semvec/pypi-packages huggingface dataset:
slurm-watchdog 0.1.0
Watches a SLURM job and aborts it if it stops outputting to a logfile
Requires Python: >=3.10

# Usage
Include ```slurm-watchdog``` in your slurm script to monitor the job progress and kill the job if it gets stuck.

```bash
#!/bin/bash
#SBATCH -J watchdog_example
#SBATCH -N 1
#SBATCH -n 1
#SBATCH --output stdout.txt
#SBATCH --time 1-00:00:00

source .venv/bin/activate
slurm-watchdog stdout.txt &
srun python src/slurm_watchdog/dummyjob.py &
wait $! # wait for srun to finish, but do not wait for the watchdog to finish
```
stepcutis 0.1.1
A document analysis program
Requires Python: >=3.10

# stepcutis

A document analysis program.

## Prerequisites

- Linux operating system
- CUDA >= 12.1
- Python 3.10

## Installation

Install stepcutis using pip:

```
pip install stepcutis
```

This will automatically download the necessary model files during installation.

## Usage

Run stepcutis:

```
stepcutis INPUT_DIR CHUNK_SIZE
```

or

```
python -m stepcutis.start --input_dir INPUT_DIR --chunk_size CHUNK_SIZE
```

## Uninstallation

To uninstall stepcutis, run:

```
pip uninstall stepcutis
```
```
deeptracer 0.1.3
A powerful tool for detecting and analyzing deepfake images and videos.
Requires Python: >=3.6


# DeepTracer

**DeepTracer** is a powerful Python tool designed to detect and analyze deepfake images and videos. Utilizing state-of-the-art deep learning techniques, it provides reliable predictions to identify manipulated media.

## Installation

You can install DeepTracer using pip:

```bash
pip install deeptracer
```

## Usage

here's how you can use the `deeptracer` package to detect deepfakes in images and videos.

### Import the DeepFakeDetector

To begin using DeepTracer, simply import the `DeepFakeDetector` class:

```python
from deeptracer import DeepFakeDetector
```

### Image Prediction

You can detect whether an image is a deepfake using the `predict_image` method. Here's an example:

```python
detector = DeepFakeDetector()
image_result = detector.predict_image('path\image.jpg')
print(image_result)
```

### Video Prediction

Similarly, you can analyze videos for deepfake detection using the `predict_video` method:

```python
detector = DeepFakeDetector()
video_result = detector.predict_video('path\video.mp4')
print(video_result)
```

### Expected Output

The output of both `predict_image` and `predict_video` methods will be in a format that indicates whether the media is likely to be real or fake, along with a confidence score. For example:

```python
{
  "label": "fake",  # or "real"
  "confidence": 0.95  # Confidence score in percentage
}
```

## Requirements

The following dependencies are required and automatically installed when using the package:

- `torch`
- `facenet-pytorch`
- `Pillow`
- `opencv-python`
- `numpy`
- `gdown`

These libraries are critical for the deep learning models and image/video processing.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Author

Developed by Vishwa. For any inquiries or suggestions, feel free to reach out at jvishu06@gmail.com.