semvec/pypi-packages huggingface dataset:
TuiToy 0.0.5
A terminal user interface library for Python using curses under the hood.
Requires Python: >=3.8

# Tuitoy
v 0.0.2

## Table of Content
* [About](#about)
* [To do](#to-do)
* [Version history](#version-history)

## About

Terminal User Interface Toy
This is a library designed to make terminal application by drawing windows and components using python curses

## To do
[ ] - Complete a well documented README with descriptions of all the classes and functions allowing for users to create custom components by extending existing classes

## Version history
- 0.0.1 initial upload
- 0.0.2 Hot fix for scene input handling and reorganizing the project and allows for `from tuitoy import *`
transcribify 0.1.1
A tool for transcribing audio files with optional speaker diarization
Requires Python: >=3.7

# Audio Transcriber

Audio Transcriber is a Python tool for transcribing audio files with optional speaker diarization. It provides both a GUI and a programmable interface for easy audio transcription.

## Features

- Transcribe audio files to text
- Optional speaker diarization
- User-friendly GUI
- Export results to HTML

## Installation

### Prerequisites

- Python 3.7 or higher

### Setup

We use `uv` for managing virtual environments and package installation. Follow these steps to set up the project:

#### On macOS and Linux:

```sh
# Download the setup script
curl -O https://raw.githubusercontent.com/yourusername/audio_transcriber/main/setup.sh

# Make the script executable
chmod +x setup.sh

# Run the setup script
./setup.sh
```

#### On Windows:

```powershell
# Download the setup script
Invoke-WebRequest -Uri https://raw.githubusercontent.com/yourusername/audio_transcriber/main/setup.ps1 -OutFile setup.ps1

# Set execution policy to run the script
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

# Run the setup script
.\setup.ps1
```

These scripts will:
1. Install `uv` if it's not already installed
2. Create a virtual environment
3. Activate the virtual environment
4. Install all required packages

## Usage

### GUI

To run the GUI:

```sh
python examples/simple_gui.py
```

### Programmatic Usage

```python
from audio_transcriber import initialize_models, transcribe_audio, diarize_audio

# Initialize models
initialize_models()

# Transcribe audio
transcription = transcribe_audio("path/to/your/audio/file.mp3")
print(transcription)

# Diarize audio (if available)
diarization = diarize_audio("path/to/your/audio/file.mp3")
for turn, _, speaker in diarization.itertracks(yield_label=True):
    print(f"Speaker {speaker}: {turn.start:.2f} - {turn.end:.2f}")
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Notes

While FFmpeg is not a direct requirement for this project, some underlying libraries may use it for certain audio processing tasks. If you encounter any issues with audio file handling, consider installing FFmpeg as an additional step.
django-js-lib-leaflet 1.9.4.1
Leaflet Repackaged for Django

# Leaflet JS Repackaged for Django

[Leaflet JS](https://leafletjs.com/) packaged in a Django reusable app.


## Installation

    pip install django-js-lib-leaflet

## Usage

1. Add `"js_lib_leaflet"` to your `INSTALLED_APPS` setting like this::

       INSTALLED_APPS = [
           ...
           "js_lib_leaflet",
           ...
       ]

2. In your template use:
   
       {% load static %}
   
   ...
   
       <link rel="stylesheet" href="{%static "js_lib_leaflet/leaflet.css" %}">

   ...
   
       <script src="{%static "js_lib_leaflet/leaflet.js" %}"></script>
RFDpy 0.1.1
A package for machine learning model evaluation and data processing.
Requires Python: >=3.6

# RFDpy

RFDpy is a Python package for machine learning model evaluation and data processing. It includes tools for dynamic training processing (DTP) and robust prediction data filtering (RPDF).

## Installation

You can install the package using pip:

```sh
pip install RFDpy


Usage

from RFDpy import DTP, RPDF
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=52)
train_data = list(zip(X_train, y_train))
test_data = list(zip(X_test, y_test))

# Example usage of DTP
def model_factory(regularization):
    return XGBRegressor(n_estimators=100, random_state=52,alpha=regularization)

evaluator = DTP(
    model_factory=model_factory,
    initial_train_data=train_data,
    initial_test_data=test_data,
    x_threshold=0.05,
    initial_lr=0.01,
    lr_decay=0.9,
    initial_regularization=0.001,
    regularization_decay=0.9
)
results = evaluator.run_evaluation_cycles(M=10, th_save_path='path\\to\\save\\cycle_data.txt')


for result in results:
    print(result)

evaluator.export_results_to_txt('path\\to\\save\\evaluation_results.txt', results)
evaluator.export_data_to_txt('path\\to\\save\\detailed_data.txt')

# Example usage of RPDF
rpdf = RPDF(target_occurrences=100, target_probability=0.95, error_margin=0.05)
X_train_updated, y_train_updated = rpdf.update_training_data(X_train, y_train)

xgb_regr = XGBRegressor(n_estimators=100, random_state=52)
xgb_regr.fit(X_train_updated, y_train_updated)
y_pred = xgb_regr.predict(X_test)

satisfactory_indices = rpdf.evaluate_predictions(y_test, y_pred)
satisfactory_values = y_test[satisfactory_indices]

output_file_path = 'path\\to\\save\\output_file.txt'
with open(output_file_path, 'w') as file:
    for value in satisfactory_values:
        file.write(f"{value}\n")

print(f"Filtered true values saved to: {output_file_path}")