semvec/pypi-packages huggingface dataset:
tarta-api 1.0.0
Python wrapper for the Tarta API, designed to seamlessly integrate AI-powered job search functionality into your applications.
Requires Python: >=3.6

# TartaAPI-Python

## Overview
TartaAPI-Python is a Python wrapper for the Tarta API, designed to seamlessly integrate AI-powered job search functionality into your applications.

## API Documentation
For detailed API documentation, visit:  
[Tarta API Documentation](https://api.tarta.ai/swagger/index.html)

## What is Tarta?
Tarta.ai is a sophisticated AI platform that consolidates job listings from a wide range of sources, including job boards and social media. The platform streamlines the job search experience, matching users with suitable vacancies and even facilitating interview scheduling.

To learn more, visit:  
[Tarta Official Website](https://tarta.ai/)

## ChatGPT Integration
Tarta is also available as a ChatGPT plugin to enhance your job search experience:  
[Tarta ChatGPT Plugin](https://chat.openai.com/g/g-Gkf9YM4sR-job-search-in-the-us)

## Installation
To install TartaAPI-Python via pip, use the following command:
```sh
pip install tarta-api
```

## Usage Example
Here's a simple example demonstrating how to use the library to execute a job search:

```python
from tarta_api.job_search import JobSearchService, JobSearchRequest

def search_jobs(title: str, size: int = 5):
    request = JobSearchRequest(title=title, size=size)
    
    try:
        results = JobSearchService.search_jobs(request)
        jobs = results.get("jobs", [])
        
        if not jobs:
            print("No job results found.")
            return
        
        print("Job Search Results:")
        for job in jobs:
            job_name = job.get("name", "N/A")
            company = job.get("companyName", "N/A")
            location = job.get("location", {})
            city = location.get("city", "Unknown")
            state = location.get("state", "Unknown")
            country = location.get("country", "Unknown")
            source = job.get("feed", "N/A")
            posted_date = job.get("created", "N/A")
            
            print(f"Job: {job_name} at {company}")
            print(f"Location: {city}, {state}, {country}")
            print(f"Source: {source}")
            print(f"Posted: {posted_date}\n")
    
    except Exception as e:
        print(f"Error fetching job results: {e}")

search_jobs("python", size=2)

```

## Key Features
- **Effortless API Integration**: Interact with Tarta's API using straightforward Python methods.
- **AI Matching**: Utilize AI algorithms to find jobs that align with user profiles and preferences.
- **Interview Scheduling**: Automate interview scheduling to streamline the application process.

## Contributions
Contributions are welcome! Feel free to open issues or submit pull requests to enhance the library.

## License
This project is under the MIT License.

---
Experience the efficiency of Tarta and integrate AI-powered job search into your Python applications today!
mobilefacenet-pytorch 0.1.0
uv-compatible fork of PyTorch implementation of MobileFaceNets
Requires Python: >=3.13

# MobileFaceNets

![apm](https://img.shields.io/apm/l/vim-mode.svg)

PyTorch implementation of MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices.
[paper](https://arxiv.org/abs/1804.07573).

## Performance

|Accuracy|LFW|MegaFace|Download|
|---|---|---|---|
|paper|99.55%|92.59%||
|ours|99.48%|82.55%|[Link](https://github.com/foamliu/MobileFaceNet/releases/download/v1.0/mobilefacenet_scripted.pt)|

## Dataset
### Introduction

Refined MS-Celeb-1M dataset for training, 3,804,846 faces over 85,164 identities. 
LFW and Megaface datasets for testing.

## Dependencies
- Python 3.6.8
- PyTorch 1.3.0

## Usage

### Data preprocess
Extract images:
```bash
$ python extract.py
$ python pre_process.py
```

### Train
```bash
$ python train.py
```

To visualize the training process：
```bash
$ tensorboard --logdir=runs
```
ak_transcribe 0.0.7
Transcribe Media Files
Requires Python: >=3.11.0

<!--- Heading --->
<div align="center">
  <h1>ak_transcribe</h1>
</div>
<br />

<!-- Table of Contents -->
<h2>Table of Contents</h2>

- [Getting Started](#getting-started)
  - [Prerequisites](#prerequisites)
  - [Installation](#installation)
- [Usage](#usage)
- [License](#license)
- [Contact](#contact)

<!-- Getting Started -->
## Getting Started

<!-- Prerequisites -->
### Prerequisites

Python 3.11 or above

**Note for Ubuntu/Debian:**
This project uses `python-magic` to identify file types. So the libmagic C library needs to be installed need to be installed. See [python-magic](https://pypi.org/project/python-magic/) for more information.

For Debian/Ubuntu systems:

- `sudo apt-get install libmagic1`

For OSX:

- When using Homebrew: `brew install libmagic`
- When using macports: `port install file`

For Windows:

- _No additional requirements_

<!-- Installation -->
### Installation

```bash
  pip install ak_transcribe
```

Check configs

```python
  import ak_transcribe
  ak_transcribe.test_configs()
```
<!-- Usage -->
## Usage


```python
from ak_transcribe import Transcriber
from pathlib import Path
res = Transcriber.process(filepath=Path("path/to/media/file"))

# Write Json to file
with open(filepath.with_suffix('.json'), 'w', encoding='utf-8') as f:
    f.write(res.json)

# Return Srt file contents
res.srt

# Embed subtitle file into the video
res.embed_srt(filepath=Path("path/to/media/file"))

# Return Transcript text
res.txt

# Extract txt from srt file
from ak_transcribe import converter
converter.srt_to_txt("path/to/srt/file")

# Read existing srt
from ak_transcribe import TranscribedContent
TranscribedContent(Path("path/to/JSON/file"))

## Alternatively, pass dictionary from json file
import json
with open(Path("path/to/JSON/file"), "r", encoding="utf-8") as f:
    return TranscribedContent(results=json.load(f))
```

<!-- License -->
## License

See [LICENSE](/LICENSE) for more information.

<!-- Contact -->
## Contact

Arun Kishore - [@rpakishore](mailto:pypi@rpakishore.co.in)

Project Link: [https://github.com/rpakishore/ak_transcribe](https://github.com/rpakishore/ak_transcribe)
django-invoke 0.0.0.dev2
Collection of frequently used tasks for Django development
Requires Python: >=3.9

## django-invoke
[![PyPI](https://img.shields.io/pypi/v/django-invoke.svg)](https://pypi.org/project/django-invok/)

Collection of frequently used tasks for Django development. It's built on top of [invoke](https://github.com/pyinvoke/invoke) to streamline how to manage task execution.

### Configuration
Add a `tasks.py` to your project
```
import invoke

import django_invoke

ns = invoke.Collection(
    django_invoke.docker,
    django_invoke.django,
)


# # Configurations for run command
ns.configure(
    dict(
        run=dict(
            pty=True,
            echo=True,
        ),
        django_invoke=django_invoke.Config(
            docker=django_invoke.DockerSettings(
                main_containers=["container1", "container2"],
            ),
            django=django_invoke.DjangoSettings(
                settings_path="<relative/path/to/django_settings>",
            ),
        )
    ),
)
```

### Usage
Here a few commands that you can use:
```
inv docker.up
inv docker.stop
inv django.run
inv django.migrate
inv django.makemigrations
inv django.check-new-migrations
inv django.db
inv django.dbshell
```
mcp-browser-use-tools 0.1.6
This package vendors a subset of [browser-use](https://github.com/browser-use/browser-use) with minimal dependencies exposed as an mcp-server.
Requires Python: >=3.11

# mcp-browser-use-tools

This package vendors a subset of [browser-use](https://github.com/browser-use/browser-use) with minimal dependencies exposed as an mcp-server.

*Note* this does not wrap the full browser-use agent. There are other MCP's that do that. This one only exposes the internal tools for folks who want to roll their own agent loop.

Supports the following tools:

 - done
 - search_google
 - go_to_url
 - go_back
 - wait
 - click_element
 - input_text
 - switch_tab
 - open_tab
 - extract_content
 - scroll_down
 - scroll_up
 - send_keys
 - scroll_to_text
 - get_dropdown_options
 - select_dropdown_option
quickemail 0.4.0
Quick send email use python3.

# QuickEmail

#### 项目介绍

        简易的邮件发信工具。支持认证/不认证发信。
        使用UTF-8编码。可以一次连接声明多收件人、抄送人

#### 使用说明

        简易的邮件发信工具。支持认证/不认证发信。
        使用UTF-8编码。
        pip install quickemail
        from quickemail import QuickEmail

1. 发信前先定义发信要素：

        指定主机和端口，必须参数：
        quicksend = QuickEmail('mail.test.com', 25)    # SMTP发信端口，默认是25 SSL链接使用465
        quicksend.debug = True                         # 打开调试，发送错误会抛出异常信息
        定义HELO主机名，参数可省略：
        quicksend.helo = 'QuickEmail'                 # HELO主机名不能使用有空格的字符串，可以省略
        quicksend.tls = True / quicksend.ssl = True   # 开启startTLS或者SSL，SSL端口一般为465
        认证用户名和密码，authsend()必须的参数：
        quicksend.mail_user = 'a'                     # 认证用户名
        quicksend.user_pass = 'test'                  # 认证用户的密码
    
        发信人、收信人为必须参数：
        quicksend.mail_from = 'AA高A<a@test.com>'    # 发信人地址，格式为:   FullName<email address>
        quicksend.mail_to = '一二三<123@test.com>,ABC<abc@test.com>'    # 格式同上，多地址使用逗号","分隔
    
        抄送人，可省略：
        quicksend.mail_cc = '一二三<123@test.com>'
        
        邮件时间定义，可省略：
        quicksend.mail_date = 1691544067
    
        邮件主题、内容，支持使用HTML： 
        quicksend.mail_subject = 'mY subject还有中文!'
        quicksend.mail_content = '<font color=red>red content一段中文</font>'
        quicksend.is_html = True
    
        添加附件，可省略：
        quicksend.mail_attach = ['abc.jpg']           # 添加附件 类型为list
        
        高级定制，可省略：
        quicksend.content_from = '邮件显示的假发件人<from@test.com>'      # 定制邮件信体显示的发件人，一般为来隐藏实际的mail_from。
        quicksend.content_to = '邮件显示的假收件人<to@test.com>'      # 定制邮件信体显示的收件人，一般为来隐藏实际的mail_to。
        quicksend.content_cc = '邮件显示的假抄送人<cc@test.com>'      # 定制邮件信体显示的抄送人，一般为来隐藏实际的mail_cc。

2. 使用creatmsg()建立邮件信体内容。(可以忽略)

        msg = quicksend.creatmsg()

3. 使用authsend()或者send()发送邮件，creatmsg()未执行，参数msg可忽略。

        quicksend.authsend(msg)
        quicksend.send()

4. 错误显示(成功返回True，失败返回False)

        result = quicksend.authsend(msg)
        if result:
            print("ok")
            
    打开调试，发送错误会抛出异常信息
    
        quicksend.debug = True
        try:
            quicksend.authsend()
        except Exception as e:
            print(e)
hack4u-slowie-version 0.1.1
Biblioteca para conultar cursos de Hack4u


Hack4U Academy Courses Library

Una biblioteca Python para consultar cursos de la academia Hack4U.
Cursos disponibles:

    Introducción a Linux [15 horas]
    Personalización de Linux [3 horas]
    Introducción al Hacking [53 horas]

Instalación

Instala el paquete usando pip3:

pip3 install hack4u

Uso básico
Listar todos los cursos

from hack4u import list_courses

for course in list_courses():
    print(course)

Obtener un curso por nombre

from hack4u import get_course_by_name

course = get_course_by_name("Introducción a Linux")
print(course)

Calcular duración total de los cursos

from hack4u.utils import total_duration

print(f"Duración total: {total_duration()} horas")
mesa-py 0.1.2
Multiomics and Ecological Spatial Analysis for Quantitative Decoding of Cellular Neighborhoods and Tissue Compartments
Requires Python: >=3.10

# MESA: Multiomics and Ecological Spatial Analysis for Quantitative Decoding of Tissue Disease States

<p align="center">
  <img src="https://raw.githubusercontent.com/Feanor007/MESA/refs/heads/main/docs/_static/images/figure1_new.png" width="75%" height="75%">
</p>

MESA is a novel pipeline that brings ecological principles together with multiomics data integration thereby enabling deeper and more quantitative decoding of the functional and spatial shifts of tissue remodeling across disease states. 
- Drawing inspiration from ecological studies, MESA adapts diversity metrics traditionally used to gauge biodiversity for spatial omics data, creating tools for systematic quantification of cellular diversity. Specifically, we introduce a multi-scale diversity index, alongside global and local diversity indices, to capture not only the overarching diversity of a tissue but also the localized patterns and dependencies.
- Furthermore, MESA employs a multi-omics approach to spatial omics analyses. MESA in silico amalgamates cross-modality single-cell data to enrich the context of spatial omics observations. With the additional layers of information brought to bear by multiomics, MESA facilitates an extended view of cellular neighborhoods and their spatial interactions within tissue microenvironments. MESA's approach, incorporating differential expression, gene set enrichment, and ligand-receptor interaction analyses within these spatially defined cellular assemblies, further enhances a mechanistic understanding of tissue remodeling across disease states.

## Installation

MESA is hosted on `pypi` and can be installed via `pip`. 

```
pip install mesa-py
```
Visit our [documentation](https://mesa-py.readthedocs.io/en/latest/) to see examples and tutorials!

## License
```MESA``` is under the [Academic Software License Agreement](https://github.com/Feanor007/MESA/blob/main/LICENSE), please use accordingly.
SCHdeepinsight 0.3.5
A tool for processing and hierarchically annotating immune scRNA-seq data with DeepInsight and CNN.
Requires Python: >=3.6

# SCHdeepinsight

SCHdeepinsight is a Python package for hierarchical annotation of immune cells in single-cell RNA sequencing (scRNA-seq) data. By combining **DeepInsight** transformation with a **hierarchical CNN model**, it provides accurate classification of immune cell types with both base-level and detailed subtype identification.

## Features

- **Batch Correction**: Aligns query data with the integrated reference dataset using STACAS integration
- **Image Transformation**: Converts gene expression matrices to image representations
- **Hierarchical Classification**: Two-level annotation providing both major cell types and detailed subtypes
- **Rare Cell Detection**: Identifies potential rare cell populations based on prediction confidence

## Installation

Install SCHdeepinsight using pip:

```bash
pip install SCHdeepinsight
```

Additionally, the package requires pyDeepInsight:

```bash
pip install git+https://github.com/alok-ai-lab/pyDeepInsight.git#egg=pyDeepInsight
```

## R Dependencies

For batch correction functionality, the following R packages are required:

```r
# Install required CRAN packages
install.packages(c("Seurat", "Matrix", "SeuratDisk"))

# Install packages from GitHub
if (!requireNamespace("remotes", quietly = TRUE)) {
  install.packages("remotes")
}

# Install STACAS
remotes::install_github("carmonalab/STACAS")
```

## Usage

Here's how to use SCHdeepinsight to analyze your scRNA-seq data:

```python
from SCHdeepinsight import immune

# Initialize with output directory
classifier = immune("./output_dir")

# Complete pipeline: batch correction, image transformation, and prediction
results = classifier.run_pipeline(
    input_file="path/to/query.h5ad",
    ref_file="path/to/reference.rds", 
    batch_size=128
)

# Access prediction results
print(results.head())

# Save results
results.to_csv("./immune_cell_predictions.csv")
```

### Step-by-Step Approach

For more fine-grained control, you can run each step separately:

```python
from SCHdeepinsight import immune

# Initialize
classifier = immune("./output_dir")

# Step 1: Batch correction
corrected_file = classifier.batch_correction(
    input_file="path/to/query.h5ad",
    ref_file="path/to/reference.rds"
)

# Step 2: Transform gene expression to images
classifier.image_transform(corrected_file)

# Step 3: Predict cell types
results = classifier.predict(
    batch_size=128,
    rare_base_threshold=60,
    rare_detailed_threshold=10
)
```

## Prediction Results

The results DataFrame contains:

- `barcode`: Cell identifiers
- `predicted_base_type`: Major cell type classification
- `predicted_detailed_type`: Detailed subtype classification
- `base_type_probability`: Confidence score for base type prediction
- `detailed_type_probability`: Confidence score for detailed type prediction
- `is_potential_rare`: Boolean flag for potential rare cell types
initkit 0.2.0
A Flask CLI tool for generating starter templates
Requires Python: >=3.7
Keywords: flask, cli, template, starter, boilerplate


# 🚀 Flask Blog Starter

A **minimal yet powerful** Flask blog starter template. This project provides a **structured foundation** for building a Flask-based blog with **SQLAlchemy** and **SQLite**.

---

## ✨ Features

✅ **SQLite Database** – Quick and lightweight.
✅ **Scalable Flask Structure** – Organized for **large** applications.
✅ **Virtual Environment Support** – Keep dependencies isolated.
✅ **Easy Configuration** – Modify settings via `config.py`.

---

## 📥 Installation

### 1️⃣ Clone the Repository
```bash
git clone https://github.com/lyznne/flask-blog-starter.git
cd flask-blog-starter
```

### 2️⃣ Create a Virtual Environment (Recommended)
```bash
python3 -m venv venv
```

### 3️⃣ Activate the Virtual Environment

**Linux / Mac:**
```bash
source venv/bin/activate
```
**Windows (CMD / PowerShell):**
```bash
venv\Scripts\activate
```

### 4️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```

---

## 🚀 Usage

Run the application using:
```bash
python run.py
```
The development server will start at:
🌎 **[http://localhost:5000/](http://localhost:5000/)**

---

## ⚙️ Configuration

Modify **`config.py`** to adjust settings like:
- Database URI
- Secret keys
- Debug mode

---

## 👥 Contributing

🎉 **Contributions are welcome!**
- **💡 Found a bug?** Open an issue!
- **📢 Have suggestions?** Submit a pull request!

---

## 📜 License

📄 **MIT License** – You are free to use and modify the code!

---

## 🎯 Acknowledgements

This project was inspired by the **Flask Mega-Tutorial**.

📚 **Resources**:
- 📖 Flask Docs → [Flask Documentation](https://flask.palletsprojects.com/)
- 🛠️ SQLAlchemy → [SQLAlchemy Documentation](https://docs.sqlalchemy.org/)

---

## 🌍 Live Demo

🔗 **Check it out here** → [Flask Blog Starter](https://enoslyznne.pythonanywhere.com/)

---

### 🚀 **Happy Coding!** 🎉
seremi 0.0.2
Read SER & EMI microscope images and metadata.
Requires Python: >=3.8

# seremi

Read SER microscope image files and the associated EMI metadata files.

Install from [PyPI](https://pypi.org/project/seremi/): `pip install seremi`

[Docs](https://thenelsonlab.github.io/seremi/build/html/index.html)
BeautifulReport-fix 0.1.3
unittest自动化测试的可视化报告模板-修复源版本

# BeautifulReport

一个基于unittest.TestResult模块实现的测试用例模板, 可把测试中的结果通过BeautifulReport整合成一个可视化的报表.

### 如何使用它?

```shell
>>> pip install BeautifulReport
```


```python

import unittest
from BeautifulReport import BeautifulReport

if __name__ == '__main__':
    test_suite = unittest.defaultTestLoader.discover('./tests', pattern='test*.py')
    result = BeautifulReport(test_suite)
    result.report(filename='测试报告', description='测试deafult报告', report_dir='report', theme='theme_default')

```

### Report API简介

* BeautifulReport.report
    * report (  
        filename -> 测试报告名称, 如果不指定默认文件名为report.html  
        description -> 测试报告用例名称展示  
        report_dir='.' -> 报告文件写入路径  
        theme='theme_default' -> 报告主题样式 theme_default theme_cyan theme_candy theme_memories  
    )

* BeautifulReport.add_test_img

如果使用报告过程中需要把测试报告的截图放在报告中, 可以使用add_test_img方法

* add_test_img (
    *pargs
)

可以在测试用例上挂载一个装饰器, 实例内容如下

    
* 默认存放的图片路径是img, 需要在当前测试项目的启动路径下, 创建一个img文件夹
* 传递给装饰器的图片,在运行测试前可以不存在, 运行测试之后生成即可.
* 当文件在报告中展示后, 想要看到原图, 可以点击报告中的缩略图查看完整的截图

```python
import unittest
from BeautifulReport import BeautifulReport


class UnittestCaseSecond(unittest.TestCase):
    """ 测试代码生成与loader 测试数据"""
    
    def test_equal(self):
        """
        test 1==1
        :return:
        """
        import time
        time.sleep(1)
        self.assertTrue(1 == 1)
    
    @BeautifulReport.add_test_img('测试报告.png')
    def test_is_none(self):
        """
        test None object
        :return:
        """
        save_some_img('测试报告.png')
        self.assertIsNone(None)
```

* 运行sample之后生成如下报告

![image](http://mocobk.test.upcdn.net/image/img20190325125101.jpg)

* 主题样式

![image](http://mocobk.test.upcdn.net/image/img20190325124850.jpg)

感谢原作者 [TesterlifeRaymond](https://github.com/TesterlifeRaymond/BeautifulReport) 的代码贡献，本人在原基础上修复了部分bug并增加了主题样式的自定义