semvec/pypi-packages huggingface dataset:
alleai 0.1.0
A Python SDK for interacting with the AlleAI platform
Requires Python: >=3.8

# AlleAI Python SDK

A  Python SDK for interacting with the AlleAI platform, providing easy access to state-of-the-art AI models for image generation, audio processing, and more.

## Features

- 🖼️ **Image Processing**
  - Image generation from text prompts
  - Image editing and manipulation
  - Support for multiple AI models

- 🎵 **Audio Processing**
  - Text-to-Speech (TTS) conversion
  - Speech-to-Text (STT) transcription
  - AI-powered audio generation

## Installation

Install the package using pip:

```bash
pip install alleai
```

## Quick Start

```python
from alleai.core import AlleAIClient

# Initialize the client
client = AlleAIClient(api_key="your-api-key")

# Generate an image
image_response = client.image.generate({
    "models": ["nova-canvas"],
    "prompt": "A futuristic cityscape at sunset with neon lights and flying cars",
    "model_specific_params": {
        "nova-canvas": {
            "n": 1,
            "height": 1024,
            "width": 1024
        }
    }
})

# Convert text to speech
tts_response = client.audio.tts({
    "models": ["gpt-4o-mini-tts"],
    "prompt": "Welcome to the future of AI",
    "voice": "nova"
})

# Transcribe audio
stt_response = client.audio.stt({
    "models": ["whisper-v3"],
    "audio_file": "path/to/your/audio.mp3"
})
```

## Documentation

### Image Generation

```python
# Generate an image
response = client.image.generate({
    "models": ["nova-canvas"],
    "prompt": "Your creative prompt here",
    "model_specific_params": {
        "nova-canvas": {
            "n": 1,
            "height": 1024,
            "width": 1024
        }
    },
    "seed": 42  # Optional: for reproducible results
})

# Edit an existing image
response = client.image.edit({
    "models": ["nova-canvas"],
    "image_file": "path/to/image.jpg",
    "prompt": "Replace the sky with a sunset"
})
```

### Audio Processing

```python
# Text to Speech
response = client.audio.tts({
    "models": ["gpt-4o-mini-tts"],
    "prompt": "Text to convert to speech",
    "voice": "nova"
})

# Speech to Text
response = client.audio.stt({
    "models": ["whisper-v3"],
    "audio_file": "path/to/audio.mp3"
})

# Generate Audio
response = client.audio.generate({
    "models": ["lyria"],
    "prompt": "Create a relaxing ambient track"
})
```

## Error Handling

The SDK includes comprehensive error handling:

```python
from alleai.core.exceptions import InvalidRequestError

try:
    response = client.image.generate({
        "models": ["nova-canvas"],
        "prompt": "Your prompt here"
    })
except InvalidRequestError as e:
    print(f"Error: {e.message}")
    print(f"Error Code: {e.code}")
```

## Requirements

- Python 3.8 or higher
- `requests` library (automatically installed with the package)

## License

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

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
arnie 0.2.7
A Python utility library to estimate, compare, and reweight RNA energetics across many secondary structure algorithms.
Requires Python: >=3.7
Keywords: RNA, RNA structure prediction, Bioinformatics

# arnie
Python API to compute RNA energetics and do structure prediction across multiple secondary structure packages.

## Documentation
[See our full docs.](https://daslab.github.io/arnie)

## Install
`arnie` is [available on PyPI](https://pypi.org/project/arnie/).

`pip install arnie`

## Repo Organization

`src/arnie`: source code for the arnie package.

`docs`: docsify-based markdown documentation for the arnie package.

`tests`: unit tests 

`notebooks`: example jupyter notebooks with usage.

`scripts`: scripts for processing sequences in batch.

`parameter_files`: dir of various parameter files for packages, put here out of convenience.


(c) 2024 Leland Stanford Jr University
Authors:
Hannah Wayment-Steele
xdTelegramMsgAPI 0.1.0
A Python library for sending messages via the xd-org.site Telegram API
Requires Python: >=3.6

# xdTelegramMsgAPI
A Python library for sending messages or images to Telegram users via the xd-org.site API.

## Installation
```bash
pip install xdTelegramMsgAPI
```

## Usage
```python
from xdTelegramMsgAPI import xdSendMsg

# Send a simple message
response = xdSendMsg(
    to="123456",
    key="xd_team",
    msg="Hello, User!"
)
print(response)

# Send a message with an image and inline buttons
response = xdSendMsg(
    to="123456",
    key="xd_team",
    msg="Check this out!",
    img="https://example.com/image.jpg",
    inlinetext1="Open Website",
    inlinelink1="https://example.com",
    inlinetext2="More Info",
    inlinelink2="https://example2.com"
)
print(response)
```

## Requirements
- Python 3.6+
- `requests` library

## License
MIT
PyGitContributionChart 0.0.3
A package for generating Git contribution charts
Requires Python: >=3.6

# GitCommitChart

This is a small python library that uses PIL to generate Github commit charts dynamically. Its very cool.

## Examples

Create a simple chart
```py
from GitCommitChart import create_git_commit_chart
import random

if __name__ == "__main__":
    # Example data array with 365 elements
    # each with a random value between 0 and 100
    data = [random.randint(0, 10) for i in range(365)] 


    # Create the Git commit chart
    image = create_git_commit_chart(data, rows_per_column=7)
    image.save("test.png")
```
![alt text](docs/image-1.png)

Create a chart with labels
```py
from GitCommitChart import create_git_commit_chart
import random

if __name__ == "__main__":
    data = [random.randint(0, 10)
            for i in range(365)]  # Example data for a year

    # Create the Git commit chart
    image = create_git_commit_chart(
        data,
        rows_per_column=7,
        horizontal_labels=["Jan", "Feb", "Mar"...
        vertical_labels=["Sun", "Wed", "Sat"],
        label_font_size=40
    )
    image.save("test.png")

```
![alt text](docs/image-2.png)
nbv 0.0.5
Read-only viewing of Jupyter notebooks in the web browser.
Requires Python: >=3.7

# nbv

Read-only Jupyter Notebook viewing using `nbconvert`.

Invoking
```
nbv notebook.ipynb
```
converts the notebook to HTML[^1] and opens this HTML file in the default web browser.

You can get a similar result with just
```
nbconvert notebook.ipynb --to html --post serve
```
Note that this (and `nbv`) doesn't require a full Jupyter installation (e.g. JupyterLab) in order to run.
But `nbv` has additional benefits[^2]:
* doesn't pollute the notebook location with HTML (unlike the above)
* no server, just open the HTML file in browser
* simple CLI, exposing only a few `nbconvert` options

**Note:** `nbv` will use the [`nbconvert` library](https://nbconvert.readthedocs.io/en/stable/nbconvert_library.html) if installed, otherwise it will fall back to the `jupyter-nbconvert` [command](https://nbconvert.readthedocs.io/en/stable/usage.html).

### [uv](https://docs.astral.sh/uv/guides/tools/) examples

Use `jupyter-nbconvert` command:
```
uvx nbv notebook.ipynb
```

Use `nbconvert` library:
```
uvx --with nbconvert nbv notebook.ipynb
```
```
uvx --from 'nbv[complete]' nbv notebook.ipynb
```
```
uv tool install --with nbconvert nbv
```

---

[^1]: Inside the OS temporary directory by default.
[^2]: Also I wanted to try [Typer](https://typer.tiangolo.com).