semvec/pypi-packages huggingface dataset:
pybinlock 0.7.0
A python package for programmatically reading, writing, and managing Avid Media Composer bin locks (`.lck` files).
Requires Python: >=3.6
Keywords: avid, media composer, nle, editorial, post, production, film, television, nexis, avb, bins, lock, lck

# pybinlock

`binlock` is a python package for programmatically reading and writing Avid bin lock (`.lck`) files in 
multi-user Avid Media Composer projects.

>[!WARNING]
>While the `.lck` lock file format is a very simple one, it is officially undocumented.  Use this library at your own risk --
>I assume no responsibility for any damage to your project, loss of data, or underwhelming box office performance.

## Interesting Uses

- Permanently locking bins
- Temporarily locking bins while programmatically reading/writing to them
- Custom lock names for displaying short messages, such as why the bin is locked
- Removing "stale" locks

## Installation

Install the `pybinlock` package [from PyPI](https://pypi.org/project/pybinlock/) using `pip`:

```bash
pip install pybinlock
```

Or clone from this repo:

```bash
git clone https://github.com/mjiggidy/pybinlock.git
cd pybinlock
pip install .
```

Now you can import `binlock`!

```python
from binlock import BinLock

with BinLock("Do Not Touch").hold_bin("Reel 1.avb") as my_lock:
  print(f"Bin is now locked as {my_lock.name}")
  ...
print("Bin is now unlocked!")
```

## Usage

See [readthedocs.io](https://pybinlock.readthedocs.io) for general usage and API documentation!

## See Also
- [`pybinhistory`](https://github.com/mjiggidy/pybinhistory) - Programmatically read and write Avid bin history log (`.log`) files
xander-cli 0.1
Xander AI CLI for training AI models
Requires Python: >=3.7

# Xander AI CLI

Xander AI CLI is a command-line tool for training AI models with ease. It allows you to train classification, regression, text, and image models using a simple command.

## Installation

Install Xander AI CLI from PyPI using:

```sh
pip install xander-cli
```

#### **Image Task**
- **Dataset Format**:
  - Provide a `.zip` file containing a folder.
  - Inside the folder:
    - Subfolders represent class labels.
    - Images within subfolders correspond to their class.

##### Example Directory Structure:
```
dataset.zip
│
├── class_1/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
│
├── class_2/
│   ├── image1.jpg
│   ├── image2.jpg
│   └── ...
│
└── class_n/
    ├── image1.jpg
    ├── image2.jpg
    └── ...
```

## Usage

### **Train a Model**
To train a regression/classification/text model, use the following command:

```sh
xander-cli train <task> <dataset_path> <model_name> <epochs> [--target_col <target_column>]
```

### **Arguments**
| Argument        | Description                                          | Example                          |
|----------------|------------------------------------------------------|----------------------------------|
| `<task>`       | Type of task: `classification`, `regression`, `text`, or `image` | `classification` |
| `<dataset_path>` | Path to your dataset file (CSV format) | `data.csv` |
| `--target_col` | Name of the target column in your dataset (not requiredfor image) | `label` |
| `<model_name>` | Name to save your trained model | `my_model` |
| `<epochs>` | Number of epochs for training | `50` |

### **Example Usage for tabular tasks**
```sh
xander-cli train classification data.csv my_model 50 --target_col label
```

### **Example Usage for image tasks**
```sh
xander-cli train image images.zip my_image_model 20
```

This command will train a classification model using `data.csv`, with `label` as the target column, and train it for 50 epochs.

## Author
**Xander Co.**