Dify DSL Workflow Collection

https://github.com/wwwzhouhui/dify-for-dsl
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A comprehensive collection of DSL workflow scripts and plugins for Dify, an open-source LLM...
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### Install Dependencies

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/tts1/README.md

Navigate to the tts1 directory and install the required Python packages using pip.

```bash
cd geekaiapp/tts1
pip install -r requirements.txt
```

--------------------------------

### Start the LaTeX to Word Application

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/laTex1/README.md

Navigate to the application directory and run the main Python script to start the Gradio web server.

```bash
cd geekaiapp/laTex1
python latex_gradio.py
```

--------------------------------

### Set up logging

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/readme.txt

Configures basic logging to INFO level and gets a logger instance.

```python
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
```

--------------------------------

### Configuration File Example

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/story/readme.md

Example of the config.ini file, detailing common, LLM, voice, server, and authentication settings. Ensure API keys and base URLs are correctly configured for your chosen LLM providers.

```ini
[common]
region = xxxx
secret_id = xxxx
secret_key = xxx
bucket = xxx

[llm]
text_llm_provider = intern
text_llm_model = internlm3-latest
image_llm_provider = siliconflow
image_llm_model = black-forest-labs/FLUX.1-schnell
openai_base_url = https://api.openai.com/v1
aliyun_base_url = https://dashscope.aliyuncs.com/compatible-mode/v1
deepseek_base_url = https://api.deepseek.com/v1
ollama_base_url = http://localhost:11434/v1
siliconflow_base_url = https://api.siliconflow.cn/v1
intern_base_url = https://chat.intern-ai.org.cn/api/v1
openai_api_key = ""
ali_api_key = ""
deepseek_api_key = ""
ollama_api_key = ""
siliconflow_api_key = sk-xxxxx
intern_api_key = 

[voice]
voice_name = zh-CN-XiaoxiaoNeural
voice_rate = 1.0

[server]
host = 127.0.0.1
port = 8000

[auth]
valid_tokens = ["zhouhui-1258720xxxx"]
```

--------------------------------

### Start Service

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/story/readme.md

Command to start the Dify Story DSL service. Ensure the configuration file is correctly set up before running.

```bash
python main.py
```

--------------------------------

### Install Dependencies

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/mcp/FastMCP/readme.md

Navigate to the project directory and install all required Python packages using pip. It's recommended to use a specific mirror for faster downloads.

```shell
cd  F:\work\code\2025dify-dsl\dify-for-dsl\mcp\FastMCP
pip install -r  requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/
```

--------------------------------

### Start HiDream-E1 Server

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforgitee/HiDream-E1说明.md

Execute this command in the terminal to start the HiDream-E1 server.

```shell
python giteeapiforall.py
```

--------------------------------

### File Upload Example

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/readme.md

Demonstrates how to upload a file using multipart/form-data. Replace 'test.jpg' with your file name.

```bash
curl -F "file=@test.jpg" http://your-api-domain/api/upload
```

--------------------------------

### Install Dependencies

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/beartAIfaceswap1/README.md

Installs all necessary Python packages for the BeArt AI Face Swap system. Ensure you have a requirements.txt file in your project directory.

```bash
pip install -r requirements.txt
```

--------------------------------

### Example Markdown Input

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/markdown2map/README.md

This is an example of Markdown syntax supported by the tool, including headings, lists, and detailed content.

```markdown
# 主题
## 子主题1
### 详细内容1
- 要点1
- 要点2
## 子主题2
### 详细内容2
- 要点3
- 要点4
```

--------------------------------

### Install markmap-cli

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/markdown2map/README.md

Install the markmap-cli globally using npm. This tool is required for converting Markdown to mindmaps.

```bash
npm install -g markmap-cli
```

--------------------------------

### Start MCP Server

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/mcp/FastMCP/readme.md

Execute the main server script to launch the FastMCP service. This command starts the server, making it available for client connections.

```shell
python doubao_mcp_ai_server2.py
```

--------------------------------

### Install Dependencies

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/story/readme.md

Installs required Python packages using pip. It's recommended to use Python 3.10+.

```bash
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/
```

--------------------------------

### Configuration File Example

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforsiliconflow/bizyair/reademe.md

This INI file defines default settings for output paths, workflow files, ComfyUI endpoint, and cloud storage credentials.

```ini
[DEFAULT]
output_path = D:\\工作临时\\2025\\1月\\2025年1月20日\\output
#output_path = /home/python/difyforgitee/pictures
workflowfile= D:\\工作临时\\2025\\1月\\2025年1月17日\\workflow_api111.json
#workflowfile= /home/python/difyforgitee/workflow_api111.json
comfyui_endpoit=192.168.1.13:8188
region = ap-nanjing              腾讯云OSS存储Region
secret_id = xxxxxx               腾讯云OSS存储SecretId
secret_key = xxxxx               腾讯云OSS存储SecretKey
bucket = dify-1258720957         腾讯云OSS存储bucket

```

--------------------------------

### Start Gradio Application

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/markdown2map/README.md

Run the Gradio application using the provided Python script. This command starts the web interface for the Markdown to Mindmap converter.

```bash
python marp1_gradio.py
```

--------------------------------

### Troubleshoot: Pandoc Not Found Error

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/laTex1/README.md

If Pandoc is not found, ensure it is installed and accessible in your system's PATH environment variable.

```text
错误: 未找到Pandoc
解决: 安装Pandoc并确保在PATH中
```

--------------------------------

### Start Gradio Visual Interface

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/tts1/README.md

Run the Gradio application to access the web-based TTS interface. The service will be available at http://localhost:16003.

```bash
python tts1_gradio.py
```

--------------------------------

### Project Dependencies

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforgitee/dify中创建并使用自定义工具-gitee绘画.md

Lists the Python packages required for the project. These should be installed using pip.

```text
uvicorn== 0.34.0
fastapi== 0.115.6
cos-python-sdk-v5==1.9.33
```

--------------------------------

### Run FastAPI Application

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforgitee/dify中创建并使用自定义工具-gitee绘画.md

Starts the FastAPI application using uvicorn. This is the standard way to run a FastAPI application in production or for development.

```python
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8081)
```

--------------------------------

### Start FastAPI API Service

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/tts1/README.md

Run the FastAPI application to expose the TTS functionality as an API. The service will be available at http://localhost:16003.

```bash
python tts1_jiekou.py
```

--------------------------------

### Comfui Bizyair Workflow JSON Example

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforsiliconflow/bizyair/reademe.md

This JSON represents a Comfui Bizyair workflow, detailing nodes, inputs, and class types for AI image generation tasks. Pay close attention to prompt text and node consistency when customizing.

```json
{
  "70": {
    "inputs": {
      "unet_name": "kolors/Kolors.safetensors"
    },
    "class_type": "BizyAir_MZ_KolorsUNETLoaderV2",
    "_meta": {
      "title": "☁️BizyAir MinusZone - KolorsUNETLoaderV2"
    }
  },
  "73": {
    "inputs": {
      "seed": 20,
      "steps": 20,
      "cfg": 4.5,
      "sampler_name": "dpmpp_sde_gpu",
      "scheduler": "karras",
      "denoise": 1,
      "model": [
        "70",
        0
      ],
      "positive": [
        "80",
        0
      ],
      "negative": [
        "81",
        0
      ],
      "latent_image": [
        "85",
        0
      ]
    },
    "class_type": "BizyAir_KSampler",
    "_meta": {
      "title": "☁️BizyAir KSampler"
    }
  },
  "75": {
    "inputs": {
      "vae_name": "sdxl/sdxl_vae.safetensors"
    },
    "class_type": "BizyAir_VAELoader",
    "_meta": {
      "title": "☁️BizyAir Load VAE"
    }
  },
  "76": {
    "inputs": {
      "samples": [
        "73",
        0
      ],
      "vae": [
        "75",
        0
      ]
    },
    "class_type": "BizyAir_VAEDecode",
    "_meta": {
      "title": "☁️BizyAir VAE Decode"
    }
  },
  "80": {
    "inputs": {
      "text": "真实的照片,20岁女生,红色外套,城市夜景"
    },
    "class_type": "BizyAir_MinusZoneChatGLM3TextEncode",
    "_meta": {
      "title": "☁️BizyAir MinusZone ChatGLM3 Text Encode"
    }
  },
  "81": {
    "inputs": {
      "text": "nsfw，脸部阴影，低分辨率，jpeg伪影、模糊、糟糕，黑脸，霓虹灯"
    },
    "class_type": "BizyAir_MinusZoneChatGLM3TextEncode",
    "_meta": {
      "title": "☁️BizyAir MinusZone ChatGLM3 Text Encode"
    }
  },
  "85": {
    "inputs": {
      "width": 1024,
      "height": 1024,
      "batch_size": 1
    },
    "class_type": "EmptyLatentImage",
    "_meta": {
      "title": "空Latent图像"
    }
  },
  "86": {
    "inputs": {
      "images": [
        "76",
        0
      ]
    },
    "class_type": "PreviewImage",
    "_meta": {
      "title": "预览图像"
    }
  }
}
```

--------------------------------

### Get Images via WebSocket and Server

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/readme.txt

Retrieves images (or videos) generated by a prompt. It queues the prompt, listens for WebSocket messages to detect completion, fetches history, and then retrieves image/video data using `get_image`. Requires `queue_prompt`, `get_history`, `get_image`, `server_address`, `client_id`, and a logger to be defined.

```python
def get_images(ws, prompt):
    prompt_id = queue_prompt(prompt)['prompt_id']
    logger.info(f"Prompt ID: {prompt_id}")
    output_images = {}
    while True:
        out = ws.recv()
        if isinstance(out, str):
            message = json.loads(out)
            if message['type'] == 'executing':
                data = message['data']
                if data['node'] is None and data['prompt_id'] == prompt_id:
                    logger.info("Execution completed")
                    break  # 执行完成
        else:
            continue  # 预览为二进制数据

    history = get_history(prompt_id)[prompt_id]
    logger.info(f"History: {history}")
    for node_id in history['outputs']:
        node_output = history['outputs'][node_id]
        # 图片分支
        if 'images' in node_output:
            images_output = []
            for image in node_output['images']:
                image_data = get_image(image['filename'], image['subfolder'], image['type'])
                images_output.append(image_data)
            output_images[node_id] = images_output
        # 视频分支
        if 'videos' in node_output:
            videos_output = []
            for video in node_output['videos']:
                video_data = get_image(video['filename'], video['subfolder'], video['type'])
                videos_output.append(video_data)
            output_images[node_id] = videos_output

    logger.info("Images retrieved successfully")
    return output_images
```

--------------------------------

### Run the Application

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/beartAIfaceswap1/README.md

Launches the BeArt AI Face Swap Gradio application. The application will be accessible at http://localhost:16001.

```bash
python bf_gradio.py
```

--------------------------------

### 进入容器

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/fapiao/README.md

通过 bash 进入正在运行的 Docker 容器，以便进行调试或执行命令。

```bash
# 进入容器
docker exec -it fapiaosqd-web bash

```

--------------------------------

### 查看容器状态

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/fapiao/README.md

检查正在运行的 Docker 容器的状态。

```bash
# 查看容器状态
docker ps

```

--------------------------------

### Configure Cherry Studio Client

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/mcp/FastMCP/readme.md

Set up Cherry Studio to connect to the MCP server. This involves adding a new server configuration, selecting 'sse' as the type, and providing the correct URL.

```json
{ "doubao_mcp_ai_server": { "url": "http://127.0.0.1:8002/sse", "headers": {}, "timeout": 60, "sse_read_timeout": 300 } }
```

--------------------------------

### Get History from Server

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/readme.txt

Fetches the processing history for a given prompt ID from the server. It makes an HTTP GET request to the server's history endpoint. Requires `server_address` to be defined globally.

```python
def get_history(prompt_id):
    with urllib.request.urlopen(f"http://{server_address}/history/{prompt_id}") as response:
        return json.loads(response.read())
```

--------------------------------

### 直接使用 Docker 运行

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/fapiao/README.md

直接运行 Docker 容器，并配置必要的目录挂载和端口映射。适用于快速启动或独立运行。

```bash
# 创建必要的目录
mkdir -p uploads exports

# 运行容器
docker run -d \
  --name fapiaosqd-web \
  -p 15601:15601 \
  -v $(pwd)/uploads:/app/uploads \
  -v $(pwd)/exports:/app/exports \
  --restart unless-stopped \
  wwwzhouhui569/fapiaosqd:latest

```

--------------------------------

### 构建 Docker 镜像

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/fapiao/README.md

使用此命令构建项目的 Docker 镜像。确保在包含 Dockerfile 的项目根目录下运行。

```bash
# 构建镜像
docker build -t fapiaosqd:latest .

```

--------------------------------

### 更新镜像

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/fapiao/README.md

拉取最新的 Docker 镜像并重新部署服务。

```bash
# 更新镜像
docker-compose pull
docker-compose up -d

```

--------------------------------

### Enable Debug Logging

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/laTex1/README.md

To get more detailed logs for debugging, configure the logging level to DEBUG.

```python
import logging
logging.basicConfig(level=logging.DEBUG)
```

--------------------------------

### 配置客户端认证头

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/jimeng/即梦文生视频逆向接口部署使用.md

在客户端代码中，需要设置Authorization请求头，其值应与服务端config.ini配置文件中的自定义秘钥保持一致，以进行身份验证。

```python
    # 添加认证头
    headers = {
        "Authorization": "Bearer sk-zhouhui11111",  # 替换为实际的认证token
        "Content-Type": "application/json"
    }
```

--------------------------------

### Initialize Global Variables

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/yewu2videoaddsrt/templates/index.html

Initializes global variables for storing file paths. `window.audio_file` stores the temporary location of uploaded audio/video files, and `window.video_url` stores the server's temporary storage location for uploaded videos.

```javascript
window.audio_file = null; // 上传视频后服务器返回的临时存储位置 window.video_url = null;
```

--------------------------------

### Download ComfyUI Bizyair Docker Image

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforsiliconflow/bizyair/reademe.md

Use this command to pull the latest ComfyUI Bizyair Docker image from Docker Hub. Ensure Docker is installed and configured on your system.

```bash
docker pull wwwzhouhui569/comfyui_bizyair:v0.4.0
```

--------------------------------

### 重启服务

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/fapiao/README.md

使用 Docker Compose 重启所有定义的服务。

```bash
# 重启服务
docker-compose restart

```

--------------------------------

### Get Image from Server

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/readme.txt

Retrieves an image from the server using its filename, subfolder, and type. It constructs a URL with query parameters and fetches the image data. Requires `server_address` to be defined globally.

```python
def get_image(filename, subfolder, folder_type):
    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
    url_values = urllib.parse.urlencode(data)
    with urllib.request.urlopen(f"http://{server_address}/view?{url_values}") as response:
        return response.read()
```

--------------------------------

### Generating OpenAPI Schema from cURL

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforgitee/dify中创建并使用自定义工具-gitee绘画.md

This prompt guides GPT to convert a cURL command into an OpenAPI 3.1.0 JSON schema. It specifically excludes response information, focusing on the request structure.

```text
请把curl请求命令转成openapi 3.1.0 版本的json schema，不需要包含response信息
<curl>
curl --location 'http://111.119.215.74:8081/generate_image/'
--header 'Content-Type: application/json'
--data '{"prompt": "一只可爱的小花猫，时尚，头上戴着彩色波点蝴蝶结三角头巾，大大的腮红，很可爱，高饱和度，可爱嘟嘟，毛绒绒且柔软，身穿头巾撞色系旗袍，羊毛毡风格，脖子带你呼应色围巾，非常可爱，怀里抱一束花，上半身肖像，送给你的姿势，卡哇伊，画面简约，高饱和度，轻松气氛，丝滑的画质，中景视角，标准镜头，简约风格，32k高清图，萌态十足，蓝天白云背景，精妙无双"}'
</curl>

json schema请参照下面的例子
<json-schema>
{
"openapi": "3.1.0",
"info": {
"title": "Get weather data",
"description": "Retrieves current weather data for a location.",
"version": "v1.0.0"
},
"servers": [
{
"url": ""
}
],
"paths": {},
"components": {
"schemas": {}
}
</json-schema>
```

--------------------------------

### Initialize Drag-and-Drop and Load Local Storage Settings

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/yewu2videoaddsrt/templates/index.html

Initializes the drag-and-drop functionality for video uploads and loads 'proxy' and 'api_key' from local storage into the respective input fields.

```javascript
$(document).ready(function () {
    dropfun($("#drop"), $("#video"), $("#close"));
    dropfun($("#drop2"), $("#video2"), $("#close2"));
    // 视频解说end
    let proxy = localStorage.getItem('proxy')
    let api_key = localStorage.getItem('api_key')
    if (proxy) {
        $('#proxy').val(proxy)
    }
    if (api_key) {
        $('#api-key').val(api_key)
    }
    // 文件选择器
    $('#load-srt').on('click', function () {
        $('#source-srt').val(''); // 清空文本框
        var fileInput = $('<input type="file" accept=".srt">');
        fileInput.on('change', function (e) {
            var file = e.target.files[0];
            var reader = new FileReader();
            reader.onload = function (e) {
                $('#source-srt').val(e.target.result);
            };
            reader.readAsText(file);
        });
        fileInput.trigger('click');
    });
    // 下载翻译结果 SRT 文件
    $('#download-srt').on('click', function () {
        var targetSrt = $('#target-srt').val().trim();
        if (!targetSrt) {
            return alert('翻译结果为空，无需下载');
        }
        var blob = new Blob([targetSrt], {type: 'text/plain'});
        var url = window.URL.createObjectURL(blob);
        var link = document.createElement('a');
        link.href = url;
        link.setAttribute('download', 'translation.srt');
        document.body.appendChild(link);
        link.click();
        document.body.removeChild(link);
    });
    // 提交表单
    $('#translate-form').on('submit', function (event) {
        event.preventDefault(); // 阻止表单默认提交行为
        var formData = {
            'language': $('#subtitle-language').val(),
            'model_name': $('#model').val(),
            'api_key': $('#api-key').val(),
            'proxy': $('#proxy').val().trim(),
            'text': $('#source-srt').val().trim(),
            'piliang': $('#piliang').val(),
            'waitsec': $('#waitsec').val(),
            "audio_file": window.audio_file
        };
        if (!formData['text'] && !formData['audio_file']) {
            return alert('必须输入srt字幕或上传音视频文件');
        }
        if (!formData['api_key']) {
            <!-- return alert('必须填写Api Key');-->
        }
        if (formData['proxy'] && formData['proxy'].substr(0, 4) != 'http') {
            return alert('代理必须以http开头');
        }
        if (formData['text'] && formData['audio_file']) {
            if (!confirm('同时上传了SRT字幕和音视频，将只对SRT字幕进行翻译，不转录音视频，若需转录请删掉原始SRT字幕，是否继续？')) {
                return;
            }
            formData['audio_file'] = '';
        }
        if (formData['text'] && formData['language'] == '-') {
            return alert('存在原始SRT字幕情况下必须选择目标语言');
        }
        formData['proxy'] && localStorage.setItem('proxy', formData['proxy']);
        localStorage.setItem('api_key', formData['api_key']);
        // 禁用提交按钮
        $('#result_tips').text('')
        $('#submit-button').prop('disabled', true).text((formDa
```

--------------------------------

### API Request for Text-to-Speech

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/tts1/README.md

Example cURL command to make a POST request to the TTS API endpoint. It includes input text, voice selection, model, speed, and desired audio format.

```bash
curl --location 'http://localhost:16003/api/edge/tts12/' \
--header 'Authorization: Bearer geekaiapp' \
--header 'Content-Type: application/json' \
--data '{
    "input":"你好，这是一个测试文本。",
    "voice": "zh-CN-XiaoxiaoNeural",
    "model": "tts-1",
    "speed": 1.0,
    "response_format": "mp3"
}'
```

--------------------------------

### Global Audio Playback Control - JavaScript

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/yewu2edgetts/index.html

Manages the current audio playback state, including the audio object and the play button element. Provides a function to get the HTML for a play/pause button.

```javascript
let currentAudio = null; // 当前播放的音频对象
let currentPlayBtn = null; // 当前播放按钮
// 修改音频列表中的播放按钮 HTML
function getPlayButtonHTML(isPlaying) {
    return isPlaying ? 
    ` <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="text-gray-600">
        <rect x="6" y="4" width="4" height="16"></rect>
        <rect x="14" y="4" width="4" height="16"></rect>
    </svg> ` :
    ` <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="text-gray-600">
        <polygon points="5 3 19 12 5 21 5 3"></polygon>
    </svg> `;
}
```

--------------------------------

### 使用 Docker Compose 部署

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/fapiao/README.md

通过 Docker Compose 管理服务的启动、状态查看、日志跟踪和停止。推荐用于生产环境。

```bash
# 启动服务
docker-compose up -d

# 查看服务状态
docker-compose ps

# 查看日志
docker-compose logs -f fapiaosqd

# 停止服务
docker-compose down

```

--------------------------------

### Configure Tencent Cloud OSS Credentials

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/readme.txt

Sets up region, secret ID, secret key, and bucket name for Tencent Cloud Object Storage using environment variables.

```python
tencent_region = os.getenv('TENCENT_REGION', 'ap-nanjing')
tencent_secret_id = os.getenv('TENCENT_SECRET_ID', 'xxxxxx')
tencent_secret_key = os.getenv('TENCENT_SECRET_KEY', 'xxxxxx')
tencent_bucket = os.getenv('TENCENT_BUCKET', 'dify-1305874767')
```

--------------------------------

### Dify HTTP Request Configuration

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/story/readme.md

Example of configuring an HTTP request in Dify to call the video generation service. Ensure the API key used in Dify matches the 'valid_tokens' in the server's configuration.

```json
{
    "model": "dify-story-video",
    "inputs": {
        "prompt": "A futuristic cityscape at sunset.",
        "video_length": 5
    },
    "response_format": {
        "type": "json_object"
    }
}
```

--------------------------------

### 查看容器日志

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/fapiao/README.md

实时查看指定 Docker 容器的日志输出。

```bash
# 查看容器日志
docker logs fapiaosqd-web

```

--------------------------------

### Add Custom Python Dependencies to Sandbox

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/README.md

Modify the docker-compose.yaml file within the Dify Docker folder to include a python-requirements.txt file in the sandbox volume. This allows for the installation of custom Python libraries like pandas.

```yaml
volumes:
        - ./volumes/sandbox/dependencies/python-requ
irements.txt:/dependencies
        - ./volumes/sandbox/conf:/conf
```

--------------------------------

### Configure PIP Mirror URL for Faster Downloads

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/README.md

Update the PIP_MIRROR_URL in the .env file to a faster mirror, such as Tsinghua University's PyPI mirror, to significantly speed up the installation of Python dependencies for models and tools.

```env
# PIP_MIRROR_URL=https://pypi.tuna.tsinghua.edu.cn/simple
```

```env
PIP_MIRROR_URL=
```

```env
# PIP_MIRROR_URL=https://pypi.tuna.tsinghua.edu.cn/simple 
PIP_MIRROR_URL=https://pypi.tuna.tsinghua.edu.cn/simple
```

--------------------------------

### Configure Alibaba Cloud LLM Service

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/readme.txt

Sets up API key, base URL, and model for Alibaba Cloud's large model service using environment variables.

```python
aliyuncs_api_key = os.getenv('ALIYUNCS_API_KEY', 'xxxxx')
aliyuncs_base_url = os.getenv('ALIYUNCS_BASE_URL', 'https://dashscope.aliyuncs.com/compatible-mode/v1')
aliyuncs_model = os.getenv('ALIYUNCS_MODEL', 'qwen-max')
```

--------------------------------

### 运行 Gradio 应用

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/wenshengtu1/README.md

启动即梦文生图 Gradio 可视化界面。应用将在本地的 http://localhost:7860 启动。

```bash
python jm1_gradio.py
```

--------------------------------

### Global State and API Endpoints Configuration

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/yewu2edgetts/index.html

Defines the global state for the text-to-speech application and configures the API endpoints used for various services like fetching voices, generating audio, and managing subtitles. Ensure these endpoints match your backend setup.

```javascript
// Modify global state management
const state = {
  selectedLanguage: 'zh-CN',
  selectedGender: 'male',
  selectedVoice: null,
  selectedSpeed: 1,
  autoplay: true,
  userId: generateUserId()
};

// Modify API endpoint configuration
const API_ENDPOINTS = {
  VOICES: '/api/voices',
  GENERATE: '/api/generate',
  DELETE: '/api/audio/',
  LIST_AUDIO: '/api/audio',
  SUBTITLE: '/api/subtitle'
};
```

--------------------------------

### Configure API Key

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/mcp/FastMCP/readme.md

Modify the 'doubao_mcp_ai_server2.py' file on line 14 to replace the placeholder with your actual Volcengine API key. This key is essential for accessing cloud AI models.

```python
API_KEY = "火山引擎API密钥"
```

--------------------------------

### Run ComfyUI Bizyair Docker Container

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforsiliconflow/bizyair/reademe.md

This command starts the ComfyUI Bizyair container in detached mode, mapping ports and mounting volumes for persistent storage of models, inputs, outputs, and user data. Adjust the host paths (e.g., D:/tmp/20250118) as needed.

```bash
docker run -d -p 8188:8188 -v "D:/tmp/20250118/models:/app/models" -v "D:/tmp/20250118/input:/app/input" -v "D:/tmp/20250118/temp:/app/output/temp" -v "D:/tmp/20250118/output:/app/output" -v "D:/tmp/20250118/user:/app/user"  --name comfyui-container2 wwwzhouhui569/comfyui_bizyair:v0.4.0
```

--------------------------------

### Dify Workflow Configuration (YAML)

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforgitee/dify中创建并使用自定义工具-gitee绘画.md

This YAML configuration defines a Dify application's workflow, including its name, icon, and operational graph. It specifies nodes for starting the conversation, calling a custom tool (giteeapiKolors), executing Python code, and providing a direct answer.

```yaml
app:
  description: ''
  icon: 🤖
  icon_background: '#FFEAD5'
  mode: advanced-chat
  name: giteeKolors工作流
  use_icon_as_answer_icon: false
kind: app
version: 0.1.3
workflow:
  conversation_variables: []
  environment_variables: []
  features:
    file_upload:
      allowed_file_extensions:
      - .JPG
      - .JPEG
      - .PNG
      - .GIF
      - .WEBP
      - .SVG
      allowed_file_types:
      - image
      allowed_file_upload_methods:
      - local_file
      - remote_url
      enabled: false
      fileUploadConfig:
        audio_file_size_limit: 50
        batch_count_limit: 5
        file_size_limit: 15
        image_file_size_limit: 10
        video_file_size_limit: 100
        workflow_file_upload_limit: 10
      image:
        enabled: false
        number_limits: 3
        transfer_methods:
        - local_file
        - remote_url
      number_limits: 3
    opening_statement: ''
    retriever_resource:
      enabled: true
    sensitive_word_avoidance:
      enabled: false
    speech_to_text:
      enabled: false
    suggested_questions: []
    suggested_questions_after_answer:
      enabled: false
    text_to_speech:
      enabled: false
      language: ''
      voice: ''
  graph:
    edges:
    - data:
        isInIteration: false
        sourceType: start
        targetType: tool
      id: 1734503281575-source-1734503376625-target
      source: '1734503281575'
      sourceHandle: source
      target: '1734503376625'
      targetHandle: target
      type: custom
      zIndex: 0
    - data:
        isInIteration: false
        sourceType: tool
        targetType: code
      id: 1734503376625-source-1734505657943-target
      source: '1734503376625'
      sourceHandle: source
      target: '1734505657943'
      targetHandle: target
      type: custom
      zIndex: 0
    - data:
        isInIteration: false
        sourceType: code
        targetType: answer
      id: 1734505657943-source-1734505427122-target
      source: '1734505657943'
      sourceHandle: source
      target: '1734505427122'
      targetHandle: target
      type: custom
      zIndex: 0
    nodes:
    - data:
        desc: ''
        selected: false
        title: 开始
        type: start
        variables:
        - label: prompt
          max_length: 256
          options: []
          required: true
          type: text-input
          variable: prompt
      height: 89
      id: '1734503281575'
      position:
        x: 92
        y: 292
      positionAbsolute:
        x: 92
        y: 292
      selected: true
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        desc: ''
        provider_id: 29303358-86a5-4a38-a3dc-5691788732d4
        provider_name: giteeapiKolors
        provider_type: api
        selected: false
        title: generateImage
        tool_configurations: {}
        tool_label: generateImage
        tool_name: generateImage
        tool_parameters:
          prompt:
            type: mixed
            value: '{{#1734503281575.prompt#}}'
        type: tool
      height: 53
      id: '1734503376625'
      position:
        x: 384
        y: 282
      positionAbsolute:
        x: 384
        y: 282
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        answer: '{{#1734505657943.result#}}'
        desc: ''
        selected: false
        title: 直接回复
        type: answer
        variables: []
      height: 102
      id: '1734505427122'
      position:
        x: 1038
        y: 270
      positionAbsolute:
        x: 1038
        y: 270
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        code: "def main(arg1: str) -> str:\n    import json\n    data = json.loads(arg1)\n\n		filename=data['filename']\n		url=data['etag']\n		markdown_result	= f\"![{filename}]({url})\"\n		return {\"result\": markdown_result}"
        code_language: python3
        desc: ''
        outputs:
          result:
            children: null
            type: string
        selected: false
        title: 代码执行
        type: code
        variables:
        - value_selector:
          - '1734503376625'
          - text
          variable: arg1
      height: 53
      id: '1734505657943'
      position:
        x: 714
        y: 276
      positionAbsolute:
        x: 714
        y: 276
      selected: false
      sourcePosition: right

```

--------------------------------

### Project File Structure

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/laTex1/README.md

Overview of the directory and file structure for the LaTeX to Word conversion tool.

```text
laTex1/
├── latex_gradio.py      # 主应用文件
├── latex_jiekou.py      # 原始FastAPI接口
├── latex_mcp_jiekou.py  # MCP接口版本
├── requirements.txt     # 依赖列表
├── README.md           # 说明文档
├── temp/               # 临时文件目录
└── static/             # 静态文件目录
```

--------------------------------

### Parse Workflow and Get Images

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/readme.txt

Parses a workflow definition, sets a text prompt, and retrieves generated images. It requires a WebSocket connection (`ws`), the prompt text, a seed value, and the workflow data. It utilizes `get_images` to fetch the results. Note: The workflow loading from file is commented out.

```python
def parse_worflow(ws, prompt, seed, workflowfile):
    logger.info(f"Workflow file: {workflowfile}")
    # with open(workflowfile, 'r', encoding="utf-8") as workflow_api_txt2gif_file:
    #     prompt_data = json.load(workflow_api_txt2gif_file)
    # 设置文本提示
    workflowfile["80"]["inputs"]["text"] = prompt
    return get_images(ws, workflowfile)
```

--------------------------------

### Clone Project Repository

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/markdown2map/README.md

Clone the project repository from GitHub and navigate into the project directory.

```bash
git clone <repository-url>
cd geekaiapp/markdown2map
```

--------------------------------

### FastAPI Backend for ComfyUI Bizyair Integration

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/dsl/difyforsiliconflow/bizyair/reademe.md

This Python code sets up a FastAPI application to serve as an API gateway for ComfyUI Bizyair. It includes configurations for file paths, ComfyUI endpoint, and Tencent Cloud COS for image storage. Ensure you have the necessary libraries installed (`websocket-client`, `fastapi`, `python-qcloud-cosapi`, `configparser`).

```python
import json
import websocket  # NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
import uuid
import urllib.request
import urllib.parse
import random
from datetime import datetime
from fastapi import FastAPI, File, UploadFile,HTTPException,Form
from pydantic import BaseModel
import configparser
import logging
import os
from qcloud_cos import CosConfig
from qcloud_cos import CosS3Client

# 读取配置文件
config = configparser.ConfigParser()
# 读取配置文件，指定编码为 utf-8
config.read('config.ini', encoding='utf-8')

# 获取配置项
output_path = config.get('DEFAULT', 'output_path')
workflowfile = config.get('DEFAULT', 'workflowfile')
comfyui_endpoit = config.get('DEFAULT', 'comfyui_endpoit')
region = config.get('DEFAULT', 'region')
secret_id = config.get('DEFAULT', 'secret_id')
secret_key = config.get('DEFAULT', 'secret_key')
bucket = config.get('DEFAULT', 'bucket')
OSSPICURL=""
# 初始化 FastAPI 应用
app = FastAPI()

# 设置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

```

--------------------------------

### Run FastAPI API Version

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/geekaiapp/markdown2map/README.md

If you need an API interface instead of the Gradio UI, run the FastAPI version of the application using this command.

```bash
python marp1_jiekou.py
```

--------------------------------

### Event Listeners and State Management

Source: https://github.com/wwwzhouhui/dify-for-dsl/blob/main/py/yewu2edgetts/index.html

Sets up event listeners for language selection and gender selection, updating the application state accordingly. Includes debouncing for voice fetching to optimize performance.

```javascript
// 使用防抖处理语音列表获取
const debouncedFetchVoices = debounce(fetchVoices, 300);

// 修改事件监听器，使用事件委托
document.addEventListener('DOMContentLoaded', () => {
  // 改语言选择的事件监听器
  document.getElementById('languageSelect').addEventListener('change', (e) => {
    state.selectedLanguage = e.target.value;
    state.selectedVoice = null; // 切换语言时重置选中的声音
    fetchVoices();
  });

  // 性别选择
  document.querySelectorAll('[data-gender]').forEach(btn => {
    btn.addEventListener('click', (e) => {
      // 更新按钮样式
      document.querySelectorAll('[data-gender]').forEach(b => {
        if (b === e.target) {
          b.classList.remove('bg-gray-100', 'text-gray-700');
          b.classList.add('bg-purple-600', 'text-white');
        } else {
          b.classList.remove('bg-purple-600', 'text-white');
          b.classList.add('bg-gray-100', 'text-gray-700');
        }
      });
      // 重置选中的
```
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