semvec/pypi-packages huggingface dataset:
teachable-machine 1.2.1
A Python package designed to simplify the integration of exported models from Google's Teachable Machine platform into various environments.     This tool was specifically crafted to work seamlessly with Teachable Machine, making it easier to implement and use your trained models.
Requires Python: >=3.7

# Teachable Machine
_By: [Meqdad Darwish](https://github.com/MeqdadDev)_


<p align="center">
<picture>
  <img alt="Teachable Machine Package Logo" src="https://meqdaddev.github.io/teachable-machine/logo.png" width="80%" height="80%" >
</picture>
</p>

[![Downloads](https://static.pepy.tech/badge/teachable-machine)](https://pepy.tech/project/teachable-machine)
[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)
[![PyPI](https://img.shields.io/pypi/v/teachable-machine)](https://pypi.org/project/teachable-machine/)

A Python package designed to simplify the integration of exported models from Google's [Teachable Machine](https://teachablemachine.withgoogle.com/) platform into various environments.
This tool was specifically crafted to work seamlessly with Teachable Machine, making it easier to implement and use your trained models.

Source Code is published on [GitHub](https://github.com/MeqdadDev/teachable-machine)

Read more about the project (requirements, installation, examples and more) in the [Documentation Website](https://meqdaddev.github.io/teachable-machine/) 

## Supported Classifiers

**Image Classification**: use exported keras model from Teachable Machine platform.

## Requirements

``` Python >= 3.7 ```

## How to install package

```bash
pip install teachable-machine
```

## Example

An example for teachable machine package with OpenCV:

```python
from teachable_machine import TeachableMachine
import cv2 as cv

cap = cv.VideoCapture(0)
model = TeachableMachine(model_path="keras_model.h5",
                         labels_file_path="labels.txt")

image_path = "screenshot.jpg"

while True:
    _, img = cap.read()
    cv.imwrite(image_path, img)

    result, resultImage = model.classify_and_show(image_path)

    print("class_index", result["class_index"])

    print("class_name:::", result["class_name"])

    print("class_confidence:", result["class_confidence"])

    print("predictions:", result["predictions"])

    cv.imshow("Video Stream", resultImage)

    k = cv.waitKey(1)
    if k == 27:  # Press ESC to close the camera view
        break
    
cap.release()
cv.destroyAllWindows()
```

Values of `result` are assigned based on the content of `labels.txt` file.

For more; take a look on [these examples](https://meqdaddev.github.io/teachable-machine/codeExamples/)

### Links:

- [Documentation](https://meqdaddev.github.io/teachable-machine)

- [PyPI](https://pypi.org/project/teachable-machine/)

- [Source Code](https://github.com/MeqdadDev/teachable-machine)

- [Teachable Machine Platform](https://teachablemachine.withgoogle.com/)
northstar-data 0.1.1
A blazingly fast data processing library implemented in Rust with Python bindings
Requires Python: >=3.7

# 🌟 NorthStar: Blazingly Fast Data Processing 🚀

<center>
<img src="./assets/northstar-logo.png" height=300>

NorthStar is a high-performance data processing library implemented in Rust with Python bindings. It's designed to make your data science workflows faster and cooler! 😎

</center>

## 🌈 Features

- 🦀 Rust-powered core for lightning-fast computations
- 🐍 Seamless Python integration
- 📊 Efficient DataFrame and Series operations
- 🚄 Optimized for speed and memory usage

## 🛠️ Installation

Get started with NorthStar in seconds:

```bash
pip install northstar-data
```

## 🚀 Quick Start

Here's a taste of what NorthStar can do:

```python
import northstar as ns

# Create a DataFrame
df = ns.PyDataFrame()
df.add_series(ns.PySeries("ages", [25, 30, 22, 28, 33]))
df.add_series(ns.PySeries("names", ["Alice", "Bob", "Charlie", "David", "Eve"]))
df.add_series(ns.PySeries("scores", [88.5, 92.0, 79.5, 95.5, 87.0]))

# Filter the data
young_df = df.filter("ages", lambda x: x < 25)

# Select specific columns
selected_df = df.select_columns(["names", "scores"])

# Apply a function to a series
boosted_scores = df.apply("scores", lambda x: x * 1.1)
```

## 🏎️ Performance

NorthStar is designed for speed. Here's a quick comparison:

```python
import northstar as ns
import time

data = list(range(1_000_000))

start_time = time.time()
ns_series = ns.PySeries("big_data", data)
ns_sum = ns_series.sum()
ns_time = time.time() - start_time

print(f"NorthStar time: {ns_time:.4f} seconds")

start_time = time.time()
py_sum = sum(data)
py_time = time.time() - start_time

print(f"Python time: {py_time:.4f} seconds")

speedup = py_time / ns_time
print(f"NorthStar is {speedup:.2f}x faster than pure Python!")
```

## 🛣️ Roadmap

We're just getting started! Here's what's coming:

- [ ] Advanced grouping and aggregation operations
- [ ] More statistical functions (median, mode, standard deviation)
- [ ] DataFrame join operations
- [ ] Enhanced data visualization capabilities
- [ ] Even more performance optimizations

## 🤝 Contributing

We'd love your help to make NorthStar even more awesome! Check out our [Contributing Guide](CONTRIBUTING.md) to get started.

## 📜 License

NorthStar is MIT licensed. See the [LICENSE](LICENSE) file for details.

## 🌟 Star Us!

If you find NorthStar helpful, give us a star on GitHub! It helps us know we're on the right track and encourages us to keep improving.

Happy data crunching! 🚀📊🦀