Update README.md

README.md

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-# LLM Educational Assistant Platform
-
-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.
-
-## Features
-
-1. **User Profile Customization**
-   - Input grade, subject, and learning needs
-   - Receive personalized learning experiences and explanations
-
-2. **Concept Decomposition and Visualization**
-   - Break down complex problems into basic concepts
-   - Visualize concept relationships through tree diagrams or network graphs
-   - Intuitively display dependencies and connections between concepts
-
-3. **Interactive Learning Experience**
-   - Click on any sub-concept to get detailed explanations
-   - View targeted examples and exercises
-   - Get recommendations for relevant learning resources
-
-4. **Progress Saving and Caching**
-   - Cache generated concept explanations to improve response speed
-   - Save learning records for easy review and revision
-
-## Installation and Running
-
-### Prerequisites
-
-- Python 3.7+ 
-- pip (Python package manager)
-
-```angular2html
-conda create -n ai_new_dream python=3.11
-pip install -r requirements.txt
-export OPENAI_API_KEY="your-secret-api-key"
-```
-### Automatic Installation and Running
-
-**Linux/Mac:**
-```bash
-# Add execution permission
-chmod +x run.sh
-
-# Run startup script
-./run.sh
-```
-
-**Windows:**
-```
-# Double-click to run, or run in command prompt
-run.bat
-```
-
-### Manual Installation and Running
-
-```bash
-# Create virtual environment
-python -m venv venv
-
-# Activate virtual environment
-# On Windows:
-venv\Scripts\activate
-# On Linux/Mac:
-source venv/bin/activate
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Run application
-python app.py
-```
-
-## Project Structure
-
-```
-.
-├── app.py              # Main application file
-├── prompts.py          # LLM prompt templates
-├── llm_utils.py        # LLM utility functions
-├── visualization.py    # Concept graph visualization module
-├── cache_utils.py      # Caching utilities
-├── concept_handler.js  # JavaScript for concept click handling
-├── requirements.txt    # Project dependencies
-├── run.sh              # Linux/Mac startup script
-└── run.bat             # Windows startup script
-```
-
-## Integrated LLM
-
-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:
-
-```python
-def call_llm(prompt: str) -> str:
-    """Call the OpenAI API with gpt4omini model"""
-    try:
-        from openai import OpenAI
-        client = OpenAI(api_key="YOUR_API_KEY")
-        response = client.chat.completions.create(
-            model="gpt4omini",
-            messages=[
-                {"role": "system", "content": "You are a helpful education assistant."},
-                {"role": "user", "content": prompt}
-            ]
-        )
-        return response.choices[0].message.content
-    except Exception as e:
-        # Fallback to mock data if API call fails
-        print(f"Error calling OpenAI API: {e}")
-        # ...
-```
-
-If the API call fails, the system falls back to mock data to demonstrate functionality.
-
-## Customization and Extension
-
-- **Adding New Subjects**: Add new subject options in the `subject_input` options in `app.py`
-- **Adjusting Prompt Templates**: Modify the prompt templates in `prompts.py` to implement specific teaching styles or methods
-- **Enhancing Visualization**: Modify the visualization functions in `visualization.py` to implement richer concept graph representations
-- **Changing LLM Model**: To use a different model, update the `model` parameter in the `call_llm` function in `llm_utils.py`
-
-## Contributions and Feedback
-
-Questions, suggestions, or code contributions are welcome to help improve this educational assistant platform!
-
-## 教育LLM应用
-
-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。
-
-### 功能特点
-
-- 根据学生的年级和学科自动调整内容难度
-- 将复杂问题分解为相互关联的子概念
-- 生成可视化的知识图谱
-- 为每个概念提供详细解释、示例、学习资源和练习题
-
-### 如何运行
-
-#### 使用脚本运行（推荐）
-
-1. 确保您已安装Python 3.7或更高版本
-2. 在终端中导航到项目目录
-
-对于Mac/Linux用户:
-```bash
-chmod +x run.sh  # 添加执行权限
-./run.sh         # 运行脚本
-```
-
-对于Windows用户:
-```
-run.bat
-```
-
-#### 手动设置
-
-1. 创建并激活虚拟环境:
-```bash
-python -m venv venv
-source venv/bin/activate  # Mac/Linux
-venv\Scripts\activate     # Windows
-```
-
-2. 安装依赖:
-```bash
-pip install -r requirements.txt
-```
-
-3. 启动应用:
-```bash
-python app.py
-```
-
-### 配置OpenAI API
-
-应用使用OpenAI API进行概念分解和解释。在`config.py`文件���设置您的API密钥：
-
-```python
-OPENAI_API_KEY = "您的API密钥"  # 替换为您的实际API密钥
-```
-
-您也可以调整其他配置参数：
-- `OPENAI_MODEL`: 要使用的OpenAI模型名称
-- `OPENAI_TIMEOUT`: API调用超时时间（秒）
-- `OPENAI_MAX_RETRIES`: 请求失败时的最大重试次数
-- `DEBUG_MODE`: 是否启用调试输出
-- `USE_FALLBACK_DATA`: API失败时是否使用备用数据
-- `CACHE_ENABLED`: 是否启用响应缓存
-
-### 系统架构
-
-应用由以下主要组件组成：
-
-1. **app.py** - 主应用文件，包含Gradio界面
-2. **llm_utils.py** - LLM调用和处理函数
-3. **visualization.py** - 知识图谱可视化
-4. **prompts.py** - LLM提示模板
-5. **cache_utils.py** - 响应缓存功能
-6. **config.py** - 应用配置
-7. **concept_handler.py** - 备用模拟数据（API失败时使用）
-
-### 自定义和扩展
-
-您可以通过以下方式自定义应用：
-
-1. **添加新学科**: 扩展prompts.py中的领域特定提示
-2. **调整提示模板**: 修改prompts.py中的系统和用户提示
-3. **增强可视化**: 在visualization.py中调整知识图谱的生成
-4. **更改模型**: 在config.py中指定不同的OpenAI模型
-
-### 故障排除
-
-如果遇到连接错误：
-1. 检查您的API密钥是否正确
-2. 确认您的网络连接正常
-3. 检查模型名称是否正确（例如："gpt-4o-mini"）
-4. 查看应用的调试输出（启用DEBUG_MODE）
-
-### 贡献
-
-欢迎提交问题报告和拉取请求来改进这个项目。 
+title: NexusLearnAI
+emoji: 📊
+colorFrom: gray
+colorTo: gray
+sdk: gradio
+sdk_version: 5.23.1
+app_file: app.py
+pinned: false
+license: apache-2.0