semvec/pypi-packages huggingface dataset:
Text-to-Image-Generator 0.0.1
A project that combines a FastApi backend with an ML service to generate images from text.
Requires Python: >=3.11


# Text to Image Generator

The project aims to create images from text using a pre-trained deep learning model.

This project contains two main components:
1. A FastAPI backend serving HTML, CSS, and JavaScript.
2. A machine learning service using the `stabilityai/sdxl-turbo` model to generate images from text.

## Project Structure

### Fastapi Backend
The Flask backend serves the frontend application, which includes HTML, CSS, and JavaScript files. It provides the user interface where users can input their question and document, and view the generated answer.

### Deep Learning Service
The deep learning service utilizes the stabilityai/sdxl-turbo model. This service accepts a question and a document as input, processes them using the model, and returns an answer based on the content of the document.


### Backend
The FastApi backend will be accessible at http://localhost:8000. It serves the front end application where users can input their text and receive the image..


## Features
### FastApi Backend: 
Serves the frontend and handles user interactions.
### ML Service: 
Uses a state-of-the-art language model to provide answers based on the provided documents.
### Docker Support: 
Both components can be easily deployed using Docker and Docker Compose.
CORS Handling: Ensures smooth interaction between the backend and the ML service

## Running the project


### Using Docker Compose

To build and run the project with Docker Compose, use the following command:

```bash
docker-compose up --build
```
indigopy-sriram-lab 1.1.0
The Python implementation of INDIGO
Requires Python: <4,>=3.8
Keywords: INDIGO, machine learning, combination therapy

![alt text](https://github.com/sriram-lab/INDIGOpy/blob/main/docs/INDIGOpy_logo.png?raw=true)

# INDIGOpy
Implementation of INDIGO (INferring Drug Interactions using chemoGenomics and Orthology) as a Python package.

[![license badge](https://img.shields.io/badge/License-GNU%20GPL-blue)](https://github.com/sriram-lab/INDIGOpy/blob/main/LICENSE)
[![DOI badge](https://badgen.net/badge/DOI/10.15252%2Fmsb.20156777/blue)](https://doi.org/10.15252/msb.20156777)

# Installation 
Using pip:  

```
pip install indigopy-sriram-lab
```

# User Guide
Please refer to the official [INDIGOpy documentation](https://sriram-lab-indigopy.readthedocs.io/en/latest/) for usage examples.  

# Repository structure: 
```
INDIGOpy
└───docs      [documentation and example usage]
└───indigopy  [source code]
└───tests     [testing modules]
|   LICENSE
|   MANIFEST.in
|   README.md
|   pyproject.toml
|   setup.py
```

# License
Released via GPL GNU License  
&copy; 2022 The Regents of the University of Michigan  
Chandrasekaran Research Group - https://systemsbiologylab.org/  
Contact: csriram@umich.edu
hellocash 0.2.0
Unofficial Python SDK for HelloCash API

# hellocash-python
Unofficial Python SDK for HelloCash API

## Documentation
The documentation for the HelloCash API can be found [here](https://hellocash.docs.apiary.io/#).\
The documentation for the PyPi package can be found [here](https://pypi.org/project/hellocash/).

## Installation

Install the package using pip:

```bash
pip install hellocash
```

## Usage

```python
from hellocash import Client
```
Text-to-Image-GeneratorAnnaMaria 0.0.1
A project that combines a FastApi backend with an ML service to generate images from text.
Requires Python: >=3.11


# Text to Image Generator

The project aims to create images from text using a pre-trained deep learning model.

This project contains two main components:
1. A FastAPI backend serving HTML, CSS, and JavaScript.
2. A machine learning service using the `stabilityai/sdxl-turbo` model to generate images from text.

## Project Structure

### Fastapi Backend
The Flask backend serves the frontend application, which includes HTML, CSS, and JavaScript files. It provides the user interface where users can input their question and document, and view the generated answer.

### Deep Learning Service
The deep learning service utilizes the stabilityai/sdxl-turbo model. This service accepts a question and a document as input, processes them using the model, and returns an answer based on the content of the document.


### Backend
The FastApi backend will be accessible at http://localhost:8000. It serves the front end application where users can input their text and receive the image..


## Features
### FastApi Backend: 
Serves the frontend and handles user interactions.
### ML Service: 
Uses a state-of-the-art language model to provide answers based on the provided documents.
### Docker Support: 
Both components can be easily deployed using Docker and Docker Compose.
CORS Handling: Ensures smooth interaction between the backend and the ML service

## Running the project


### Using Docker Compose

To build and run the project with Docker Compose, use the following command:

```bash
docker-compose up --build
```
sorpus 0.0.1.post2
Sentence (Operations) Processing Utilities
Requires Python: >=3.8

# Sorpus
![CI/CD: Workflow](https://img.shields.io/badge/CI/CD-Workflow-green)
[![PyPI](https://img.shields.io/badge/PyPI-v.0.0.1_alpha-blue)](https://pypi.org/project/sorpus/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)


Sentence (OpeRations) Processing UtilitieS.

Sorpus is a sentence processing library for computational linguistics and natural language processing. It is a demo version currently under development, where I am adding features that I personally felt were necessary but were not available in other libraries. 
## Install
```
! pip install sorpus
```
## Document
https://sites.google.com/view/sorpus/