semvec/pypi-packages huggingface dataset:
newer-llama 0.0.1
Get information from custom text
Requires Python: >=3.10

## LLAMA

This is an openai model that able you to give your own text to it and ask question of that text from model.

To use this model first you need to buy a token from openai.

link of model documentation:

https://docs.llamaindex.ai/


## How to use Model:

```
lama = llama_model("Your data text file directory, "Your open ai token)
result = lama.getquery("Your query)
print(result)
```
quantificationlib 0.1.2
quantificationlib: A library for Quantification Learning
Requires Python: >=3.6
Keywords: quantification,label shift,prevalence estimation

![cibadge](https://github.com/aicgijon/quantificationlib/actions/workflows/quantificationlib.yml/badge.svg)
[![docs](https://img.shields.io/badge/docs-sphinx-blue)](https://aicgijon.github.io/quantificationlib/)
[![codecov](https://codecov.io/github/AICGijon/quantificationlib/branch/main/graph/badge.svg?token=6Y3SMCVWCD)](https://app.codecov.io/github/AICGijon/quantificationlib)
[![Downloads PyPI](https://static.pepy.tech/personalized-badge/quantificationlib?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=pypi)](https://pypi.org/project/quantificationlib/)


# QuantificationLib

QuantificationLib is an open-source library for quantification learning. 

### Installation and documentation

The installation, quick-start guide and documentation are available [here](https://aicgijon.github.io/quantificationlib/).
new-llama 0.0.1
Get information from custom text

## LLAMA

This is an openai model that able you to give your own text to it and ask question of that text from model.

To use this model first you need to buy a token from openai.

link of model documentation:

https://docs.llamaindex.ai/


## How to use Model:

```
lama = llama_model("Your data text file directory, "Your open ai token)
result = lama.getquery("Your query)
print(result)
```
daily-summary 0.1.29
A package to generate daily development reports
Requires Python: >=3

# daily-summary

Generate a summary of your commit history for the day.

## Usage:
export OPENAI_API_KEY=your-api-key

daily-summary --repo relative/path/to/your/repo --author "Author Name" --model <model-name>

--model is an optional argument that specifies the OpenAI model to be used. Defaults to "gpt-4" if not provided.
--date is optional but lets you create reports for different dates than today
  
  "Author Name" should match your git commit author name

## Publishing:

Build the package:

python3 publish.py 

OR

python3 setup.py sdist bdist_wheel
Update the version number in setup.py
pip install twine
twine upload dist/* 
username = \___token__
password = PYPI_TOKEN
inspira 0.17.0
Inspira is a lightweight framework for building asynchronous web applications.
Requires Python: >=3.8

# Inspira

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

Inspira is a lightweight framework for building asynchronous web applications.

## Quick Start

### Prerequisites

Make sure you have Python and `pip` installed on your system.

### Create a Python Virtual Environment

```bash
# Create a new directory for your project
$ mkdir myproject
$ cd myproject
```

**Create and activate a virtual environment**

```bash
$ python -m venv venv
$ source venv/bin/activate
```

**Install Inspira**

```bash
$ pip install inspira
```

## Generating an App

To generate a new app for your project, run the following command:

```bash
$ inspira init
```

## Generated Directory Structure

After running the `init` command, the directory structure of your project should look like the following:

```bash
├── main.py
├── src
│   ├── __init__.py
│   ├── controller
│   │   └── __init__.py
│   ├── model
│   │   └── __init__.py
│   ├── repository
│   │   └── __init__.py
│   └── service
│       └── __init__.py
└── tests
    └── __init__.py
```

## Generate Database file

Use the following command to generate a database file:

```bash
$ inspira new database --name mydb --type sqlite
```

This command will create a new database file named `mydb` with `SQLite` as the database type.

The generated database file (`database.py`) will typically contain initial configurations and may look like this:

```python
from sqlalchemy import create_engine
from sqlalchemy.orm import declarative_base, scoped_session, sessionmaker

engine = create_engine("sqlite:///mydb.db")
db_session = scoped_session(
    sessionmaker(autocommit=False, autoflush=False, bind=engine)
)
Base = declarative_base()
Base.query = db_session.query_property()
```

## Generating Controller

To generate necessary controller for your project, run the following command:

```bash
$ inspira new controller order
```

## Generating Repository

To generate repository file, run the following command:

```bash
$ inspira new repository order
```

## Generating Service

To generate service file, run the following command:

```bash
$ inspira new service order
```

## Generating Model

To generate model file, run the following command:

```bash
$ inspira new model order
```

## Starting the Server

After generating your app and setting up the necessary resources, start the server with the following command:

```bash
$ uvicorn main:app --reload
```

## Links
Documentation: https://www.inspiraframework.com/


## License

This project is licensed under the terms of the MIT license.
small-llama 0.0.1
Get information from custom text

## LLAMA

This is an openai model that able you to give your own text to it and ask question of that text from model.

To use this model first you need to buy a token from openai.

link of model documentation:

https://docs.llamaindex.ai/


## How to use Model:

```
lama = llama_model("Your data text file directory, "Your open ai token)
result = lama.getquery("Your query)
print(result)
```