semvec/pypi-packages huggingface dataset:
vosk-tts 0.3.61
Offline text to speech synthesis
Requires Python: >=3.7

# Vosk TTS

Simple TTS based on VITS with some old ideas

## Usage

### Command line

```
pip3 install vosk-tts

vosk-tts -n vosk-model-tts-ru-0.9-multi -s 2 --input "Привет мир!" --output out.wav
```

### API

```
from vosk_tts import Model, Synth
model = Model(model_name="vosk-model-tts-ru-0.9-multi")
synth = Synth(model)

synth.synth("Привет мир!", "out.wav", speaker_id=2)
```

## Voices

For now we support several Russian voices 3 females and 2 males. Get the model here:

[vosk-model-tts-ru-0.9-multi](https://alphacephei.com/vosk/models/vosk-model-tts-ru-0.9-multi.zip)

You can use speaker IDs from 0 to 4 included.

We plan to add more voices and languages in the future.
FHIL-spatial-utils 0.1.1
Utility functions for spatial RNA analysis
Requires Python: >=3.9

# FHIL Spatial Utils

A collection of utility functions for spatial RNA analysis, including SVM-based classification and visualization tools for single-cell and spatial transcriptomics data.

## Available Functions

### SVM Analysis
- **`svm_feature_weighting_heatmap`**: Generate heatmaps showing the top informative features per class for SVM models.
- **`svm_classes_from_proba`**: Extract predicted classes from SVM probability predictions with confidence thresholds and margin criteria.
- **`annotate_clusters_by_consensus`**: Annotate clusters based on consensus of individual cell predictions using proportion thresholds.

### Visualization
- **`plot_confusion_matrix`**: Create confusion matrix visualizations for comparing two sets of categorical labels with optional normalization.
- **`grid_plot_adata`**: Create grid layouts of plots from multiple AnnData objects for easy comparison across datasets and annotations.

## Dependencies

- pandas
- numpy
- scanpy
- seaborn
- matplotlib
- pybiomart (for mapping ensembl IDs to gene symbols)
- anndata
- sklearn (for SVM functionality)

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
Classy-AI 0.0.15
A python module that utilizes AI to classify natural language into categories for processing with other models.

# Classy
## How to use
Install: 
<pre><code>pip install Classy-AI</code></pre>
Setup: **Only do this if you plan to run the model locally**
<pre><code>import Classy
Classy.download()</code></pre>
Downloads "data.pth" to your working directory.


Usage (Complete version for a server):
<pre><code>import Classy
Classy.init('/path/to/data.pth','OpenAI api key')
primary, secondary, stemmed, organized = Classy.server("hello, how are you?")</code></pre>
Usage (Just the classifier)
<pre><code>import Classy
intent, certainty = Classy.classify("hello, how are you?",'/path/to/data.pth')</code></pre>

## API

Usage
<pre><code>import Classy
intent, certainty = Classy.classify_api("hello, how are you?")</code></pre>


Go to http://app.chat314.com/api to learn about how to use the api yourself.


## Other tools
GPT:<pre><code>Classy.chat_gpt(input,model,api_key)</code></pre>
DALL-E:<pre><code>Classy.dall_e(input,model,size,api_key)</code></pre>
Search:<pre><code>Classy.search(input,model,api_key)</code></pre>
Personal:<pre><code>Classy.personal(input)</code></pre>

## Example:
<pre><code>import Classy
api_key='sk-proj-api-key'
while True:
    var=input(': ')
    output,prob= Classy.classify_api(var)
    final=''
    secondary=''
    if output == 'GPT' and prob >= 0.7:
        final=Classy.chat_gpt(var,"gpt-4o-mini",api_key)
    elif output == 'Dall-e' and prob >= 0.7:
        final=Classy.dall_e(var,"dall-e-3","1024x1024",api_key)
    elif output == 'Search' and prob >= 0.7:
        final=Classy.search(var,"gpt-4o-mini",api_key)
    elif output == 'Personal' and prob >= 0.7:
        final,secondary,x,y=Classy.personal(var)
    print(final)
    if secondary:
        print(secondary)
</code></pre>

## I need more training data

Open a pull request if you have some and put it in unorganized data. I will format it. If you have organized data, put it directly in the intents.json file.

An easy way to contribute is by scrolling through your ChatGPT conversations/Google searches/DALL-E inputs and pasting some of your own data into the unformatted data file.
# Deployed here:
https://chat314.com

Uses models gpt-4o-mini and dall-e-3.

I can add a custom password for you on the website if you add training data. Message me at 123scoring@gmail.com. Put in the description of the pull request that you want a custom password for your Openai key. 


## Thanks
Thanks to [Patrick Loeber](https://github.com/patrickloeber) for teaching me PyTorch
siblink 1.2.2
Sibling Link for python packages.

# siblink - a Python sibling package linker

![Version Badge](https://badgen.net/badge/version/1.2.2/blue?icon=github)

### Installation

```bash
pip install siblink
```

**OR**

```bash
git clone git@github.com:TreltaSev/siblink.git
cd ./siblink
pip install .
```

### Usage

`**siblink**` has some commands that you should familiarize yourself with, since siblink is built over with the [click](https://github.com/pallets/click) package, after installation, you can run

```bash
siblink --help
```

#### Usage

A package in pythons eyes is just a folder containing a `__init__.py` file, it makes variables easily discoverable between "packages". The way python knows what files it should make discoverable to you is through its built-in sys.path variable. `**siblink**` just adds a few more directories, the one behind your current directory, as well as every directory between the `.py` file and your working directory, which in turn, adds your current directories' siblings together.

Say you had two directories within the same directory.

```bash
parent
├───package_a
│       __init__.py
│
└───package_b
        main.py
```

the `__init__.py` file contains `some_variable`, you want to get that variable from main.py, if you attempt to run main.py normally, this would be your result.

```bash
python ./parent/package_b/main.py

Traceback (most recent call last):
  File ".\parent\package_b\main.py", line 1, in <module>
    import package_a
ModuleNotFoundError: No module named 'package_a'
```

You could always mutate the sys.path variable yourself but I've found that its annoying to do that if you have hundreds of files.

`**siblink**` automatically does this for you, now if you were to run main.py with `**siblink**`, this is your result.

```bash
siblink run ./parent/package_b/main.py

10
```
solar-elements 0.3.0
Python library for interacting with the Solar System
Requires Python: <4.0,>=3.11

# Elements

Elements is a Python library for interacting with Solar, a framework for building
cross-compatible social media platforms. Solar is built around the nostr protocol
as a standard for storing and sharing verifiable data.

## Development Information

Elements is in active development. Current version: v0.2.2

This package is maintained using [poetry](https://python-poetry.org/docs/repositories/#configuring-credentials)

This library has too many external dependencies. It should be able to work with the basic
urllib instead of using "requests", but fixing this is not my priority at the moment.
runeq 0.17.7
Query data from Rune Labs APIs
Keywords: rune labs api query data

# runeq-python

![PyPI - License](https://img.shields.io/pypi/l/runeq)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/runeq)
![PyPI](https://img.shields.io/pypi/v/runeq)
[![Documentation Status](https://readthedocs.org/projects/runeq/badge/?version=latest)](https://runeq.readthedocs.io/en/latest/?badge=latest)
[![CircleCI Status](https://circleci.com/gh/rune-labs/runeq-python.svg?style=shield)](https://app.circleci.com/pipelines/gh/rune-labs/runeq-python)

Python 3 Standard Development Kit (SDK) for Rune Lab's Query API: `runeq`

## References

* Library documentation: [https://runeq.readthedocs.io/en/latest](https://runeq.readthedocs.io/en/latest)
* API documentation: [https://docs.runelabs.io](https://docs.runelabs.io)
* Rune Labs home page: [https://runelabs.io](https://runelabs.io)

## Installation

Python 3.8+ is required.

To install the library using pip:

    pip3 install runeq

To install from source:

    python3 setup.py install

## Development

Initialize a virtual environment, with dev requirements installed:

    make init

### Run tests
    
    make test
    # With coverage
    make test-coverage
    # For a single test
    make test-single


### Lint

    make lint

### Preview documentation
    
    make build-docs

- This will build the documents in the `docs` directory. Open the `index.html` file in your browser to preview the documentation.

### Build PyPI artifact:

    make build-dist

### Clean up ignored files/artifacts

    make clean
astrolore 1.0.0
package for finding nearest source in sci-fi
Requires Python: >=3.9

# Astrolore

This package, given the name of any astrophysical object, finds the nearest source that has been referenced in science fiction. There are options to 1) observe
the nearest sci-fi source in the [Aladin Sky Atlas](https://aladin.cds.unistra.fr/AladinLite/) and 2) generate a sky plot showing the separation between the input object and nearest sci-fi source.


### Installation

This package can be pip installed.

```
pip install astrolore
```

#### Dependencies
* pandas
* astropy
* numpy
* matplotlib
* pywebview
* tkinter

### Running the code

```python
import astrolore

obj_name = 'Pleiades'



astrolore.closest_star_finder(obj_name)
```
<div align="center">
  <img width="528" height="416" alt="image" src="https://github.com/user-attachments/assets/3b48d0e0-c7f1-4351-abbb-ee5d91de5f38" />
</div>

### Authors:

* Arnab Lahiry (alahiry@ics.forth.gr)
* Isabele Souza Vitorio (ivitorio@umich.edu)
* Joe Adamo (jadamo@arizona.edu)
* Kayvon Sharifi (ksharifi1@gsu.edu)