增的样式 */
|
| 384 |
-
.concept-explanation-container {
|
| 385 |
-
margin-top: 20px;
|
| 386 |
-
padding: 15px;
|
| 387 |
-
background: white;
|
| 388 |
-
border-radius: 12px;
|
| 389 |
-
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
|
| 390 |
-
transition: all 0.3s ease;
|
| 391 |
-
max-height: 600px; /* 固定最大高度 */
|
| 392 |
-
overflow-y: auto; /* 添加垂直滚动条 */
|
| 393 |
-
|
| 394 |
-
/* 自定义滚动条样式 */
|
| 395 |
-
scrollbar-width: thin;
|
| 396 |
-
scrollbar-color: #3498db #f1f1f1;
|
| 397 |
-
}
|
| 398 |
-
|
| 399 |
-
/* Webkit浏览器的滚动条样式 */
|
| 400 |
-
.concept-explanation-container::-webkit-scrollbar {
|
| 401 |
-
width: 8px;
|
| 402 |
-
}
|
| 403 |
-
|
| 404 |
-
.concept-explanation-container::-webkit-scrollbar-track {
|
| 405 |
-
background: #f1f1f1;
|
| 406 |
-
border-radius: 4px;
|
| 407 |
-
}
|
| 408 |
-
|
| 409 |
-
.concept-explanation-container::-webkit-scrollbar-thumb {
|
| 410 |
-
background: #3498db;
|
| 411 |
-
border-radius: 4px;
|
| 412 |
-
}
|
| 413 |
-
|
| 414 |
-
.concept-explanation-container::-webkit-scrollbar-thumb:hover {
|
| 415 |
-
background: #2980b9;
|
| 416 |
-
}
|
| 417 |
-
|
| 418 |
-
.concept-explanation-container h3 {
|
| 419 |
-
color: #2c3e50;
|
| 420 |
-
margin-bottom: 15px;
|
| 421 |
-
position: sticky;
|
| 422 |
-
top: 0;
|
| 423 |
-
background: white;
|
| 424 |
-
padding: 10px 0;
|
| 425 |
-
z-index: 1;
|
| 426 |
-
}
|
| 427 |
-
|
| 428 |
-
/* 内容区域样式 */
|
| 429 |
-
.card-explanation {
|
| 430 |
-
padding: 20px;
|
| 431 |
-
background: white;
|
| 432 |
-
border-radius: 12px;
|
| 433 |
-
}
|
| 434 |
-
|
| 435 |
-
.explanation-section, .key-points-section, .examples-section,
|
| 436 |
-
.practice-section, .resources-section {
|
| 437 |
-
margin-top: 20px;
|
| 438 |
-
padding: 15px;
|
| 439 |
-
background: #f8f9fa;
|
| 440 |
-
border-radius: 8px;
|
| 441 |
-
}
|
| 442 |
-
|
| 443 |
-
.explanation-section h4, .key-points-section h4,
|
| 444 |
-
.examples-section h4, .practice-section h4,
|
| 445 |
-
.resources-section h4 {
|
| 446 |
-
color: #2c3e50;
|
| 447 |
-
margin-bottom: 10px;
|
| 448 |
-
}
|
| 449 |
-
|
| 450 |
-
/* 修改generate_card_explanation函数中的标题文本 */
|
| 451 |
-
.explanation-section h4:before { content: "📚 Concept Explanation"; }
|
| 452 |
-
.key-points-section h4:before { content: "🎯 Key Points"; }
|
| 453 |
-
.examples-section h4:before { content: "📝 Example Analysis"; }
|
| 454 |
-
.practice-section h4:before { content: "✍️ Practice Problems"; }
|
| 455 |
-
.resources-section h4:before { content: "📚 Learning Resources"; }
|
| 456 |
-
|
| 457 |
-
/* 加载动画容器 */
|
| 458 |
-
.loading-container {
|
| 459 |
-
display: flex;
|
| 460 |
-
flex-direction: column;
|
| 461 |
-
align-items: center;
|
| 462 |
-
justify-content: center;
|
| 463 |
-
padding: 40px;
|
| 464 |
-
background: white;
|
| 465 |
-
border-radius: 12px;
|
| 466 |
-
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
|
| 467 |
-
}
|
| 468 |
-
|
| 469 |
-
/* 加载动画旋转器 */
|
| 470 |
-
.loading-spinner {
|
| 471 |
-
width: 50px;
|
| 472 |
-
height: 50px;
|
| 473 |
-
border: 4px solid #f3f3f3;
|
| 474 |
-
border-top: 4px solid #3498db;
|
| 475 |
-
border-radius: 50%;
|
| 476 |
-
animation: spin 1s linear infinite;
|
| 477 |
-
margin-bottom: 20px;
|
| 478 |
-
}
|
| 479 |
-
|
| 480 |
-
/* 加载文本 */
|
| 481 |
-
.loading-text {
|
| 482 |
-
color: #666;
|
| 483 |
-
font-size: 1.1em;
|
| 484 |
-
margin-top: 10px;
|
| 485 |
-
}
|
| 486 |
|
| 487 |
-
|
| 488 |
-
|
| 489 |
-
|
| 490 |
-
100% { transform: rotate(360deg); }
|
| 491 |
-
}
|
| 492 |
-
"""
|
| 493 |
|
| 494 |
# Custom JavaScript code
|
| 495 |
custom_js = """
|
|
@@ -1698,11 +1246,4 @@ def create_interface():
|
|
| 1698 |
if __name__ == "__main__":
|
| 1699 |
demo = create_interface()
|
| 1700 |
|
| 1701 |
-
|
| 1702 |
-
if os.getenv("SPACE_ID"):
|
| 1703 |
-
# Hugging Face空间环境
|
| 1704 |
-
demo.launch()
|
| 1705 |
-
else:
|
| 1706 |
-
# 本地环境
|
| 1707 |
-
import uvicorn
|
| 1708 |
-
uvicorn.run(app, host="0.0.0.0", port=7861)
|
|
|
|
| 13 |
from fastapi.responses import JSONResponse
|
| 14 |
import json
|
| 15 |
|
| 16 |
+
def ensure_assets_directory():
|
| 17 |
+
"""确保assets目录存在并包含必要的静态文件"""
|
| 18 |
+
# 创建assets目录
|
| 19 |
+
os.makedirs("assets", exist_ok=True)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 20 |
|
| 21 |
+
# 将CSS内容写入文件
|
| 22 |
+
css_path = os.path.join("assets", "style.css")
|
| 23 |
+
if not os.path.exists(css_path):
|
| 24 |
+
with open(css_path, "w", encoding="utf-8") as f:
|
| 25 |
+
f.write(custom_css) # 使用之前定义的custom_css内容
|
| 26 |
+
|
| 27 |
+
# 读取CSS文件
|
| 28 |
+
def load_css():
|
| 29 |
+
"""读取CSS文件内容"""
|
| 30 |
+
css_path = os.path.join("assets", "style.css")
|
| 31 |
+
try:
|
| 32 |
+
with open(css_path, "r", encoding="utf-8") as f:
|
| 33 |
+
return f.read()
|
| 34 |
+
except Exception as e:
|
| 35 |
+
print(f"读取CSS文件失败: {str(e)}")
|
| 36 |
+
return "" # 如果读取失败,返回空字符串
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 37 |
|
| 38 |
+
# 在应用启动时初始化
|
| 39 |
+
ensure_assets_directory()
|
| 40 |
+
custom_css = load_css()
|
|
|
|
|
|
|
|
|
|
| 41 |
|
| 42 |
# Custom JavaScript code
|
| 43 |
custom_js = """
|
|
|
|
| 1246 |
if __name__ == "__main__":
|
| 1247 |
demo = create_interface()
|
| 1248 |
|
| 1249 |
+
demo.launch()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
assets/script.js
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
// Handle concept card clicks
|
| 2 |
+
function conceptClick(conceptId) {
|
| 3 |
+
// Find the hidden input field and update its value
|
| 4 |
+
const conceptSelection = document.getElementById('concept-selection');
|
| 5 |
+
if (conceptSelection) {
|
| 6 |
+
conceptSelection.value = conceptId;
|
| 7 |
+
conceptSelection.dispatchEvent(new Event('input', { bubbles: true }));
|
| 8 |
+
|
| 9 |
+
// Highlight the selected card
|
| 10 |
+
document.querySelectorAll('.concept-card').forEach(card => {
|
| 11 |
+
card.classList.remove('selected-card');
|
| 12 |
+
if (card.getAttribute('data-concept-id') === conceptId) {
|
| 13 |
+
card.classList.add('selected-card');
|
| 14 |
+
}
|
| 15 |
+
});
|
| 16 |
+
}
|
| 17 |
+
}
|
| 18 |
+
|
| 19 |
+
// Enhance image display after loading
|
| 20 |
+
document.addEventListener('DOMContentLoaded', function() {
|
| 21 |
+
const graphContainer = document.getElementById('concept-graph');
|
| 22 |
+
if (graphContainer) {
|
| 23 |
+
const observer = new MutationObserver(function(mutations) {
|
| 24 |
+
mutations.forEach(function(mutation) {
|
| 25 |
+
if (mutation.addedNodes && mutation.addedNodes.length > 0) {
|
| 26 |
+
const img = graphContainer.querySelector('img');
|
| 27 |
+
if (img) {
|
| 28 |
+
img.style.maxWidth = '100%';
|
| 29 |
+
img.style.height = 'auto';
|
| 30 |
+
img.style.borderRadius = '8px';
|
| 31 |
+
img.style.boxShadow = '0 4px 8px rgba(0,0,0,0.1)';
|
| 32 |
+
}
|
| 33 |
+
}
|
| 34 |
+
});
|
| 35 |
+
});
|
| 36 |
+
|
| 37 |
+
observer.observe(graphContainer, { childList: true, subtree: true });
|
| 38 |
+
}
|
| 39 |
+
});
|
assets/style.css
ADDED
|
@@ -0,0 +1,474 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
| 1 |
+
/* Global styles */
|
| 2 |
+
body {
|
| 3 |
+
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Microsoft YaHei", "WenQuanYi Micro Hei", sans-serif;
|
| 4 |
+
color: #333;
|
| 5 |
+
background-color: #f7f9fc;
|
| 6 |
+
}
|
| 7 |
+
|
| 8 |
+
/* Heading styles */
|
| 9 |
+
h1 {
|
| 10 |
+
color: #2c3e50;
|
| 11 |
+
font-weight: 700;
|
| 12 |
+
margin-bottom: 1rem;
|
| 13 |
+
}
|
| 14 |
+
|
| 15 |
+
h2, h3, h4 {
|
| 16 |
+
color: #3498db;
|
| 17 |
+
font-weight: 600;
|
| 18 |
+
}
|
| 19 |
+
|
| 20 |
+
/* Button styles */
|
| 21 |
+
button.primary {
|
| 22 |
+
background: linear-gradient(135deg, #3498db, #2980b9);
|
| 23 |
+
border: none;
|
| 24 |
+
box-shadow: 0 4px 6px rgba(52, 152, 219, 0.3);
|
| 25 |
+
}
|
| 26 |
+
|
| 27 |
+
button.secondary {
|
| 28 |
+
background: #ecf0f1;
|
| 29 |
+
color: #2980b9;
|
| 30 |
+
border: 1px solid #bdc3c7;
|
| 31 |
+
}
|
| 32 |
+
|
| 33 |
+
button:hover {
|
| 34 |
+
transform: translateY(-2px);
|
| 35 |
+
box-shadow: 0 6px 8px rgba(0, 0, 0, 0.1);
|
| 36 |
+
transition: all 0.3s ease;
|
| 37 |
+
}
|
| 38 |
+
|
| 39 |
+
/* Input fields enhancement */
|
| 40 |
+
input, textarea, select {
|
| 41 |
+
border: 1px solid #ddd;
|
| 42 |
+
border-radius: 8px;
|
| 43 |
+
padding: 10px;
|
| 44 |
+
box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.05);
|
| 45 |
+
transition: all 0.3s ease;
|
| 46 |
+
}
|
| 47 |
+
|
| 48 |
+
input:focus, textarea:focus, select:focus {
|
| 49 |
+
border-color: #3498db;
|
| 50 |
+
box-shadow: 0 0 0 2px rgba(52, 152, 219, 0.25);
|
| 51 |
+
}
|
| 52 |
+
|
| 53 |
+
/* Tab styles */
|
| 54 |
+
.tabs {
|
| 55 |
+
border-bottom: 2px solid #e0e0e0;
|
| 56 |
+
}
|
| 57 |
+
|
| 58 |
+
.tab-selected {
|
| 59 |
+
color: #3498db;
|
| 60 |
+
border-bottom: 2px solid #3498db;
|
| 61 |
+
}
|
| 62 |
+
|
| 63 |
+
/* Concept card styles */
|
| 64 |
+
.concept-card {
|
| 65 |
+
transition: all 0.3s ease;
|
| 66 |
+
border: 1px solid #e0e0e0;
|
| 67 |
+
border-radius: 12px;
|
| 68 |
+
padding: 16px;
|
| 69 |
+
margin-bottom: 16px;
|
| 70 |
+
cursor: pointer;
|
| 71 |
+
background-color: #fff;
|
| 72 |
+
box-shadow: 0 2px 5px rgba(0,0,0,0.05);
|
| 73 |
+
}
|
| 74 |
+
|
| 75 |
+
.concept-card:hover {
|
| 76 |
+
box-shadow: 0 5px 15px rgba(0,0,0,0.1);
|
| 77 |
+
transform: translateY(-3px);
|
| 78 |
+
border-color: #bdc3c7;
|
| 79 |
+
}
|
| 80 |
+
|
| 81 |
+
.selected-card {
|
| 82 |
+
border-color: #3498db;
|
| 83 |
+
background-color: rgba(52, 152, 219, 0.05);
|
| 84 |
+
box-shadow: 0 0 0 2px rgba(52, 152, 219, 0.3);
|
| 85 |
+
}
|
| 86 |
+
|
| 87 |
+
.concept-title {
|
| 88 |
+
font-weight: bold;
|
| 89 |
+
margin-bottom: 8px;
|
| 90 |
+
color: #2c3e50;
|
| 91 |
+
font-size: 1.1em;
|
| 92 |
+
}
|
| 93 |
+
|
| 94 |
+
.concept-desc {
|
| 95 |
+
font-size: 0.95em;
|
| 96 |
+
color: #7f8c8d;
|
| 97 |
+
line-height: 1.5;
|
| 98 |
+
}
|
| 99 |
+
|
| 100 |
+
/* Knowledge graph styles */
|
| 101 |
+
#concept-graph {
|
| 102 |
+
background-color: #fff;
|
| 103 |
+
border-radius: 12px;
|
| 104 |
+
padding: 16px;
|
| 105 |
+
box-shadow: 0 2px 10px rgba(0,0,0,0.05);
|
| 106 |
+
}
|
| 107 |
+
|
| 108 |
+
#concept-graph img {
|
| 109 |
+
max-width: 100%;
|
| 110 |
+
height: auto;
|
| 111 |
+
border-radius: 8px;
|
| 112 |
+
box-shadow: 0 4px 15px rgba(0,0,0,0.1);
|
| 113 |
+
transition: all 0.3s ease;
|
| 114 |
+
}
|
| 115 |
+
|
| 116 |
+
#concept-graph img:hover {
|
| 117 |
+
box-shadow: 0 8px 25px rgba(0,0,0,0.15);
|
| 118 |
+
}
|
| 119 |
+
|
| 120 |
+
/* Example box styles */
|
| 121 |
+
.example-box {
|
| 122 |
+
background-color: #f1f8fe;
|
| 123 |
+
border-left: 4px solid #3498db;
|
| 124 |
+
padding: 15px;
|
| 125 |
+
margin: 15px 0;
|
| 126 |
+
border-radius: 0 8px 8px 0;
|
| 127 |
+
}
|
| 128 |
+
|
| 129 |
+
.example-box h4 {
|
| 130 |
+
margin-top: 0;
|
| 131 |
+
color: #2980b9;
|
| 132 |
+
}
|
| 133 |
+
|
| 134 |
+
/* Resource item styles */
|
| 135 |
+
.resource-item {
|
| 136 |
+
padding: 12px;
|
| 137 |
+
margin: 10px 0;
|
| 138 |
+
border-bottom: 1px dashed #e0e0e0;
|
| 139 |
+
transition: all 0.2s ease;
|
| 140 |
+
}
|
| 141 |
+
|
| 142 |
+
.resource-item:hover {
|
| 143 |
+
background-color: #f9f9f9;
|
| 144 |
+
}
|
| 145 |
+
|
| 146 |
+
/* Details and answers styles */
|
| 147 |
+
details {
|
| 148 |
+
margin: 10px 0;
|
| 149 |
+
padding: 10px;
|
| 150 |
+
border: 1px solid #e0e0e0;
|
| 151 |
+
border-radius: 8px;
|
| 152 |
+
background-color: #f9f9f9;
|
| 153 |
+
}
|
| 154 |
+
|
| 155 |
+
summary {
|
| 156 |
+
cursor: pointer;
|
| 157 |
+
color: #3498db;
|
| 158 |
+
font-weight: 600;
|
| 159 |
+
padding: 5px;
|
| 160 |
+
}
|
| 161 |
+
|
| 162 |
+
summary:hover {
|
| 163 |
+
color: #2980b9;
|
| 164 |
+
}
|
| 165 |
+
|
| 166 |
+
/* Layout container styles */
|
| 167 |
+
.container {
|
| 168 |
+
background-color: #fff;
|
| 169 |
+
border-radius: 12px;
|
| 170 |
+
padding: 20px;
|
| 171 |
+
box-shadow: 0 4px 6px rgba(0,0,0,0.05);
|
| 172 |
+
margin-bottom: 20px;
|
| 173 |
+
}
|
| 174 |
+
|
| 175 |
+
/* Responsive adjustments */
|
| 176 |
+
@media (max-width: 768px) {
|
| 177 |
+
.concept-card {
|
| 178 |
+
padding: 12px;
|
| 179 |
+
}
|
| 180 |
+
|
| 181 |
+
.example-box {
|
| 182 |
+
padding: 12px;
|
| 183 |
+
}
|
| 184 |
+
}
|
| 185 |
+
|
| 186 |
+
/* Answer box styles */
|
| 187 |
+
.answer-box {
|
| 188 |
+
background-color: #f8f9fa;
|
| 189 |
+
border-radius: 12px;
|
| 190 |
+
padding: 20px;
|
| 191 |
+
margin: 15px 0;
|
| 192 |
+
border: 1px solid #e9ecef;
|
| 193 |
+
box-shadow: 0 2px 4px rgba(0,0,0,0.05);
|
| 194 |
+
}
|
| 195 |
+
|
| 196 |
+
.answer-content {
|
| 197 |
+
margin-bottom: 15px;
|
| 198 |
+
line-height: 1.6;
|
| 199 |
+
color: #2c3e50;
|
| 200 |
+
}
|
| 201 |
+
|
| 202 |
+
.answer-content h4 {
|
| 203 |
+
color: #3498db;
|
| 204 |
+
margin-top: 0;
|
| 205 |
+
margin-bottom: 10px;
|
| 206 |
+
}
|
| 207 |
+
|
| 208 |
+
.main-concept {
|
| 209 |
+
background-color: #e3f2fd;
|
| 210 |
+
padding: 10px 15px;
|
| 211 |
+
border-radius: 8px;
|
| 212 |
+
color: #1976d2;
|
| 213 |
+
font-size: 0.95em;
|
| 214 |
+
}
|
| 215 |
+
|
| 216 |
+
.main-concept strong {
|
| 217 |
+
color: #1565c0;
|
| 218 |
+
}
|
| 219 |
+
|
| 220 |
+
/* Answer section styles */
|
| 221 |
+
.answer-section {
|
| 222 |
+
margin-top: 20px;
|
| 223 |
+
background: white;
|
| 224 |
+
border-radius: 15px;
|
| 225 |
+
padding: 20px;
|
| 226 |
+
}
|
| 227 |
+
|
| 228 |
+
.answer-box {
|
| 229 |
+
background-color: #f8f9fa;
|
| 230 |
+
border-radius: 12px;
|
| 231 |
+
padding: 20px;
|
| 232 |
+
margin: 15px 0;
|
| 233 |
+
border: 1px solid #e9ecef;
|
| 234 |
+
box-shadow: 0 2px 4px rgba(0,0,0,0.05);
|
| 235 |
+
transition: all 0.3s ease;
|
| 236 |
+
}
|
| 237 |
+
|
| 238 |
+
.answer-box:hover {
|
| 239 |
+
box-shadow: 0 4px 8px rgba(0,0,0,0.1);
|
| 240 |
+
}
|
| 241 |
+
|
| 242 |
+
.answer-content {
|
| 243 |
+
margin-bottom: 15px;
|
| 244 |
+
line-height: 1.6;
|
| 245 |
+
color: #2c3e50;
|
| 246 |
+
font-size: 1.1em;
|
| 247 |
+
}
|
| 248 |
+
|
| 249 |
+
.main-concept {
|
| 250 |
+
background-color: #e3f2fd;
|
| 251 |
+
padding: 12px 16px;
|
| 252 |
+
border-radius: 8px;
|
| 253 |
+
color: #1976d2;
|
| 254 |
+
font-size: 0.95em;
|
| 255 |
+
margin-top: 15px;
|
| 256 |
+
border: 1px solid rgba(25, 118, 210, 0.1);
|
| 257 |
+
}
|
| 258 |
+
|
| 259 |
+
.main-concept strong {
|
| 260 |
+
color: #1565c0;
|
| 261 |
+
font-weight: 600;
|
| 262 |
+
}
|
| 263 |
+
|
| 264 |
+
/* Loading animation styles */
|
| 265 |
+
.loading {
|
| 266 |
+
padding: 20px;
|
| 267 |
+
text-align: center;
|
| 268 |
+
color: #666;
|
| 269 |
+
font-size: 1.1em;
|
| 270 |
+
position: relative;
|
| 271 |
+
}
|
| 272 |
+
|
| 273 |
+
.loading:after {
|
| 274 |
+
content: '...';
|
| 275 |
+
position: absolute;
|
| 276 |
+
animation: dots 1.5s steps(5, end) infinite;
|
| 277 |
+
}
|
| 278 |
+
|
| 279 |
+
@keyframes dots {
|
| 280 |
+
0%, 20% { content: '.'; }
|
| 281 |
+
40% { content: '..'; }
|
| 282 |
+
60% { content: '...'; }
|
| 283 |
+
80%, 100% { content: ''; }
|
| 284 |
+
}
|
| 285 |
+
|
| 286 |
+
.loading::before {
|
| 287 |
+
content: '';
|
| 288 |
+
display: block;
|
| 289 |
+
width: 30px;
|
| 290 |
+
height: 30px;
|
| 291 |
+
border: 3px solid #3498db;
|
| 292 |
+
border-top-color: transparent;
|
| 293 |
+
border-radius: 50%;
|
| 294 |
+
margin: 0 auto 10px;
|
| 295 |
+
animation: spin 1s linear infinite;
|
| 296 |
+
}
|
| 297 |
+
|
| 298 |
+
@keyframes spin {
|
| 299 |
+
to { transform: rotate(360deg); }
|
| 300 |
+
}
|
| 301 |
+
|
| 302 |
+
/* 概念详解面板样式 */
|
| 303 |
+
.concept-explanation {
|
| 304 |
+
padding: 20px;
|
| 305 |
+
background: white;
|
| 306 |
+
border-radius: 12px;
|
| 307 |
+
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
|
| 308 |
+
}
|
| 309 |
+
|
| 310 |
+
.explanation-content {
|
| 311 |
+
line-height: 1.6;
|
| 312 |
+
margin: 15px 0;
|
| 313 |
+
color: #2c3e50;
|
| 314 |
+
}
|
| 315 |
+
|
| 316 |
+
.examples-section {
|
| 317 |
+
margin-top: 20px;
|
| 318 |
+
}
|
| 319 |
+
|
| 320 |
+
.example-box {
|
| 321 |
+
background: #f8f9fa;
|
| 322 |
+
border-left: 4px solid #3498db;
|
| 323 |
+
padding: 15px;
|
| 324 |
+
margin: 15px 0;
|
| 325 |
+
border-radius: 0 8px 8px 0;
|
| 326 |
+
}
|
| 327 |
+
|
| 328 |
+
.example-problem {
|
| 329 |
+
margin-bottom: 10px;
|
| 330 |
+
color: #2c3e50;
|
| 331 |
+
}
|
| 332 |
+
|
| 333 |
+
.example-solution {
|
| 334 |
+
color: #34495e;
|
| 335 |
+
padding: 10px;
|
| 336 |
+
background: rgba(52, 152, 219, 0.05);
|
| 337 |
+
border-radius: 4px;
|
| 338 |
+
}
|
| 339 |
+
|
| 340 |
+
/* 加载动画样式 */
|
| 341 |
+
.loading {
|
| 342 |
+
text-align: center;
|
| 343 |
+
padding: 20px;
|
| 344 |
+
}
|
| 345 |
+
|
| 346 |
+
.loading-spinner {
|
| 347 |
+
border: 3px solid #f3f3f3;
|
| 348 |
+
border-top: 3px solid #3498db;
|
| 349 |
+
border-radius: 50%;
|
| 350 |
+
width: 30px;
|
| 351 |
+
height: 30px;
|
| 352 |
+
animation: spin 1s linear infinite;
|
| 353 |
+
margin: 0 auto 10px;
|
| 354 |
+
}
|
| 355 |
+
|
| 356 |
+
@keyframes spin {
|
| 357 |
+
0% { transform: rotate(0deg); }
|
| 358 |
+
100% { transform: rotate(360deg); }
|
| 359 |
+
}
|
| 360 |
+
|
| 361 |
+
.loading-text {
|
| 362 |
+
color: #666;
|
| 363 |
+
font-size: 0.9em;
|
| 364 |
+
}
|
| 365 |
+
|
| 366 |
+
/* 新增的样式 */
|
| 367 |
+
.concept-explanation-container {
|
| 368 |
+
margin-top: 20px;
|
| 369 |
+
padding: 15px;
|
| 370 |
+
background: white;
|
| 371 |
+
border-radius: 12px;
|
| 372 |
+
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
|
| 373 |
+
transition: all 0.3s ease;
|
| 374 |
+
max-height: 600px; /* 固定最大高度 */
|
| 375 |
+
overflow-y: auto; /* 添加垂直滚动条 */
|
| 376 |
+
|
| 377 |
+
/* 自定义滚动条样式 */
|
| 378 |
+
scrollbar-width: thin;
|
| 379 |
+
scrollbar-color: #3498db #f1f1f1;
|
| 380 |
+
}
|
| 381 |
+
|
| 382 |
+
/* Webkit浏览器的滚动条样式 */
|
| 383 |
+
.concept-explanation-container::-webkit-scrollbar {
|
| 384 |
+
width: 8px;
|
| 385 |
+
}
|
| 386 |
+
|
| 387 |
+
.concept-explanation-container::-webkit-scrollbar-track {
|
| 388 |
+
background: #f1f1f1;
|
| 389 |
+
border-radius: 4px;
|
| 390 |
+
}
|
| 391 |
+
|
| 392 |
+
.concept-explanation-container::-webkit-scrollbar-thumb {
|
| 393 |
+
background: #3498db;
|
| 394 |
+
border-radius: 4px;
|
| 395 |
+
}
|
| 396 |
+
|
| 397 |
+
.concept-explanation-container::-webkit-scrollbar-thumb:hover {
|
| 398 |
+
background: #2980b9;
|
| 399 |
+
}
|
| 400 |
+
|
| 401 |
+
.concept-explanation-container h3 {
|
| 402 |
+
color: #2c3e50;
|
| 403 |
+
margin-bottom: 15px;
|
| 404 |
+
position: sticky;
|
| 405 |
+
top: 0;
|
| 406 |
+
background: white;
|
| 407 |
+
padding: 10px 0;
|
| 408 |
+
z-index: 1;
|
| 409 |
+
}
|
| 410 |
+
|
| 411 |
+
/* 内容区域样式 */
|
| 412 |
+
.card-explanation {
|
| 413 |
+
padding: 20px;
|
| 414 |
+
background: white;
|
| 415 |
+
border-radius: 12px;
|
| 416 |
+
}
|
| 417 |
+
|
| 418 |
+
.explanation-section, .key-points-section, .examples-section,
|
| 419 |
+
.practice-section, .resources-section {
|
| 420 |
+
margin-top: 20px;
|
| 421 |
+
padding: 15px;
|
| 422 |
+
background: #f8f9fa;
|
| 423 |
+
border-radius: 8px;
|
| 424 |
+
}
|
| 425 |
+
|
| 426 |
+
.explanation-section h4, .key-points-section h4,
|
| 427 |
+
.examples-section h4, .practice-section h4,
|
| 428 |
+
.resources-section h4 {
|
| 429 |
+
color: #2c3e50;
|
| 430 |
+
margin-bottom: 10px;
|
| 431 |
+
}
|
| 432 |
+
|
| 433 |
+
/* 修改generate_card_explanation函数中的标题文本 */
|
| 434 |
+
.explanation-section h4:before { content: "📚 Concept Explanation"; }
|
| 435 |
+
.key-points-section h4:before { content: "🎯 Key Points"; }
|
| 436 |
+
.examples-section h4:before { content: "📝 Example Analysis"; }
|
| 437 |
+
.practice-section h4:before { content: "✍️ Practice Problems"; }
|
| 438 |
+
.resources-section h4:before { content: "📚 Learning Resources"; }
|
| 439 |
+
|
| 440 |
+
/* 加载动画容器 */
|
| 441 |
+
.loading-container {
|
| 442 |
+
display: flex;
|
| 443 |
+
flex-direction: column;
|
| 444 |
+
align-items: center;
|
| 445 |
+
justify-content: center;
|
| 446 |
+
padding: 40px;
|
| 447 |
+
background: white;
|
| 448 |
+
border-radius: 12px;
|
| 449 |
+
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
|
| 450 |
+
}
|
| 451 |
+
|
| 452 |
+
/* 加载动画旋转器 */
|
| 453 |
+
.loading-spinner {
|
| 454 |
+
width: 50px;
|
| 455 |
+
height: 50px;
|
| 456 |
+
border: 4px solid #f3f3f3;
|
| 457 |
+
border-top: 4px solid #3498db;
|
| 458 |
+
border-radius: 50%;
|
| 459 |
+
animation: spin 1s linear infinite;
|
| 460 |
+
margin-bottom: 20px;
|
| 461 |
+
}
|
| 462 |
+
|
| 463 |
+
/* 加载文本 */
|
| 464 |
+
.loading-text {
|
| 465 |
+
color: #666;
|
| 466 |
+
font-size: 1.1em;
|
| 467 |
+
margin-top: 10px;
|
| 468 |
+
}
|
| 469 |
+
|
| 470 |
+
/* 旋转动画 */
|
| 471 |
+
@keyframes spin {
|
| 472 |
+
0% { transform: rotate(0deg); }
|
| 473 |
+
100% { transform: rotate(360deg); }
|
| 474 |
+
}
|
requirements.txt
CHANGED
|
@@ -1,8 +1,299 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
fastapi==0.115.12
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
gradio==5.23.1
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 |
langchain==0.0.352
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
matplotlib==3.7.3
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 5 |
networkx==3.1
|
| 6 |
-
|
| 7 |
-
|
| 8 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
accelerate==0.25.0
|
| 2 |
+
acres==0.1.0
|
| 3 |
+
aiofiles==23.2.1
|
| 4 |
+
aiohttp==3.9.1
|
| 5 |
+
aiosignal==1.3.1
|
| 6 |
+
altair==5.5.0
|
| 7 |
+
amightygirl.paapi5-python-sdk==1.0.0
|
| 8 |
+
annotated-types==0.6.0
|
| 9 |
+
anyio==3.7.1
|
| 10 |
+
anytree==2.12.1
|
| 11 |
+
APScheduler==3.10.4
|
| 12 |
+
asttokens==3.0.0
|
| 13 |
+
async-timeout==4.0.3
|
| 14 |
+
attrs==23.1.0
|
| 15 |
+
Authlib==1.3.2
|
| 16 |
+
backcall==0.2.0
|
| 17 |
+
bcrypt==4.2.0
|
| 18 |
+
beautifulsoup4==4.12.2
|
| 19 |
+
BingImageCreator==0.5.0
|
| 20 |
+
bleach==6.2.0
|
| 21 |
+
blinker==1.8.2
|
| 22 |
+
blis==1.0.1
|
| 23 |
+
boto3==1.35.52
|
| 24 |
+
botocore==1.35.52
|
| 25 |
+
cachelib==0.9.0
|
| 26 |
+
catalogue==2.0.10
|
| 27 |
+
certifi==2024.8.30
|
| 28 |
+
cffi==1.16.0
|
| 29 |
+
chardet==5.2.0
|
| 30 |
+
charset-normalizer==3.3.2
|
| 31 |
+
ci-info==0.3.0
|
| 32 |
+
click==8.1.7
|
| 33 |
+
cloudpathlib==0.20.0
|
| 34 |
+
command-not-found==0.3
|
| 35 |
+
confection==0.1.5
|
| 36 |
+
configobj==5.0.9
|
| 37 |
+
configparser==7.1.0
|
| 38 |
+
contourpy==1.3.1
|
| 39 |
+
cryptography==3.4.8
|
| 40 |
+
curl-cffi==0.5.10
|
| 41 |
+
cycler==0.12.1
|
| 42 |
+
cymem==2.0.10
|
| 43 |
+
dataclasses-json==0.5.14
|
| 44 |
+
dbus-python==1.2.18
|
| 45 |
+
decorator==5.1.1
|
| 46 |
+
defusedxml==0.7.1
|
| 47 |
+
Deprecated==1.2.14
|
| 48 |
+
diffusers==0.24.0
|
| 49 |
+
distro==1.7.0
|
| 50 |
+
distro-info===1.1build1
|
| 51 |
+
docopt==0.6.2
|
| 52 |
+
duckduckgo-search==4.1.0
|
| 53 |
+
EdgeGPT==0.6.10
|
| 54 |
+
et_xmlfile==2.0.0
|
| 55 |
+
etelemetry==0.3.1
|
| 56 |
+
exceptiongroup==1.2.0
|
| 57 |
+
executing==2.2.0
|
| 58 |
+
faiss-cpu==1.7.4
|
| 59 |
+
faiss-gpu==1.7.2
|
| 60 |
fastapi==0.115.12
|
| 61 |
+
fastjsonschema==2.21.1
|
| 62 |
+
ffmpy==0.5.0
|
| 63 |
+
filelock==3.13.1
|
| 64 |
+
fitz==0.0.1.dev2
|
| 65 |
+
Flask==3.0.3
|
| 66 |
+
Flask-APScheduler==1.13.1
|
| 67 |
+
Flask-Caching==2.3.0
|
| 68 |
+
Flask-Cors==5.0.0
|
| 69 |
+
Flask-JWT-Extended==4.6.0
|
| 70 |
+
Flask-Limiter==3.8.0
|
| 71 |
+
Flask-WTF==1.2.2
|
| 72 |
+
fonttools==4.55.0
|
| 73 |
+
frontend==0.0.3
|
| 74 |
+
frozenlist==1.4.1
|
| 75 |
+
fsspec==2023.12.2
|
| 76 |
+
GoogleBard==2.1.0
|
| 77 |
+
gpt4-openai-api==0.9.0
|
| 78 |
gradio==5.23.1
|
| 79 |
+
gradio_client==1.8.0
|
| 80 |
+
greenlet==3.0.1
|
| 81 |
+
groovy==0.1.2
|
| 82 |
+
gunicorn==23.0.0
|
| 83 |
+
gyp==0.1
|
| 84 |
+
h11==0.14.0
|
| 85 |
+
httpcore==1.0.2
|
| 86 |
+
httplib2==0.20.2
|
| 87 |
+
httpx==0.25.2
|
| 88 |
+
hugchat==0.3.8
|
| 89 |
+
huggingface-hub==0.29.3
|
| 90 |
+
idna==3.8
|
| 91 |
+
importlib-metadata==4.6.4
|
| 92 |
+
importlib_resources==6.4.5
|
| 93 |
+
iniconfig==2.0.0
|
| 94 |
+
ipython==8.12.3
|
| 95 |
+
isodate==0.6.1
|
| 96 |
+
itsdangerous==2.2.0
|
| 97 |
+
jedi==0.19.2
|
| 98 |
+
jeepney==0.7.1
|
| 99 |
+
Jinja2==3.1.2
|
| 100 |
+
jmespath==1.0.1
|
| 101 |
+
joblib==1.4.2
|
| 102 |
+
jsonpatch==1.33
|
| 103 |
+
jsonpointer==2.4
|
| 104 |
+
jsonschema==4.23.0
|
| 105 |
+
jsonschema-specifications==2024.10.1
|
| 106 |
+
jupyter_client==8.6.3
|
| 107 |
+
jupyter_core==5.7.2
|
| 108 |
+
jupyterlab_pygments==0.3.0
|
| 109 |
+
keyring==23.5.0
|
| 110 |
+
kiwisolver==1.4.7
|
| 111 |
langchain==0.0.352
|
| 112 |
+
langchain-community==0.0.3
|
| 113 |
+
langchain-core==0.1.1
|
| 114 |
+
langchain-experimental==0.0.47
|
| 115 |
+
langcodes==3.5.0
|
| 116 |
+
langsmith==0.0.71
|
| 117 |
+
language_data==1.3.0
|
| 118 |
+
lark==1.1.8
|
| 119 |
+
# Editable install with no version control (latex_marker==0.1)
|
| 120 |
+
-e /mnt/d/知识库/Obsidian Vault/Upenn学习资源/Upenn 课程/HW 8000/tex2bbox
|
| 121 |
+
launchpadlib==1.10.16
|
| 122 |
+
lazr.restfulclient==0.14.4
|
| 123 |
+
lazr.uri==1.0.6
|
| 124 |
+
limits==3.13.0
|
| 125 |
+
looseversion==1.3.0
|
| 126 |
+
lxml==4.9.3
|
| 127 |
+
marisa-trie==1.2.1
|
| 128 |
+
markdown-it-py==3.0.0
|
| 129 |
+
markdownify==0.11.6
|
| 130 |
+
MarkupSafe==2.1.3
|
| 131 |
+
marshmallow==3.20.1
|
| 132 |
matplotlib==3.7.3
|
| 133 |
+
matplotlib-inline==0.1.7
|
| 134 |
+
mdurl==0.1.2
|
| 135 |
+
mistune==3.1.3
|
| 136 |
+
more-itertools==8.10.0
|
| 137 |
+
mpmath==1.3.0
|
| 138 |
+
multidict==6.0.4
|
| 139 |
+
murmurhash==1.0.11
|
| 140 |
+
mypy-extensions==1.0.0
|
| 141 |
+
narwhals==1.32.0
|
| 142 |
+
nbclient==0.10.2
|
| 143 |
+
nbconvert==7.16.6
|
| 144 |
+
nbformat==5.10.4
|
| 145 |
+
nest-asyncio==1.5.8
|
| 146 |
+
netifaces==0.11.0
|
| 147 |
networkx==3.1
|
| 148 |
+
nibabel==5.3.2
|
| 149 |
+
nipype==1.9.1
|
| 150 |
+
nltk==3.9.1
|
| 151 |
+
numexpr==2.8.8
|
| 152 |
+
numpy==1.26.0
|
| 153 |
+
nvidia-cublas-cu12==12.1.3.1
|
| 154 |
+
nvidia-cuda-cupti-cu12==12.1.105
|
| 155 |
+
nvidia-cuda-nvrtc-cu12==12.1.105
|
| 156 |
+
nvidia-cuda-runtime-cu12==12.1.105
|
| 157 |
+
nvidia-cudnn-cu12==8.9.2.26
|
| 158 |
+
nvidia-cufft-cu12==11.0.2.54
|
| 159 |
+
nvidia-curand-cu12==10.3.2.106
|
| 160 |
+
nvidia-cusolver-cu12==11.4.5.107
|
| 161 |
+
nvidia-cusparse-cu12==12.1.0.106
|
| 162 |
+
nvidia-nccl-cu12==2.18.1
|
| 163 |
+
nvidia-nvjitlink-cu12==12.3.101
|
| 164 |
+
nvidia-nvtx-cu12==12.1.105
|
| 165 |
+
oauthlib==3.2.0
|
| 166 |
+
openai==1.12.0
|
| 167 |
+
openapi-schema-pydantic==1.2.4
|
| 168 |
+
openpyxl==3.1.5
|
| 169 |
+
ordered-set==4.1.0
|
| 170 |
+
orjson==3.10.16
|
| 171 |
+
outcome==1.3.0.post0
|
| 172 |
+
packaging==23.2
|
| 173 |
+
pandas==2.1.4
|
| 174 |
+
pandocfilters==1.5.1
|
| 175 |
+
parso==0.8.4
|
| 176 |
+
pathlib==1.0.1
|
| 177 |
+
pexpect==4.9.0
|
| 178 |
+
pickleshare==0.7.5
|
| 179 |
+
Pillow==10.0.0
|
| 180 |
+
pipreqs==0.5.0
|
| 181 |
+
platformdirs==4.3.7
|
| 182 |
+
playwright==1.40.0
|
| 183 |
+
pluggy==1.3.0
|
| 184 |
+
preshed==3.0.9
|
| 185 |
+
prompt-toolkit==3.0.43
|
| 186 |
+
prov==2.0.1
|
| 187 |
+
psutil==5.9.6
|
| 188 |
+
ptyprocess==0.7.0
|
| 189 |
+
pure_eval==0.2.3
|
| 190 |
+
puremagic==1.28
|
| 191 |
+
py==1.11.0
|
| 192 |
+
pycparser==2.21
|
| 193 |
+
pydantic==2.4.2
|
| 194 |
+
pydantic_core==2.10.1
|
| 195 |
+
pydot==3.0.3
|
| 196 |
+
pydub==0.25.1
|
| 197 |
+
pyee==11.0.1
|
| 198 |
+
Pygments==2.17.2
|
| 199 |
+
PyGObject==3.42.1
|
| 200 |
+
PyJWT==2.3.0
|
| 201 |
+
PyLaTeX==1.4.2
|
| 202 |
+
pylatexenc==2.10
|
| 203 |
+
PyMuPDF==1.25.0
|
| 204 |
+
PyMySQL==1.1.1
|
| 205 |
+
pyparsing==3.2.0
|
| 206 |
+
PySocks==1.7.1
|
| 207 |
+
pytest==7.4.3
|
| 208 |
+
pytest-base-url==2.0.0
|
| 209 |
+
pytest-playwright==0.4.3
|
| 210 |
+
python-apt==2.4.0+ubuntu1
|
| 211 |
+
python-dateutil==2.9.0.post0
|
| 212 |
+
python-dotenv==1.0.0
|
| 213 |
+
python-multipart==0.0.20
|
| 214 |
+
python-slugify==8.0.1
|
| 215 |
+
pytz==2023.3.post1
|
| 216 |
+
pyxnat==1.6.2
|
| 217 |
+
PyYAML==5.4.1
|
| 218 |
+
pyzmq==26.3.0
|
| 219 |
+
rdflib==6.3.2
|
| 220 |
+
redis==5.2.0
|
| 221 |
+
referencing==0.36.2
|
| 222 |
+
regex==2023.10.3
|
| 223 |
+
requests==2.31.0
|
| 224 |
+
requests-toolbelt==1.0.0
|
| 225 |
+
retry==0.9.2
|
| 226 |
+
rich==13.7.0
|
| 227 |
+
rpds-py==0.23.1
|
| 228 |
+
ruff==0.11.2
|
| 229 |
+
s3transfer==0.10.3
|
| 230 |
+
safehttpx==0.1.6
|
| 231 |
+
safetensors==0.4.1
|
| 232 |
+
scikit-learn==1.5.2
|
| 233 |
+
scipy==1.14.1
|
| 234 |
+
seaborn==0.13.2
|
| 235 |
+
SecretStorage==3.3.1
|
| 236 |
+
selenium==4.16.0
|
| 237 |
+
semantic-version==2.10.0
|
| 238 |
+
sentence-transformers==3.3.0
|
| 239 |
+
shellingham==1.5.4
|
| 240 |
+
simplejson==3.19.3
|
| 241 |
+
six==1.16.0
|
| 242 |
+
smart-open==7.0.5
|
| 243 |
+
sniffio==1.3.0
|
| 244 |
+
socksio==1.0.0
|
| 245 |
+
sortedcontainers==2.4.0
|
| 246 |
+
soupsieve==2.5
|
| 247 |
+
spacy==3.8.2
|
| 248 |
+
spacy-legacy==3.0.12
|
| 249 |
+
spacy-loggers==1.0.5
|
| 250 |
+
SQLAlchemy==2.0.23
|
| 251 |
+
srsly==2.4.8
|
| 252 |
+
stack-data==0.6.3
|
| 253 |
+
starlette==0.46.1
|
| 254 |
+
sympy==1.12
|
| 255 |
+
systemd-python==234
|
| 256 |
+
tabulate==0.9.0
|
| 257 |
+
tenacity==8.2.3
|
| 258 |
+
text-unidecode==1.3
|
| 259 |
+
thinc==8.3.2
|
| 260 |
+
threadpoolctl==3.5.0
|
| 261 |
+
tiktoken==0.8.0
|
| 262 |
+
tinycss2==1.4.0
|
| 263 |
+
tokenizers==0.20.3
|
| 264 |
+
tomli==2.0.1
|
| 265 |
+
tomlkit==0.12.0
|
| 266 |
+
torch==2.1.2
|
| 267 |
+
tornado==6.4.2
|
| 268 |
+
tqdm==4.66.1
|
| 269 |
+
traitlets==5.14.3
|
| 270 |
+
traits==6.4.3
|
| 271 |
+
transformers==4.46.2
|
| 272 |
+
trio==0.23.2
|
| 273 |
+
trio-websocket==0.11.1
|
| 274 |
+
triton==2.1.0
|
| 275 |
+
typer==0.14.0
|
| 276 |
+
typing-inspect==0.9.0
|
| 277 |
+
typing_extensions==4.12.2
|
| 278 |
+
tzdata==2023.3
|
| 279 |
+
tzlocal==5.2
|
| 280 |
+
ubuntu-advantage-tools==8001
|
| 281 |
+
ufw==0.36.1
|
| 282 |
+
unattended-upgrades==0.1
|
| 283 |
+
undetected-chromedriver==3.5.4
|
| 284 |
+
urllib3==2.2.2
|
| 285 |
+
uvicorn==0.23.2
|
| 286 |
+
wadllib==1.3.6
|
| 287 |
+
wasabi==1.1.3
|
| 288 |
+
wcwidth==0.2.12
|
| 289 |
+
weasel==0.4.1
|
| 290 |
+
webencodings==0.5.1
|
| 291 |
+
websockets==11.0.3
|
| 292 |
+
Werkzeug==3.0.4
|
| 293 |
+
wikipedia==1.4.0
|
| 294 |
+
wrapt==1.16.0
|
| 295 |
+
wsproto==1.2.0
|
| 296 |
+
WTForms==3.2.1
|
| 297 |
+
yarg==0.1.9
|
| 298 |
+
yarl==1.9.4
|
| 299 |
+
zipp==1.0.0
|
style.css
ADDED
|
@@ -0,0 +1,48 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
/* Global styles */
|
| 2 |
+
:root {
|
| 3 |
+
--primary-color: #3498db;
|
| 4 |
+
--secondary-color: #2c3e50;
|
| 5 |
+
--background-color: #f7f9fc;
|
| 6 |
+
--border-color: #e0e0e0;
|
| 7 |
+
--shadow-color: rgba(0,0,0,0.1);
|
| 8 |
+
}
|
| 9 |
+
|
| 10 |
+
body {
|
| 11 |
+
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Microsoft YaHei", "WenQuanYi Micro Hei", sans-serif !important;
|
| 12 |
+
color: #333 !important;
|
| 13 |
+
background-color: var(--background-color) !important;
|
| 14 |
+
}
|
| 15 |
+
|
| 16 |
+
/* 强制应用容器样式 */
|
| 17 |
+
.gradio-container {
|
| 18 |
+
max-width: 100% !important;
|
| 19 |
+
padding: 0 !important;
|
| 20 |
+
}
|
| 21 |
+
|
| 22 |
+
/* 确保section样式 */
|
| 23 |
+
.section {
|
| 24 |
+
background: white !important;
|
| 25 |
+
border-radius: 15px !important;
|
| 26 |
+
padding: 20px !important;
|
| 27 |
+
margin-bottom: 20px !important;
|
| 28 |
+
box-shadow: 0 2px 4px var(--shadow-color) !important;
|
| 29 |
+
}
|
| 30 |
+
|
| 31 |
+
/* 确保卡片样式 */
|
| 32 |
+
.concept-card {
|
| 33 |
+
transition: all 0.3s ease !important;
|
| 34 |
+
border: 1px solid var(--border-color) !important;
|
| 35 |
+
border-radius: 12px !important;
|
| 36 |
+
padding: 16px !important;
|
| 37 |
+
margin-bottom: 16px !important;
|
| 38 |
+
cursor: pointer !important;
|
| 39 |
+
background-color: white !important;
|
| 40 |
+
box-shadow: 0 2px 5px var(--shadow-color) !important;
|
| 41 |
+
}
|
| 42 |
+
|
| 43 |
+
.concept-card:hover {
|
| 44 |
+
box-shadow: 0 5px 15px var(--shadow-color) !important;
|
| 45 |
+
transform: translateY(-3px) !important;
|
| 46 |
+
}
|
| 47 |
+
|
| 48 |
+
/* 其他样式保持不变,但添加 !important */
|
visualization.py
CHANGED
|
@@ -16,7 +16,7 @@ matplotlib.use('Agg')
|
|
| 16 |
# Set up Chinese font support
|
| 17 |
# Try to find suitable Chinese fonts
|
| 18 |
font_found = False
|
| 19 |
-
chinese_fonts = ['
|
| 20 |
for font in chinese_fonts:
|
| 21 |
try:
|
| 22 |
matplotlib.font_manager.findfont(font)
|
|
|
|
| 16 |
# Set up Chinese font support
|
| 17 |
# Try to find suitable Chinese fonts
|
| 18 |
font_found = False
|
| 19 |
+
chinese_fonts = ['SimHei', 'Microsoft YaHei', 'WenQuanYi Micro Hei', 'AR PL UMing CN', 'STSong', 'NSimSun', 'FangSong', 'KaiTi']
|
| 20 |
for font in chinese_fonts:
|
| 21 |
try:
|
| 22 |
matplotlib.font_manager.findfont(font)
|