semvec/pypi-packages huggingface dataset:
chatsnip 1.1.0
A tool for extracting and managing chat data from HTML files. It includes a file splitter function to split up large files so they can be loaded back into ChatGPT if needed.
Requires Python: >=3.6

# ChatSnip

ChatSnip is a command-line tool and web app for extracting chat content from HTML files generated by ChatGPT. This tool allows users to upload HTML files, specify chat names, and extract the relevant chat content, which can then be downloaded as a text file.

## Features

- Upload HTML files containing chat data.
- Extract chat content by specifying the chat name.
- Download extracted chat content as a text file.
- User-friendly web interface powered by Streamlit and Flask.

## Installation

To install ChatSnip, follow these steps:

1. **Clone the repository:** (if applicable):

   ```bash
   git clone https://github.com/leighvdveen/chatsnip.git
   cd chatsnip
   ```

2. **Create a virtual environment:**

   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
    ```

3. **Install the required packages:**

   ```bash
   pip install -r requirements.txt
   ```

4. **Run the application:**

   ```bash
   python chatsnip.py
   ```

## Usage

1.	Open your web browser and go to http://localhost:8501 to access the Streamlit interface.
2.	Upload an HTML file containing the chat data.
3.	Enter the name of the chat you want to extract.
4.	Click the “Extract Chat” button.
5.	Download the extracted chat content as a text file.

## License

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

## Contributing

If you want to contribute to the project, feel free to fork the repository and submit a pull request. For any issues or feature requests, please open an issue.
ft4ftirs 0.1.0
A package for processing FTIR interferograms.
Requires Python: >=3.7

# FT4FTIR

FT4FTIR (Fourier Tranform for Fourier Transform Infrared Spectroscopy) is a Python package for processing interferograms from FTIR (Fourier Transform Infrared Spectroscopy) data. 
It supports reading Bruker OPUS files, processing interferograms (apodization, zero-filling, Fourier transform), 
and extracting spectra for further analysis.

## Installation

You can install the package using `pip`:

```bash
pip install ft4ftir
calango 2.2.8
It looks like calango
Requires Python: >=3.6

# Calango Project

It looks like magic

![Tests](https://github.com/cereja-project/calango/workflows/Python%20package/badge.svg)
![PyPi Publish](https://github.com/cereja-project/calango/workflows/Upload%20Python%20Package/badge.svg)
[![PyPI version](https://badge.fury.io/py/calango.svg)](https://badge.fury.io/py/calango)

## Get started

Install with pip

`pip install calango`

or

`python -m pip install calango`

## Mouse Interface

```python
from calango.devices import Mouse

mouse = Mouse()

mouse.up()  # move mouse pointer [up, down, left, right, top_left, top_right ...

mouse.position = (10, 10)  # move mouse pointer to (x, y)
print(mouse.position)  # return current postion

```
## Image API
### Image reading

```python
from calango import Image

image = Image(image_or_path='image.png')
image[:50, :50].plot() # plot image cropped (0:50, 0:50)
image.right.top.plot() # get image cropped at right top corner
image.draw_text('Hello World!', pos='left_bottom')  # draw text on image
image.height  # return image height
image.width  # return image width
image.center_position  # return image center position
image.crop_by_center((20, 20))  # crop image by center
image.prune()  # resize image to square size by min(width, height)
# ... and more
```

## Video API
### Video Writer
```python
from calango import VideoWriter
import numpy as np

frames = [np.zeros((100, 100, 3), dtype=np.uint8) for _ in range(10)]
with VideoWriter('test.mp4', fps=10) as writer:
    for frame in frames:
        writer.add_frame(frame)
# OR INLINE
VideoWriter.write_frames('test.mp4', frames) # on end is closed automatically
```
### Video from Camera
```python
from calango import Video

cam = Video(0) # 0 is the default camera
cam.show() # is running in a new thread

cam.total_frames # return the total number of frames
cam.fps # return the frames per second
cam.is_opened # return True if the camera is opened
# ... and more
```

### Video from File
```python
from calango import Video

cam = Video('./video.mp4')
cam.show()
```
### Video from frames directory
```python
from calango import Video

cam = Video('./video/', fps=24)
cam.show()
```
### Video from sequence of images
```python
from calango import Video

images = ['./video/img1.jpg', './video/img2.jpg', './video/img3.jpg'] # or list of images
cam = Video(images, fps=10)
cam.show()
```
fcmeans-softwrd 0.0.2
A Python package of Fuzzy-C-means algorithm for softwrd data and ml team.
Requires Python: >=3.9

[![Build and Release Python Package](https://github.com/softwrdai/ML-FUZZY-C-MEANS/actions/workflows/release_package.yaml/badge.svg)](https://github.com/softwrdai/ML-FUZZY-C-MEANS/actions/workflows/release_package.yaml)


# ml-fuzzy-c-means
This package will be a unified implementation of the fuzzy c-means clustering algorithm, a popular method for clustering data points into distinct groups.

## Download the package
* Please go to the release page to download the package ``*.whl`` file
* After downloading the package, please run the following command to install the package
```bash
pip install fcmeans_softwrd
```

## How to use the package

* Once done the installation mentioned in the above section [Download the package](#download-the-package), please import the package in your python code as follows
```python
import fcm

fcm.__version__

```
py-morgan-orm 0.2.0
A simple and easy-to-use ORM for managing your database queries.
Requires Python: >=3.9

# Morgan ORM for Python
A simple and easy-to-use ORM for managing your database queries.

## Motivation
I decided to create an ORM for two main reasons:
- To build an ORM that is extremely simple to use, without complex configurations—just set up your database access and you're ready to go.
- As a learning experience to understand how an ORM works internally.

I've worked with Laravel for many years, so in a way, I'm drawing inspiration from Eloquent for this ORM.

## Usage
### Using the query builder from a model

Creating a model
```python
# Import required packages
from morgan.connection import DatabaseConfig, ConnectionType
from morgan.orm import Model

# Create your model inheriting from Model
class User(Model):
    table = "users"     # Name of your table
    primary_key = "id"  # Name of your primary key

    # Configuration for the database (now it only supports SQLite)
    db_config = DatabaseConfig(connector=ConnectionType.SQLite, database_url="morgan.db")
```

Some available operations
```python
# Get all the users
users = User.all()

# Get user by primary key
user = User.get_by_pk(pk=92)

# Get users by filtering results
users = User.where("status = ?", 1).get()

# Limiting results
users = User.where("status = ?", 1).limit(5).get()

# Ordering results
users = User.where("status = ?", 1).order_by("created_at", "DESC").get()

# Creating records
user = User.create_one(username="admin", email="admin@email.com", status=1)

# Or...
users_created = User.create_many([
    {"username": "admin", "email": "admin@email.com", "status": 1},
    {"username": "staff", "email": "staff@email.com", "status": 1},
    {"username": "guest", "email": "guest@email.com", "status": 1},
])

# Updating records
User.where("status = ?", 0).update("status = ?", 1).exec()

# Deleting records
User.where("email = ?", "user@email.com").delete().exec()
```

### Working with an instance
```python
# Creating a new user
user = User(username="admin", email="admin@email.com", status=1)
user.save()

# Or...
user = User()
user.username = "admin"
user.email = "admin@email.com"
user.status = 1
user.save()

# Updating an existing record
user = User.get_by_pk(pk=8)
user.email = "support@email.com"
user.save()

# Deleting a record
user = User.get_by_pk(pk=9)
user.delete()
```

## Roadmap
- Add support for methods such as `create_one` and `create_many` in the `QueryBuilder`.
- Add support for logging queries.
- Add unit tests.
- Add support for MySQL/MariaDB and PostgreSQL.
- Add support for statements like `GROUP BY` and `HAVING`.
- Add support for relationships such as one-to-one, one-to-many, and many-to-one.

Future plans include:
- Support for migrations.
- CLI tool for creating migrations and models files.

## License
[MIT](https://github.com/vcgtz/py-morgan-orm/blob/main/LICENSE)
libp2p-accelerate-py 0.0.3
a python wrapper around nodejs orbitdb
Requires Python: >=3.8

# IPFS ACCELERATE

This is meant to be an extension of the Huggingface accelerate library, with the intent that the method load_checkpoint_and_dispatch() is going to be overloaded with a new function which will have some modular back ends, such as Libp2p, Akash, Lilypad, Huggingface Zero, Vast AI. If the model is already listed in the ipfs_model_manager there should be an associated hw_requirements key of the manifest. In the case of libp2p the request to do inference will go out to peers if there are peers in the network available to fulfill the task, otherwise a docker container will need to be launched with one of the providers here. The docker containers will will then be loaded with the ipfs_model_manager, and will use the from_auto_download() method to download the requested model and perform inference for the user.

# Method #1 Huggingface Zero

# Method #2 Akash

# Method #3 Lilypad

# Method #4 Vast AI


# IPFS Huggingface Bridge:

for huggingface transformers python library visit:
https://github.com/endomorphosis/ipfs_transformers/

for huggingface datasets python library visit:
https://github.com/endomorphosis/ipfs_datasets/

for faiss KNN index python library visit:
https://github.com/endomorphosis/ipfs_faiss

for transformers.js visit:                          
https://github.com/endomorphosis/ipfs_transformers_js

for orbitdb_kit nodejs library visit:
https://github.com/endomorphosis/orbitdb_kit/

for fireproof_kit nodejs library visit:
https://github.com/endomorphosis/fireproof_kit

for ipfs_kit nodejs library visit:
https://github.com/endomorphosis/ipfs_kit/

for python model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager/

for nodejs model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager_js/

for nodejs ipfs huggingface scraper with pinning services visit:
https://github.com/endomorphosis/ipfs_huggingface_scraper/

for ipfs agents visit:
https://github.com/endomorphosis/ipfs_agents/

for ipfs accelerate visit:
https://github.com/endomorphosis/ipfs_accelerate/

Author - Benjamin Barber
QA - Kevin De Haan