semvec/pypi-packages huggingface dataset:
sachi 0.4.0
Terminal media files renamer (FileBot alternative)
Requires Python: >=3.12

# sachi

Sachi is a TV Series and Movie TUI renamer made with [Textual](https://github.com/textualize/textual/).

![demo](https://github.com/NextFire/sachi/assets/20094890/e16de440-1f8f-4d69-b66b-7cef3188b883)

## Getting Started

**Requirements:** [uv](https://github.com/astral-sh/uv) and [mediainfo](https://mediaarea.net/en/MediaInfo)

<p>
  <a href="https://pypi.org/project/sachi" alt="Python version compatibility">
    <img src="https://img.shields.io/pypi/pyversions/sachi" /></a>
  <a href="https://pypi.org/project/sachi" alt="PyPI version">
    <img src="https://img.shields.io/pypi/v/sachi" /></a>
</p>

```sh
uvx sachi --help
```

Or, with Docker:

```sh
docker run --rm -it \
  -v /path/to/config:/root/.config/sachi \
  -v /path/to/media:/media \
  ghcr.io/nextfire/sachi:latest --help
```

## Features

Development is still in early stages, here's what's implemented so far:

- File selection
- TVDB series search (requires an API key)
- Movies custom renaming
- Filename jinja2 templating

In progress:

- FileBot context (https://www.filebot.net/naming.html)

The global configuration can be edited with `sachi config`.
atmst 0.0.6
A Python library for wrangling atproto-flavoured Merkle Search Trees
Requires Python: >=3.9

# atmst

[![Static Badge](https://img.shields.io/badge/license-MIT-blue)](./LICENSE) ![Static Badge](https://img.shields.io/badge/works%20on%20my%20machine-forestgreen) ![Static Badge](https://img.shields.io/badge/test%20coverage-nonzero-red) [![Static Badge](https://img.shields.io/badge/docs-exist!-orange)](https://davidbuchanan314.github.io/atmst/) ![Static Badge](https://img.shields.io/badge/cryptography-certified%20hand--rolled-yellow)

A Python library for wrangling atproto-flavoured Merkle Search Trees

Current status: ⚠️ prototype ⚠️

### Installation

```
git clone https://github.com/DavidBuchanan314/atmst
cd atmst
python3 -m pip install .
```

dev install: (editable)

```
python3 -m pip install -e .
```

Running the tests:

```
python3 -m unittest discover -v
```

build the docs:

```
cd docs/
sphinx-apidoc -f -o _apidocs/ ../src/atmst ../src/atmst/all.py
make html
# open _build/html/index.html
```

publishing to pypi: (this one is mainly for my benefit!)

```
rm -rf build/* dist/*
python3 -m build
python3 -m twine upload --repository pypi dist/*
```
Kala-NuroNetwork 0.1.0
A hybrid quantum-classical neural network framework using Kala_Quantum and Kala_Torch
Requires Python: >=3.8


# Kala_NuroNetwork

Kala_NuroNetwork is a hybrid quantum-classical neural network framework that integrates Kala_Quantum and Kala_Torch for advanced machine learning tasks.

## Features

- **Quantum Layer**: Leverage quantum circuits with Hadamard and CNOT gates for preprocessing.
- **Classical Neural Network**: Includes fully connected layers for classical computation.
- **Trainer Class**: Train and evaluate models with ease.
- **Large Dataset Support**: Handle big data with efficient batching and parallelism.

## Installation

```bash
pip install Kala_Quantum Kala_Torch torch
```

## Usage

```python
from KalaNeroNetwork import KalaNuroNetwork, KalaNuroTrainer
import torch
import torch.nn as nn
import torch.optim as optim

# Define hyperparameters
input_size = 2
n_qubits = 2
hidden_size = 128
output_size = 2
batch_size = 128
epochs = 10

# Generate synthetic dataset
def generate_large_data(num_samples, input_size):
    data = torch.rand(num_samples, input_size)
    labels = (data.sum(axis=1) > 1.0).long()  # Binary classification based on sum threshold
    return data, labels

num_samples = 10000
data, labels = generate_large_data(num_samples, input_size)
dataset = torch.utils.data.TensorDataset(data, labels)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)

# Initialize model, optimizer, and criterion
model = KalaNuroNetwork(input_size, n_qubits, hidden_size, output_size)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# Train and evaluate
trainer = KalaNuroTrainer(model, optimizer, criterion, device="cpu")

print("Starting training...")
trainer.train(data_loader, epochs)

print("Evaluating model...")
trainer.evaluate(data_loader)
```

## License

This project is licensed under the MIT License. See the LICENSE file for details.