semvec/pypi-packages huggingface dataset:
st-discord-nav 2.1.0
Streamlit component that allows you to do navigation in your Streamlit app inspired by Discord.
Requires Python: >=3.7

# streamlit-custom-component

Streamlit component that allows you to do X

## Installation instructions

```sh
pip install st_discord_nav
```

## Usage instructions

```python
import streamlit as st

from st_discord_nav import st_discord_nav

page = st_discord_nav()

```
image-processing-project.py 0.0.2
Image processing project in Python
Requires Python: >=3.8

# image_processing_project.py

Description. 
The package image_processing_project.py is used to:
	Processing:
		- Histogram matching
		- Structural similarity
		- Resize image
	Utils:
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install image_processing_project.py

```bash
pip install image_processing
```

## Usage

```python
from package_name.module1_name import file1_name
file1_name.my_function()
```

## Author
Yuri M.

## License
[MIT](https://choosealicense.com/licenses/mit/)
incribo 0.1.2
Generate stateful embeddings for your AI
Requires Python: >=3.9

incribo

Incribo lets you add state to your embeddings. Using it, you can track how embeddings change over time, compare different embeddings to see their similarities and stream new data to update embeddings on the fly.

- Real-time Embedding Updates: Efficient handling of evolving, dynamic data.
- Compare embedding qualities: Compare different embeddings and evaluate their quality using customizable metrics.
- Versioning & Control: Create multiple versions of embeddings and track them over time with rollback abilities.
- Streaming data: Stream data from multiple sources.
- Cross-Platform Compatibility: Hassle-free hosted solution.

🔧 Quick install
#Download the Python package
pip install incribo

✨ Basic Usage
Before we get started, it is advised to setup a virtual environment to work in. Next, add your embedding model and generate stateful embeddings with Incribo like so -
from incribo import Embedding

# Create a new embedding and add an associated model for better identification
emb = Embedding([1.0, 2.0, 3.0], "bert-base-uncased")

# Get the vector
vector = emb.get_vector()
print(f"Embedding vector: {vector}")

# Get the model name
model = emb.get_model()
print(f"Model: {model}")

# print the Embedding object 
print(emb)


📖 Documentation
For detailed usage instructions and example use cases, visit our documentation at docs.incribo.com.


🌐 Contributing
Drop us an email to report a bug, or request a feature here: info@incribo.com

🤝 Connect with the founders 1:1
Connect with us here: https://cal.com/uma08/30min

💪 Community
Join our growing community around the world, for help, ideas, and everything on AI and beyond:

Chat with us on Discord.
Follow us on Twitter.
Connect with us on LinkedIn.


License
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.