semvec/pypi-packages huggingface dataset:
easyocr-unstructured 1.3.4
Parse unstructured text from PDFs
Requires Python: >=3.8


# EasyOCR Unstructured

EasyOCR Unstructured is a powerful library for Optical Character Recognition (OCR) that can extract text from PDFS, then group the text based on proximity.

It is intended for PDF files that have text that doesn't follow the left to right top to bottom standard of document writing.


## Getting Started

pip install easyocr-unstructured

```
import easyocr_unstructured

# Initialize the EasyOCR Unstructured object
easyocr = EasyocrUnstructured()

# Invoke the OCR process on your PDF file
result = easyocr.invoke('/path/to/your_pdf_file.pdf')

#result will be a list of lists containing strings
from pprint import pprint as pp
pp(result)
```

## Example Output

The output will look something like this:

```python
[
    ["This is the piece of text. Nothing near it"],
    ["This is the second piece of text.", "This is the third piece of text that was close to the second"],
    ["This is the fourth piece of text. Nothing near it"],
    ...
]
```

### Prerequisites

- Python 3.12 +

### Installing

pip install easyocr-unstructured

## Usage

```
import easyocr_unstructured

easyocr = EasyocrUnstructured()
result = easyocr.invoke('/path/to/your_pdf_file.pdf')
```

Keyword arguments for more control:

```
import easyocr_unstructured

easyocr = EasyocrUnstructured(init_reader=False, gpu=True)
result = easyocr.invoke('/path/to/your_pdf_file.pdf', proximity_in_pixels=20, gpu=True, dpi=120, batch_size=3, **kwargs):)
```

- init_reader (bool): Load the EasyOCR reader on class initialization. 
    If set to False will load the reader everytime invoke is called
- proximity_in_pixels (int, optional): The proximity threshold 
    for grouping text entries. Defaults to 20.
- gpu (bool): Toggle to compute on GPU, if True and there is
    no gpu, will use cpu
- dpi (int): DPI setting for parsing PDF, higher value
    will be more accurate but slower and use more memory
- batch_size (int): Will determine the batch size for both
    parsing pdfs and scanning them

## Running the tests

No tests yet

## Built With

- Wing Pro
- Python 3.12
- numpy
- easyocr
- pdf2image
- hashlib

## Contributing

Please do, any sensible and safe change will be added!

## Authors

Kevin Fink

## License

MIT

## Acknowledgments
oven-compiler 0.1.7
Python-to-PTX GPU Kernel Compiler with MLIR optimization
Requires Python: >=3.12
Keywords: mlir, gpu, cuda, compiler, optimization, kernel, ptx, python, llvm, nvptx

# Oven Compiler

Python-to-PTX GPU compiler using MLIR.

## Installation

```bash
pip install oven-compiler
```

## Quick Start

### Command Line

```bash
# Compile Python to PTX
oven-compiler --python kernel.py -o output.ptx
```

### Python API

```python
import oven_compiler

python_code = "def add(a, b): return a + b"
ptx_code = oven_compiler.compile_python_string_to_ptx(python_code)
```

## Requirements

- Python ≥ 3.12
- CUDA-capable GPU (for execution)

## License

MIT License
time-mcp-pypi 1.1.1
A Model Context Protocol (MCP) server that provides current UTC time in RFC 3339 format
Requires Python: >=3.8
Keywords: mcp, model-context-protocol, time, utc, timestamp

# Time MCP Server

A minimal Model Context Protocol (MCP) server built with Python.

The server provides one simple tool:
- `get_current_utc_time` - Gets the current UTC date and time in RFC 3339 format

## Quick start

Prerequisites:
- Python 3.8 or later
- Claude Desktop or another MCP-compatible client

Install and run with:

```bash
pip install time-mcp-pypi  # or from a local clone: pip install -e .
time-mcp-pypi
```

## Configuration

To use this server with Claude Desktop, add the following to your MCP configuration file:

### Windows
Add to `%APPDATA%\Claude\claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "time-server": {
      "command": "time-mcp-pypi"
    }
  }
}
```

### macOS
Add to `~/Library/Application Support/Claude/claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "time-server": {
      "command": "time-mcp-pypi"
    }
  }
}
```

## Usage

Once configured, you can use the time tool in Claude Desktop:
- "What's the current UTC time?"
- "Give me an RFC 3339 timestamp"

## Publishing New Versions

To publish a new version of the package:

1. **Update the version in `pyproject.toml`**, and commit as `Release x.y.z`

2. **Tag the release** with a version number prefixed with 'v':
   ```bash
   git tag v1.0.1
   ```

3. **Push the tag** to trigger the publishing pipeline:
   ```bash
   git push --tags
   ```

Then the CI/CD pipeline will automatically publish the package.

### CI/CD
The project includes GitHub Actions workflows for:
- **Build & Test** - Builds and tests the Python package
- **Publish** - Automatically publishes to PyPI and creates GitHub releases on version tags

<!-- mcp-name: io.github.domdomegg/time-mcp-pypi -->
llama-index-llms-oci-genai 0.6.4
llama-index llms OCI GenAI integration
Requires Python: <4.0,>=3.9

# LlamaIndex Llms Integration: Oracle Cloud Infrastructure (OCI) Generative AI

> Oracle Cloud Infrastructure (OCI) [Generative AI](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm) is a fully managed service that provides a set of state-of-the-art,
> customizable large language models (LLMs) that cover a wide range of use cases, and which are available through a single API.
> Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned
> custom models based on your own data on dedicated AI clusters.

## Installation

```bash
pip install llama-index-llms-oci-genai
```

You will also need to install the OCI sdk

```bash
pip install -U oci
```

## Usage

```bash
from llama_index.llms.oci_genai import OCIGenAI

llm = OCIGenAI(
    model="MY_MODEL",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="MY_OCID",
)
```
llama-index-embeddings-oci-genai 0.4.2
llama-index embeddings oci-genai integration
Requires Python: <4.0,>=3.9

# LlamaIndex Embeddings Integration: Oracle Cloud Infrastructure (OCI) Generative AI

> Oracle Cloud Infrastructure (OCI) [Generative AI](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm) is a fully managed service that provides a set of state-of-the-art,
> customizable large language models (LLMs) that cover a wide range of use cases, and which are available through a single API.
> Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned
> custom models based on your own data on dedicated AI clusters.

## Installation

```bash
pip install llama-index-embeddings-oci-genai
```

You will also need to install the OCI sdk

```bash
pip install -U oci
```

## Usage

```bash
from llama_index.embeddings.oci_genai import OCIGenAIEmbeddings

embedding = OCIGenAIEmbeddings(
    model_name="MY_MODEL",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="MY_OCID",
)
```
motp 1.0.3
motp generator CLI and library
Requires Python: >=3.6
Keywords: motp

# motp

mOTP generator

- <https://motp.n4n5.dev/>
- <https://motp.sourceforge.net/>
- [motp.py](./motp.py)
- [motp.sh](./motp.sh)

## Python package

```sh
python -m pip install motp
python -m motp

# from args
python -m motp -s secret -p pin

# from input
python -m motp -s - -p -

# from files
python -m motp --file-secret ./pin_secret --file-pin ./pin_file
```

## License

- [MIT](./LICENSE)
blenv 0.0.1b4
A utility for creating and managing Blender development environments
Requires Python: >=3.7

# Blenv - Blender Environment Manager

🔴 warning - blenv is in beta - api or config may change 🔴

## Overview
Blenv is like `venv` for [Blender](https://docs.blender.org/api/current/index.html) python projects. It uses a `blenv.yaml` for configuring 1 or more blender/python environments for a given project. This project makes setting up a development environment for blender app templates and add ons simple.

The cli utility has a command to run blender using a selected environment. It will generate blender cli options and environment variables that point blender to your current project so it will load your addon or app template. You don't need to worry about making sure it's in the correct Blender folder. Plus, it adds hot-reloading to your development workflow. This speeds up your workflow and also keeps your blender installation clean of all your test applications. You can define multiple environments in one project test against different blender versions or run python commands.

## Documentation

See [DOCUMENTATION.md](./DOCUMENTATION.md) in the [repository](https://github.com/medium-tech/bl-env).