semvec/pypi-packages huggingface dataset:
tweetcaptureplus 0.3.4
Easily take screenshots of tweets, mentions, and full threads.
Requires Python: >=3.9
Keywords: twitter, screenshot, capture, tweet, thread, mention

[![Hits](https://hits.sh/github.com/abdallahheidar/tweetcaptureplus.svg?label=TweetCapturePlus%20Views&extraCount=142)](https://hits.sh/github.com/abdallahheidar/tweetcaptureplus/)

# TweetCapturePlus

Easily take screenshots of **tweets**, **mentions**, and **full threads**.

# About This Fork

This project is a fork of xacnio/tweetcapture. It includes the following additional functionality.

**What's new**

- Take screenshots of long Tweets (that require scrolling to capture)
- Some settings are default now: overwrite, Dim mode, Capture full threads

**Bug fixes**

- Fixed an issue where the screenshot would be cut off when the tweet was too long
- Fixed issues related to the Twitter UI changes

**Deprecated Features**

- Scale, Mode

The original project is licensed under the MIT License, and this fork retains the same license.

## Command-Line Usage

```
> pip install tweetcaptureplus
> tweetcaptureplus https://x.com/elonmusk/status/1519480761749016577
> tweetcaptureplus -h
```

## Code Usage Examples

- [CLI](tweetcaptureplus/cli.py)
- [Code Examples](tweetcaptureplus/examples/)

## Testing

```
> pip install opencv-python numpy
> cd tweetcaptureplus/tests/
> python -m unittest
```

## Docker Usage

```
docker run --rm -v $(pwd):/app xacnio/tweetcaptureplus -h
docker run --rm -v $(pwd):/app xacnio/tweetcaptureplus https://x.com/elonmusk/status/1519480761749016577
```

- _<font size="1">On Windows: Replace `$(pwd)` with `${PWD}`_ (**Powershell**)
- _On Windows: Replace `$(pwd)` with `%cd%`_ (**Command Line**)</font>

## Night Modes

| #   | Night Mode | Screenshot                                                                 |
| --- | ---------- | -------------------------------------------------------------------------- |
| 0   | Default    | <img src="/tweetcaptureplus/assets/night_mode_default.png" width="500">    |
| 1   | Dim        | <img src="/tweetcaptureplus/assets/night_mode_dim.png" width="500">        |
| 2   | Lights out | <img src="/tweetcaptureplus/assets/night_mode_lights_out.png" width="500"> |

## Show Mentions Example

_If the tweet have a very many mentions, there may be problems because "show more" option not supported. The tool can show only first loaded mentions. You can limit mention count on screenshot by using -sc <count> argument_

```
tweetcaptureplus -sm 3 https://twitter.com/Twitter/status/1445078208190291973
```

<img src="https://i.imgur.com/IZ0GHl8.png" />

## Show Parent Tweets Example

**NOTICE:** You have to be logged in the show the full thread.

```
tweetcaptureplus -sp https://x.com/elonmusk/status/1746970616060580326
```

<img src="/tweetcaptureplus/assets/@elonmusk_1746970616060580326.png" width="500">
captchabreaker 0.1.1
A library to break captchas using ONNX models.
Requires Python: >=3.9

# Captcha Breaker [![PyPI - Latest Version](https://img.shields.io/pypi/v/captchabreaker)](https://pypi.org/project/captchabreaker/) [![GitHub Tag](https://img.shields.io/github/v/tag/ramSeraph/captchabreaker?filter=v*)](https://github.com/ramSeraph/captchabreaker/releases/latest)

A library to break captchas using a deep learning model.

## Provenance

This project's inference code is adapted from the [Hugging Face Space by docparser](https://huggingface.co/spaces/docparser/Text_Captcha_breaker/tree/main).

The underlying model is a copy of [DocParser/captcha](https://huggingface.co/DocParser/captcha), licensed under CC BY 4.0. 

A local copy of the model is hosted in this repository for convenience [https://github.com/ramSeraph/captchabreaker/releases/tag/model](https://github.com/ramSeraph/captchabreaker/releases/tag/model).

## Installation

To install the necessary dependencies, run:

```bash
pip install captchabreaker
```

## Usage

To use the captcha breaker, you can use the `captchabreaker.solve()` function.
It takes a PIL Image object as input and returns the predicted text.

Here is an example:

```python
from PIL import Image
import captchabreaker

if __name__ == '__main__':
    # This is an example of how to use the captchabreaker package.
    # You need to have a captcha image file (e.g., captcha.png) in the same directory.
    try:
        img = Image.open("sample.png")
        text = captchabreaker.solve(img)
        print(f"Predicted text: {text}")
    except FileNotFoundError:
        print("Error: captcha.png not found. Please provide a captcha image.")
```
roadintel 1.0.0
A Plotly-based visualization library for road accident data analysis.
Requires Python: >=3.7

# 🚗 RoadIntel – Road Accident Data Visualization Library

**RoadIntel** is a Python library built on **Plotly Express** for interactive visualization of road accident datasets.  
It helps researchers, analysts, and students uncover meaningful patterns in road safety data through clean, reusable visualization functions.

---

## 📦 Installation

```bash
pip install roadintel
```

---

## 🧠 Features

- Create rich, interactive charts using Plotly Express  
- Analyze accident data by state, vehicle type, road condition, and more  
- Automatically generate human-readable insights  
- Optimized for dark-themed dashboards  
- Modular and ready for integration in Dash or Jupyter apps  

---

## 📊 Example Usage

```python
import pandas as pd
from roadintel import charts

# Load dataset
df = pd.read_csv('road_accidents.csv')

# Example 1: Create India Map
fig = charts.create_india_map_chart(df, geojson_data, 'plotly_dark')
fig.show()

# Example 2: Accident Trend
charts.create_monthly_trend_chart(df, 'plotly_dark').show()
```

---

## 📈 Available Charts

| Function | Description |
|-----------|--------------|
| `create_india_map_chart` | Choropleth map of state-wise accidents |
| `create_monthly_trend_chart` | Monthly accident trend (line chart) |
| `create_state_bar_chart` | Top 10 states by accident count |
| `create_severity_donut_chart` | Accident severity donut chart |
| `create_road_type_chart` | Severity comparison across road types |
| `create_vehicle_bar_chart` | Top 5 vehicle types involved |
| `create_alcohol_pie_chart` | Alcohol involvement in accidents |
| `create_weather_light_chart` | Weather & lighting impact |
| `create_age_casualty_bar_chart` | Casualties by driver age group |
| `calculating_insights` | Generates 5 textual insights from dataset |

---

## 🧾 License

Licensed under the **MIT License**.

---

## 👨‍💻 Author

**Anubhav Shukla**  
M.Sc. Data Science | Central University of Haryana  
📧 Email: anubhavshukla870@gmail.com  
🌐 GitHub: [https://github.com/mr-robot369](https://github.com/mr-robot369)
python-custom-exceptions 0.0.2
A collection of custom exceptions for Python projects.
Requires Python: >=3.11

# Python Custom Exceptions
A repository containing custom exception classes for Python projects. <br>
These exceptions are designed to provide more specific error handling and improve code readability. 
They can be easily integrated into any Python application to enhance error management. <br>

---

### Features
- ✅ Specific Exception Classes: Custom exceptions for various error scenarios.
- ✅ Improved Readability: Clearer error handling in code.
- ✅ Easy Integration: Simple to add to existing Python projects.
- ✅ Standardized Error Handling: Consistent approach to managing exceptions.
- ✅ Structured Hierarchy: Organized exception classes for better maintainability.

---

### Installation
```bash
pip install python-custom-exceptions
```

---

### Usage Examples
```python
import os
from python_custom_exceptions import DiagnosticInfo, IsNotExistException


class DiagnosticInfoTest(DiagnosticInfo):
    file_path: str


file_path = "test_dir"
os.mkdir(file_path)
if not os.path.exists(file_path):
    raise IsNotExistException(
        subject="Directory",
        diagnostic_info=DiagnosticInfoTest(
            file_path=file_path
        )
    )
```

---

### 🤝 Contributing
If you have a helpful tool, pattern, or improvement to suggest:
Fork the repo <br>
Create a new branch <br>
Submit a pull request <br>
I welcome additions that promote clean, productive, and maintainable development. <br>

---

### 🙏 Thanks
Thanks for exploring this repository! <br>
Happy coding! <br>
roadviz 1.0.0
A Plotly-based visualization library for road accident data analysis.
Requires Python: >=3.7

# 🚗 RoadIntel – Road Accident Data Visualization Library

**RoadIntel** is a Python library built on **Plotly Express** for interactive visualization of road accident datasets.  
It helps researchers, analysts, and students uncover meaningful patterns in road safety data through clean, reusable visualization functions.

---

## 📦 Installation

```bash
pip install roadintel
```

---

## 🧠 Features

- Create rich, interactive charts using Plotly Express  
- Analyze accident data by state, vehicle type, road condition, and more  
- Automatically generate human-readable insights  
- Optimized for dark-themed dashboards  
- Modular and ready for integration in Dash or Jupyter apps  

---

## 📊 Example Usage

```python
import pandas as pd
from roadintel import charts

# Load dataset
df = pd.read_csv('road_accidents.csv')

# Example 1: Create India Map
fig = charts.create_india_map_chart(df, geojson_data, 'plotly_dark')
fig.show()

# Example 2: Accident Trend
charts.create_monthly_trend_chart(df, 'plotly_dark').show()
```

---

## 📈 Available Charts

| Function | Description |
|-----------|--------------|
| `create_india_map_chart` | Choropleth map of state-wise accidents |
| `create_monthly_trend_chart` | Monthly accident trend (line chart) |
| `create_state_bar_chart` | Top 10 states by accident count |
| `create_severity_donut_chart` | Accident severity donut chart |
| `create_road_type_chart` | Severity comparison across road types |
| `create_vehicle_bar_chart` | Top 5 vehicle types involved |
| `create_alcohol_pie_chart` | Alcohol involvement in accidents |
| `create_weather_light_chart` | Weather & lighting impact |
| `create_age_casualty_bar_chart` | Casualties by driver age group |
| `calculating_insights` | Generates 5 textual insights from dataset |

---

## 🧾 License

Licensed under the **MIT License**.

---

## 👨‍💻 Author

**Anubhav Shukla**  
M.Sc. Data Science | Central University of Haryana  
📧 Email: anubhavshukla870@gmail.com  
🌐 GitHub: [https://github.com/mr-robot369](https://github.com/mr-robot369)
adc-aios3 0.1.0
Async S3 client with lifecycle management
Requires Python: >=3.8
Keywords: async, aws, s3, storage

# ADC-AioS3

ADC-AioS3 is a modern, async-first S3 client library for Python that provides a clean and intuitive interface for working with S3-compatible storage services.

## Features

- 🚀 Fully async/await based
- 🔄 Automatic lifecycle management
- 📦 Support for multipart uploads
- 🔗 Presigned URL generation
- 📝 Comprehensive metadata handling
- 🛡️ Error handling and connection management

## Installation

```bash
pip install adc-aios3
```

## Quick Start

```python
import asyncio
from adc_aios3 import S3Client

async def main():
    # Create and start the client
    client = await S3Client.create(
        bucket="my-bucket",
        url="http://localhost:9000",
        access_key="minioadmin",
        secret_key="minioadmin"
    )

    # Upload a file
    with open("example.txt", "rb") as f:
        key = await client.upload(f, "example.txt")

    # Download a file
    data = await client.download(key)
    
    # Generate a presigned URL
    url = await client.generate_download_url(key, expiration=3600)

    # Clean up
    await client.close()

if __name__ == "__main__":
    asyncio.run(main())
```

## Documentation

For more detailed documentation, visit [adc-aios3.readthedocs.io](https://adc-aios3.readthedocs.io/).

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
diffcrysgen 0.1.0
"DiffCrysGen is a score-based diffusion model for accelerated design of diverse inorganic crystalline materials."
Requires Python: >=3.11

# DiffCrysGen [![Project Version](https://img.shields.io/badge/version-v0.1.0-blue)](https://github.com/SouravMal/DiffCrysGen)

DiffCrysGen is a score-based diffusion model. It treats the entire materials representation with a single, unified diffusion process, allowing a single denosing neural network to predict a holistic score for the entire noisy crystal data. This unified treatment significantly simplifies the architecture and improves the computational efficiency.


<p align="center">
  <img src="images/logo-DiffCrysGen.png" alt="DiffCrysGen Logo" width="350"/>
</p>


## Generative diffusion framework in DiffCrysGen
<img src="images/diffusion-schematic.png" alt="DiffCrysGen Schematic" width="550">

---

## Architecture of the denoising neural network
<img src="images/architecture.png" alt="DiffCrysGen Architecture" width="750">

---

## Installation

### Prerequisites

The package requires specific environments and dependencies.
Using a virtual environment is highly recommended.
**Environment Setup using Conda**

```
conda create -n diffcrysgen python=3.11
conda activate diffcrysgen
```

### Install from PyPI
```
pip install diffcrysgen
```

### Install from Source Code
```
git clone https://github.com/SouravMal/DiffCrysGen.git
cd DiffCrysGen
pip install -e .
```

## Quick Start
For a simple walkthrough of generating materials and analyzing them, see the [DiffCrysGen Demo Notebook](./notebooks/DiffCrysGen-demo.ipynb).

## License

This project is licensed under the **MIT License**.

See the [LICENSE](LICENSE) file for details.

Developed by: [Sourav Mal](https://github.com/SouravMal) at Harish-Chandra Research Institute (HRI) (https://www.hri.res.in/), Prayagraj, India.


## Citation

Please consider citing our work if you find it helpful:

```bibtex
@misc{mal2025generativediffusionmodeldiffcrysgen,
      title={Generative Diffusion Model DiffCrysGen Discovers Rare Earth-Free Magnetic Materials}, 
      author={Sourav Mal and Nehad Ahmed and Subhankar Mishra and Prasenjit Sen},
      year={2025},
      eprint={2510.12329},
      archivePrefix={arXiv},
      primaryClass={cond-mat.mtrl-sci},
      url={https://arxiv.org/abs/2510.12329}, 
}
```


## Contact

If you have any questions, feel free to reach us at:
**Sourav Mal** <souravmal492@gmail.com>