semvec/pypi-packages huggingface dataset:
Rithvika-tryit 0.1
A Python package for data analysis and visualization using Pandas, NumPy, Matplotlib, and Seaborn.
Requires Python: >=3.6

# Rithvika_tryit 📊

Rithvika_tryit is a lightweight and easy-to-use Python library built to accelerate Exploratory Data Analysis (EDA) and preliminary dataset inspection for data science projects. It wraps essential Pandas, NumPy, Seaborn, and Matplotlib utilities to help you quickly understand the structure, quality, and relationships within your dataset.

---

## ✨ Features

- ✅ Quick DataFrame summary: shape, datatypes, missing values, and statistics  
- ✅ Visualize correlation matrix with heatmaps  
- ✅ Detect outliers using the IQR method  
- ✅ Plot numeric feature distributions  
- ✅ Report missing values as proportions  
- ✅ Get value counts for all categorical columns  

---

## 📦 Installation


bash
pip install Rithvika_tryit


Since this is a custom library, clone or copy the source code into your project directory or package it using setuptools if needed.

bash
# Clone this repository or include the file directly
git clone https://github.com/KanumuruRithvika/Rithvika_tryit.git




In your Python project:




from Rithvika_tryit import df_summary, missing_report, correlation_matrix, outlier_summary, plot_distributions, value_counts_all




🧪 How to Use


1. df_summary(df)
Provides an overall summary of the DataFrame including shape, data types, missing values, and descriptive statistics.



import pandas as pd
from Rithvika_tryit import df_summary

df = pd.read_csv("your_data.csv")
df_summary(df)


2. missing_report(df)
Returns a Series with the proportion of missing values in each column (sorted descending).


from Rithvika_tryit import missing_report

missing_report(df)


3. correlation_matrix(df)
Plots a heatmap of correlation coefficients for all numeric features in the DataFrame.


from Rithvika_tryit import correlation_matrix

correlation_matrix(df)



4. outlier_summary(df)
Returns a count of outliers per numeric column using the IQR method.

from Rithvika_tryit import outlier_summary

outlier_summary(df)


5. plot_distributions(df)
Displays histograms of all numeric columns in the DataFrame.


from Rithvika_tryit import plot_distributions

plot_distributions(df)


6. value_counts_all(df)
Returns a dictionary with value counts for all object (categorical) columns.


from Rithvika_tryit import value_counts_all

value_counts_all(df)



🧑‍💻 Developer
Kanumuru Rithvika
Artificial Intelligence & Data Science | ML & Web Dev Enthusiast

📜 License
This project is licensed under the MIT License. Feel free to use and modify!

🙌 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change.

🧠 Future Features
Advanced outlier detection (Z-score, Isolation Forest)

Interactive plots using Plotly

Automatic report generation using pandas-profiling or sweetviz


Let me know if you'd like help setting this up as a proper Python package
pure-db-uzb 0.1.0
Oddiy faylga asoslangan va serverga ulanadigan yani global ma'lumotlarni saqlay oladigan ma'lumotlar bazasi
Requires Python: >=3.6

# 📦 PureDB

PureDB — bu Flask asosida ishlaydigan oddiy va xavfsiz JSON (pdb) bazasi tizimi.

## 🔧 O‘rnatish

```bash
pip install flask requests
```

Serverni ishga tushiring:

```bash
python app.py
```

## 🚀 Ishlatish

### 📡 Server orqali ishlash

API Endpointlar:
```
GET    /api/<server>/records
POST   /api/<server>/records
PUT    /api/<server>/records/<id>
DELETE /api/<server>/records/<id>
```

Har bir so‘rovga header kerak:
```
X-DB-Password: <hashed_password>
```

### 🧑‍💻 Python’da foydalanish

```python
from puredb import ServerDB

db = ServerDB('http://localhost:5000/api/mydb')
db.password('your_hashed_password')
db.add({'name': 'Ali'})
print(db.all())
```

## 🔐 Parol hashing

```python
from hashlib import sha256
h = sha256('your_password'.encode()).hexdigest()
```

## ❗ Xatoliklar

| Kod | Sabab              |
|-----|--------------------|
| 401 | Parol noto‘g‘ri    |
| 403 | Ruxsat yo‘q        |
| 404 | Server topilmadi   |

## 🧪 Test qilish

```bash
curl -H "X-DB-Password: <hash>" http://localhost:5000/api/mydb/records
```

## 📫 Muallif

Salohiddin Esanbekov  
Telegram: https://t.me/SalohiddinEsanbekov
pylibbhavya 0.1
A Python package for data analysis and visualization using Pandas, NumPy, Matplotlib, and Seaborn.
Requires Python: >=3.6

# pylibbhavya 📊

pylibbhavya is a lightweight and easy-to-use Python library built to accelerate Exploratory Data Analysis (EDA) and preliminary dataset inspection for data science projects. It wraps essential Pandas, NumPy, Seaborn, and Matplotlib utilities to help you quickly understand the structure, quality, and relationships within your dataset.

---

## ✨ Features

- ✅ Quick DataFrame summary: shape, datatypes, missing values, and statistics  
- ✅ Visualize correlation matrix with heatmaps  
- ✅ Detect outliers using the IQR method  
- ✅ Plot numeric feature distributions  
- ✅ Report missing values as proportions  
- ✅ Get value counts for all categorical columns  

---

## 📦 Installation


bash
pip install pylibbhavya


Since this is a custom library, clone or copy the source code into your project directory or package it using setuptools if needed.

bash
# Clone this repository or include the file directly
git clone https://github.com/yourusername/pylibbhavya.git




In your Python project:




from pylibbhavya import df_summary, missing_report, correlation_matrix, outlier_summary, plot_distributions, value_counts_all




🧪 How to Use


1. df_summary(df)
Provides an overall summary of the DataFrame including shape, data types, missing values, and descriptive statistics.



import pandas as pd
from pylibbhavya import df_summary

df = pd.read_csv("your_data.csv")
df_summary(df)


2. missing_report(df)
Returns a Series with the proportion of missing values in each column (sorted descending).


from pylibbhavya import missing_report

missing_report(df)


3. correlation_matrix(df)
Plots a heatmap of correlation coefficients for all numeric features in the DataFrame.


from pylibbhavya import correlation_matrix

correlation_matrix(df)



4. outlier_summary(df)
Returns a count of outliers per numeric column using the IQR method.

from pylibbhavya import outlier_summary

outlier_summary(df)


5. plot_distributions(df)
Displays histograms of all numeric columns in the DataFrame.


from pylibbhavya import plot_distributions

plot_distributions(df)


6. value_counts_all(df)
Returns a dictionary with value counts for all object (categorical) columns.


from pylibbhavya import value_counts_all

value_counts_all(df)



🧑‍💻 Developer
Pavan Aditya Kumar Gorrela
Artificial Intelligence & Data Science | ML & Web Dev Enthusiast

📜 License
This project is licensed under the MIT License. Feel free to use and modify!

🙌 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change.

🧠 Future Features
Advanced outlier detection (Z-score, Isolation Forest)

Interactive plots using Plotly

Automatic report generation using pandas-profiling or sweetviz


Let me know if you'd like help setting this up as a proper Python package
pavnds 0.1
A Python package for data analysis and visualization using Pandas, NumPy, Matplotlib, and Seaborn.
Requires Python: >=3.6

# pavnds 📊

**pavnds** is a lightweight and easy-to-use Python library built to accelerate Exploratory Data Analysis (EDA) and preliminary dataset inspection for data science projects. It wraps essential Pandas, NumPy, Seaborn, and Matplotlib utilities to help you quickly understand the structure, quality, and relationships within your dataset.

---

## ✨ Features

- ✅ Quick DataFrame summary: shape, datatypes, missing values, and statistics  
- ✅ Visualize correlation matrix with heatmaps  
- ✅ Detect outliers using the IQR method  
- ✅ Plot numeric feature distributions  
- ✅ Report missing values as proportions  
- ✅ Get value counts for all categorical columns  

---

## 📦 Installation


```bash
pip install pavnds
```

Since this is a custom library, clone or copy the source code into your project directory or package it using `setuptools` if needed.

```bash
# Clone this repository or include the file directly
git clone https://github.com/Pavan-Aditya-Kumar-Gorrela/pavnds.git

```


In your Python project:




from pavnds import df_summary, missing_report, correlation_matrix, outlier_summary, plot_distributions, value_counts_all




🧪 How to Use


1. df_summary(df)
Provides an overall summary of the DataFrame including shape, data types, missing values, and descriptive statistics.



import pandas as pd
from pavnds import df_summary

df = pd.read_csv("your_data.csv")
df_summary(df)


2. missing_report(df)
Returns a Series with the proportion of missing values in each column (sorted descending).


from pavnds import missing_report

missing_report(df)


3. correlation_matrix(df)
Plots a heatmap of correlation coefficients for all numeric features in the DataFrame.


from pavnds import correlation_matrix

correlation_matrix(df)



4. outlier_summary(df)
Returns a count of outliers per numeric column using the IQR method.

from pavnds import outlier_summary

outlier_summary(df)


5. plot_distributions(df)
Displays histograms of all numeric columns in the DataFrame.


from pavnds import plot_distributions

plot_distributions(df)


6. value_counts_all(df)
Returns a dictionary with value counts for all object (categorical) columns.


from pavnds import value_counts_all

value_counts_all(df)



🧑‍💻 Developer
Pavan Aditya Kumar Gorrela
Artificial Intelligence & Data Science | ML & Web Dev Enthusiast

📜 License
This project is licensed under the MIT License. Feel free to use and modify!

🙌 Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you'd like to change.

🧠 Future Features
Advanced outlier detection (Z-score, Isolation Forest)

Interactive plots using Plotly

Automatic report generation using pandas-profiling or sweetviz


Let me know if you'd like help setting this up as a proper Python package
py2hackCraft 0.1.0
Python client library for hackCraft2
Requires Python: >=3.7

# py2hackCraft

Python client library for hackCraft2

## ドキュメント

詳細なAPIリファレンスと使用例は[ドキュメント](https://github.com/0x48lab/hackCraft2-python)を参照してください。

## デプロイ手順

### 事前準備

1. PyPIアカウントの設定
   - [PyPI](https://pypi.org)でアカウントを作成（まだの場合）
   - Account Settings → API tokens で新しいトークンを生成
   - トークンをGitHubリポジトリのSecretsに追加
     - GitHubリポジトリの Settings → Secrets → New repository secret
     - 名前: `PYPI_API_TOKEN`
     - 値: PyPIで生成したトークン

2. パッケージ情報の更新
   - `setup.py`の以下の項目を確認・更新
     - `version`: 新しいバージョン番号（セマンティックバージョニング推奨）
     - `author`: 作者名
     - `author_email`: 連絡先メールアドレス
     - `url`: GitHubリポジトリのURL
     - `description`: パッケージの説明
   - `README.md`の更新（必要に応じて）
   - ドキュメントの更新（必要に応じて）

### デプロイ方法

1. 変更をコミット
   ```bash
   git add .
   git commit -m "Update package information and documentation"
   git push origin main
   ```

2. 新しいバージョンのタグを作成してプッシュ
   ```bash
   git tag v0.1.0  # バージョン番号を適切に設定
   git push origin v0.1.0
   ```

3. GitHub Actionsの実行
   - タグがプッシュされると、自動的にGitHub Actionsが実行されます
   - Actionsタブでデプロイの進行状況を確認できます
   - 成功すると、新しいバージョンがPyPIに公開されます
   - ドキュメントがGitHub Pagesに自動的にデプロイされます

### 注意事項

- バージョン番号は必ず更新してください（同じバージョンでの再デプロイはできません）
- PyPIトークンは安全に管理し、GitHub Secretsとして保存してください
- デプロイ前に`setup.py`と`README.md`の内容が正しいことを確認してください
- テストを実行して、パッケージが正しく動作することを確認してください
- ドキュメントの更新が必要な場合は、`docs/`ディレクトリ内のファイルを編集してください

## インストール方法

```bash
pip install py2hackCraft
```

## 使用方法

```python
from py2hackCraft.modules import Player, Volume, LocationFactory

# プレイヤーの接続
player = Player("your_name")
player.login("localhost", 25570)

# エンティティの取得と操作
entity = player.get_entity("entity_name")
entity.set_event_area(Volume.local(10, 10, 10, -10, -10, -10))

# その他の操作...
```

## ライセンス

MIT License
apidoc2 0.1.2
从Java项目批量提取ApiDoc注解并生成API文档的工具
Requires Python: >=3.6

# apidoc2
        
        一个用于从Java项目中批量提取ApiDoc注解并生成API文档的Python工具，支持多版本对比和自动生成apidoc文档。
        
        ## 功能简介
        
        - 支持从指定Git仓库克隆Java项目，提取所有Java文件中的ApiDoc注解
        - 支持多版本API文档对比，输出差异统计
        - 自动生成apidoc格式文档，便于前端/后端协作
        - 支持命令行参数自定义仓库、版本、输出文件名等
        
        ## 安装方法
        
        推荐使用pip安装（需先打包上传到PyPI）：
        
        ```bash
        pip install extract-apidoc
        ```
        
        或本地安装：
        
        ```bash
        git clone <your_repo_url>
        cd extract_apidoc
        pip install .
        ```
        
        ## 使用说明
        
        ### 基本用法
        
        ```bash
        python3 extract_apidoc.py -r <git仓库地址> -v1 <当前版本号> [-v2 <对比版本号>] [-o <输出文件名>]
        ```
        
        ### 参数说明
        
        - `-r, --repourl`：Git仓库地址（必填）
        - `-v1, --version1`：当前版本号或分支名（必填）
        - `-v2, --version2`：对比的历史版本号或分支名（可选）
        - `-o, --output`：输出文件名（可选，默认为output）
        
        ### 示例
        
        1. 只提取当前版本API文档：
           ```bash
           python3 extract_apidoc.py -r https://your.git.repo/project.git -v1 master
           ```
        2. 对比两个版本API文档差异：
           ```bash
           python3 extract_apidoc.py -r https://your.git.repo/project.git -v1 master -v2 develop
           ```
        
        ### 依赖
        
        - Python 3.6+
        - git
        - apidoc（需全局安装，`npm install -g apidoc`）
        
        ### 贡献
        
        欢迎提交PR或issue改进本工具。
        
        ### License
        
        MIT
py-snmp-sync 1.0.1
Lightweight synchronous SNMPv1 Client implemented as a PySNMP wrapper optimized for sequential queries
Requires Python: >=3.7

# py-snmp-sync

[![PyPI](https://img.shields.io/pypi/v/py-snmp-sync.svg?maxAge=2592000)](https://pypi.org/project/py-snmp-sync/)
[![PyPI download month](https://img.shields.io/pypi/dm/py-snmp-sync.svg)](https://pypi.python.org/pypi/py-snmp-sync/)

**Lightweight synchronous SNMPv1 Client implemented as a PySNMP wrapper optimized for sequential queries**

---

## Features

- **Synchronous API** for straightforward SNMPv1 GET operations
- Lightweight wrapper around PySNMP's core components
- Can be used to interface Epson printers

---

## Installation

```bash
pip install py-snmp-sync
```

## Quick Start

```python
from py_snmp_sync import (
    SyncUdpTransportTarget, sync_get_cmd, ObjectIdentity, CommunityData
)

target = SyncUdpTransportTarget(('demo.pysnmp.com', 161))
oid = ObjectIdentity('1.3.6.1.2.1.1.1.0')

# Perform SNMP GET request
error, status, index, binds = sync_get_cmd(
    CommunityData("public", mpModel=0),
    target,
    oid
)

if not error and not status:
    for name, value in binds:
        print(f"{name.prettyPrint()} = {value.prettyPrint()}")
```

## Usage

### Basic Configuration

```python
transport = SyncUdpTransportTarget(
    (host, 161), 
    timeout=2, 
    retries=3
)
community = CommunityData("public", mpModel=0)  # SNMPv1
```

- `timeout`: Response wait timeout (seconds)
- `retries`: Number of retry attempts

Alternatively, `community` can be a string (e.g., `"public"`).

`oid` can be an `ObjectIdentity()` (e.g., `ObjectIdentity("SNMPv2-MIB", "sysDescr", 0)` or `ObjectIdentity('1.3.6.1.2.1.1.1.0')`), or a string (e.g., `"1.3.6.1.2.1.1.1.0"`).

`sync_get_cmd()` returns a tuple similar to pysnmp `get_cmd()`: `(error_indication, error_status, error_index, var_binds)` and supports multiple OIDs per request.

### Batch operations

```python
oids = [
    ObjectIdentity('1.3.6.1.2.1.1.1.0'),
    ObjectIdentity('1.3.6.1.2.1.1.5.0')
]

with SyncUdpTransportTarget(('device-ip', 161)) as transport:
    for oid in oids:
        error, status, _, binds = sync_get_cmd(community, transport, oid)
        # Handle response
```

## Contributing

Contributions welcome! Please follow standard guidelines:

- Fork the repository

- Create a feature branch

- Submit a Pull Request

## License

EUPL-1.2 License - See LICENSE for details.
mkdocs-apidescribed-plugin 1.2.0
mkdocs plugin to generate API documentation for Python programs
Requires Python: >=3.10
Keywords: api, mkdocs, plugin

# mkdocs-apidescribed-plugin

https://github.com/idlesign/mkdocs-apidescribed-plugin

[![PyPI - Version](https://img.shields.io/pypi/v/mkdocs-apidescribed-plugin)](https://pypi.python.org/pypi/mkdocs-apidescribed-plugin)
[![License](https://img.shields.io/pypi/l/mkdocs-apidescribed-plugin)](https://pypi.python.org/pypi/mkdocs-apidescribed-plugin)
[![Coverage](https://img.shields.io/coverallsCoverage/github/idlesign/mkdocs-apidescribed-plugin)](https://coveralls.io/r/idlesign/mkdocs-apidescribed-plugin)
[![Docs](https://img.shields.io/readthedocs/mkdocs-apidescribed-plugin)](https://mkdocs-apidescribed-plugin.readthedocs.io/)

## Description

*mkdocs plugin to generate API documentation for Python programs*

* Made to autodocument Python code
* Plays well with [Material for MkDocs](https://squidfunk.github.io/mkdocs-material/)
* Configurable
* Easy to use

Just add ``::: apidescribed:`` directive into your documentation followed by a module path:

```md title="index.md"
This is my documentation

::: apidescribed: somepackage.somemodule

```

## Documentation

https://mkdocs-apidescribed-plugin.readthedocs.io/
apibeat 0.1.8
Benchmarking tool to compare REST and GraphQL API performance
Requires Python: >=3.7
Keywords: api, benchmark, graphql, rest, latency, performance, cli

# apibeat

**A simple CLI tool to benchmark and compare REST vs GraphQL API performance by measuring average latency.**



## Features

- Measure average latency of REST and GraphQL endpoints  
- Support custom HTTP methods (GET, POST, etc.) for REST  
- Accept GraphQL queries via CLI  
- Simple, clean CLI interface  
- Useful for API optimization and performance comparisons  

---

## Installation

### Install from PyPI

```bash
pip install apibeat
```

### Benchmark REST API with default GET method

```bash
 apibeat --url https://api.example.com/data

```

### Use a different HTTP method (REST)

```bash
apibeat --url https://api.example.com/data --method POST
```

### Use a different HTTP method (GraphQL Apis)

```bash
apibeat --type graphql --url https://api.example.com/graphql --query "{ users { id name } }"
```
## CLI Arguments

| Argument  | Description                                   | Required            | Default |
|-----------|-----------------------------------------------|---------------------|---------|
| `--url`   | The API endpoint URL to benchmark              | Yes                 | `NA`     |
| `--type`  | API type: `rest` or `graphql`                  | No                  | `rest`  |
| `--method`| HTTP method for REST requests                   | No (only for REST)  | `GET`   |
| `--query` | GraphQL query string (required if `--type graphql`) | Conditional         |  `NA`       |



For any questions or support, please open an issue or contact [demonking15543@gmail.com](mailto:demonking15543@gmail.com).
fixed-income-tools 0.0.1
Tools for fixed income analysis
Requires Python: >=3.7

# Fixed Income Tools
[![PyPI version](https://badge.fury.io/py/fixed-income-tools.svg)](https://pypi.org/project/fixed-income-tools/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![Supported Python versions](https://img.shields.io/pypi/pyversions/fixed-income-tools.svg)
docformer 1.0.7
A document editor for .docformer and .docform files
Requires Python: >=3.6

# DocFormer

DocFormer是一个简单的文档编辑器，支持创建和编辑.docformer格式的文本文件，并可以导出为HTML格式。

## 功能特点

- 创建和编辑.docformer格式的文本文件
- 导出为.docform格式，自动生成目录和格式化文本
- 支持多级缩进，自动调整字号
- 可以安装到系统中，支持命令行操作
- 提供文件夹配置功能，自动创建导出脚本

##安装要求
- 确保安装tkinter

## 安装方法

1. 确保已安装Python 3.6或更高版本
2. 运行以下命令安装：

```bash
pip install .
```

## 使用方法

### 图形界面

安装后，可以通过以下命令启动编辑器：

```bash
docformer
```

## 文件格式说明

### .docformer格式

- 纯文本格式
- 使用缩进来表示层级关系
- 第一行为总标题
- 每个空格表示一级缩进
- 自动生成目录和对应编号
- 点击目录高亮跳转到对应块
- 鼠标悬浮在块上会高亮对应块
- 以\开头的行表示无需计入目录的注文
- 插入图片和文件（[img:] 和 [file:]）需要单起一行

### HTML格式

- 包含目录和格式化文本
- 在文本前添加对应编号
- 自动根据缩进级别调整字号
- 最大缩进级别的字号为20px
- 每减少一级缩进，字号增加
- 插入的图片会直接显示，插入的文件会显示文件名，单击下载

## 示例

输入文件（.docformer）：
```
总标题
一级标题
 二级标题
  三级标题
另一个一级标题
\正文
[img:path]
[file:path]
```

导出后的HTML文件将包含：
- 自动生成的目录
- 格式化的文本、图片和文件，字号根据缩进级别自动调整
- 保持原有的层级结构

## 版本说明
- 1.0.x版本均为初始测试版本，小版本更新仅对于程序架构进行少量修改

## 许可证

MIT License
agentutil 0.2.0
Basic classes and utils for developing agents
Requires Python: >=3.12

# AgentUtil

Basic classes and utilities for developing agent-based applications.

## Installation

```sh
pip install agentutil
```

Or, if you are developing locally:

```sh
pip install -e .
```

Or, if you are use `uv`:
```sh
uv add agentutil
```

## Usage

### Importing

```python
from agentutil.utils.agentAssistant import TestAgentAssistant, AgentAssistant
from agentutil.agent import Agent  # or your custom agent class
from agentutil.utils.models import News
```

### Example: Custom Assistant and Agent

```python
import asyncio
from agentutil.utils.agentAssistant import AgentAssistant
from agentutil.agent import Agent
from agentutil.utils.models import News

class SJAssistant(AgentAssistant):
    def __init__(self):
        super().__init__()

    def publish_article(self, news_id: str, news: News):
        print("MY CUSTOM PUBLISH...")
        await asyncio.sleep(1)

    def update_news_status(
        self,
        news_id: str,
        new_status: str,
        title: str = None,
        cms_news_id: int = None,
        cost: int = None,
        duration=None
    ):
        print("MY CUSTOM UPDATE...")

assistant = SJAssistant()

# Example agent class inheriting from Agent
class MasterAgent(Agent):
    async def run(self, data):
        print("Running MasterAgent...")

agent = MasterAgent(assitant=assistant)
agent.assitant.update_news_status("master_agent", "running")

asyncio.run(agent.assitant.publish_article(News(title="Sample"), "test"))
```

## Custom Agent package structure

```
ai_engine/graphs/agent1
├── __init__.py
├── agent.py
├── config.py
│
├── form
│   ├── __init__.py
│   └── agent_form.py
│
├── agents
│   ├── __init__.py
│   ├── node1.py
│   ├── node2.py
│   .
│   :
│   └── nodeN.py
│
├── models
│   ├── __init__.py
│   └──  models.py
│
├── outputformats
│   ├── __init__.py
│   └── formats.py
│
├── prompts
│   ├── __init__.py
│   └──  prompts.py
│
└── tools
    ├── __init__.py
    ├── tools1.py
    ├── tools2.py
    .
    :
    └── toolsN.py
```

your `agent.py` file should be like this:

```
from agentutil.agent import Agent
from .form.agent_form import NewsForm
from .config import AGENT_CONFIG

class MasterAgent(Agent):
    def __init__(self, assistant=None, form=None):
        super().__init__(assitant=assistant)
        self.form = NewsForm
        self.config = AGENT_CONFIG

    async def run(self, data):
        # your custom agent graph
        pass
```
your `config.py` should contains these fields:
```
AGENT_CONFIG = {
    "name": "نام خط تولید",
    "description": "توضیحات مربوط به خط تولید",
    "parameters": {
    }
}
```
your `form.agent_form.py` should be like this:

```
from django import forms

class NewsForm(forms.Form):
    pass
```
## License

MIT