semvec/pypi-packages huggingface dataset:
odbg 0.1.0
Open Data Badger
Requires Python: <4.0,>=3.12

# Open Data Badger (ODBG)

## Overview

Open Data Badger (ODBG) is a powerful data batching utility designed for OpenAI endpoints, with plans for future support for local language models (LLMs). ODB provides robust tools for managing, submitting, and processing large batches of data efficiently.

## Features

- **BatchProcessor**:
  - Initializes a SQLite database to store job details.
  - Reads system prompts from a string or file.
  - Creates batch files from input data.
  - Submits batch jobs to OpenAI API.
  - Estimates and processes batches based on maximum batch size and request limits.
  - Supports CSV and Parquet input files.
  - Handles random sampling and dry runs.
  - Verbose mode for detailed batch information.

- **ResultDownloader**:
  - Retrieves the status of batch jobs.
  - Downloads and saves results from completed batch jobs.
  - Supports output in CSV and Parquet formats.

## Requirements

- Python 3.6+
- Poetry for dependency management

## Installation

1. Clone the repository:

   ```sh
   git clone https://github.com/ashim-mahara/odbg.git
   cd odbg
   ```

2. Install the required dependencies using Poetry:

   ```sh
   poetry install
   ```

## Usage

### BatchProcessor

Use the `BatchProcessor` to create and submit batch jobs to the OpenAI API.

#### Example

```sh
poetry run python batch_processor.py \
    --base_url https://your-openai-proxy.com \
    --system_prompt "Your system prompt here" \
    --input_file data/input.csv \
    --data_path data/output \
    --text_field text \
    --task_name example_task \
    --model text-davinci-003 \
    --id_field id \
    --description "Example batch processing task" \
    --random_samples 100 \
    --dry_run \
    --verbose
```

### ResultDownloader

Use the `ResultDownloader` to retrieve and save results from completed batch jobs.

#### Example

```sh
poetry run python result_downloader.py \
    --task_name example_task \
    --task_run_id 1 \
    --output_format csv
```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.
djako-plugin-ya-metrics 0.0.5
Плагин для Djako, который предоставляет интеграцию с API Яндекс метрики и визуализирует счётчики через Chart.js
Requires Python: >=3.10
Keywords: Djako, Django, CMS, Web, Charts, Yandex Metrics, Admin Widgets

# Djako Plugin Yandex Metrics


Плагин для Djako, который предоставляет 
интеграцию с API Яндекс метрики и визуализирует счётчики через Chart.js

## Быстрый старт
-----------

0. Укажите в переменных окружения (enviroments) переменную `YANDEX_ACCESS_TOKEN`, который вы должны получить, следуя инструкции API Яндекс метрики. 

1. Добавьте "djako_plugin_ya_metrics" в `INSTALLED_APPS`:

```
    INSTALLED_APPS = [
        ...,
        "djako_plugin_ya_metrics",
    ]
```

2. Добавьте маршруты плагина в urls.py проекта:

```
    path("", include("djako_plugin_ya_metrics.urls")),
```

3. Запустите ``python manage.py migrate`` для создания моделей.

4. Перейдите в панель админа, затем добавьте сначала счётчик, стиль диаграмм, а затем сами диаграммы.
sqzhash 1.0.4
SqzHash is a custom cryptographic hash algorithm implementation designed to compute hash values for strings and files.
Requires Python: >=3.6

# SqzHash

SqzHash is a simple yet reliable 256-bit cryptographic hash algorithm designed for efficient computation of hash values. It employs shifts, XOR operations, and prime numbers to ensure robust hashing capabilities. SqzHash processes data in chunks, continuously updating its state to generate unique hash sums for strings and files, making it suitable for a wide range of applications.

## Installation

You can install SqzHash using pip:

```sh
pip install sqzhash
```

# Example Usage
## Hashing a String
You can use the hash_string function to compute the hash of a string.
```python
from sqzhash import hash_string

input_string = "example string"
hash_value = hash_string(input_string)
print(f"Hash value for the string is: {hash_value}")
```

## Hashing a File
You can use the hash_file function to compute the hash of a file.
```python
from sqzhash import hash_file

file_path = "path/to/your/file.txt"
hash_value = hash_file(file_path)
print(f"Hash value for the file is: {hash_value}")
```
raspinet 0.1.0
A library for simplifying networking between Raspberry Pis
Requires Python: <4.0,>=3.6

# Raspinet

Raspinet is a Python library for simplifying networking between Raspberry Pis.

## Features

- Discover devices on the local network.
- Connect to and communicate with devices.
- Send and receive messages and files.

## Installation

You can install Raspinet via pip:

```sh
pip install raspinet