semvec/pypi-packages huggingface dataset:
toolri 1.0.4
A tool for extracting, labeling and linking entities in document images for Information Extraction tasks.
Requires Python: >=3.10

# ToolRI

ToolRI was created to simplify and standardize the creation of samples for the task of Information Extraction in document images. The tool allows text extraction by OCR, the creation of document entities and their labeling and linking. The project was created purely with Python and can be run on any desktop platform. The graphical user interface is implemented thanks to the amazing <a href="https://github.com/tomschimansky/customtkinter">CustomTkinter</a> library.

## Instalation

### PyPi

Install the ToolRI package with `pip`:

    pip install toolri

### Source

Clone the ToolRI repository with:

    git clone https://github.com/Victorgonl/ToolRI

And install using `pip`:

    pip install ./ToolRI/

## Standalone

### Download

You can download a portable binary of the tool to start using right away. Download and run a version on the <a href="https://github.com/Victorgonl/ToolRI/releases">releases</a> page of the ToolRI repository.

### Build

To build the standalone version of ToolRI into a portable binary, clone the repository:

    git clone https://github.com/Victorgonl/ToolRI

Change current directory to `./ToolRI`:

    cd ./ToolRI

Install all the dependencies found on `requirements.txt`:

    pip install -r requirements.txt

And run the script `toolri_build.py`:

    python3 toolri_build.py

The binary will be available on `dist` folder.

## Documentation

***Under construction.*** :construction:

## Tesseract OCR

To be able to use the OCR function in ToolRI, Tesseract OCR must be installed separately.

***For now, OCR is configured for English and Portuguese languages only, but it will be updated soon for all languages available.*** :construction:

### Debian based

Use the command:

    sudo apt-get install tesseract-ocr tesseract-ocr-eng tesseract-ocr-por

### Windows

- Download and run the installer available at https://github.com/UB-Mannheim/tesseract/wiki.

- Make sure to install Tesseract on `C:\Program Files\Tesseract-OCR\` (the default directory) due to a predefined configuration in current ToolRI version.

## Usage

ToolRI was developed and used to create the <a href="https://github.com/LabRI-Information-Retrieval-Lab/UFLA-FORMS">UFLA-FORMS</a> dataset. Download the dataset to try the tool on the available samples or create a new metadata for any document image available.

### Example

    import toolri

    image = toolri.load_image("document_image.jpg")

    labels = [
        toolri.ToolRILabel(name="QUESTION", color="#004B80", links=["ANSWER"], is_visible=True),
        toolri.ToolRILabel(name="ANSWER", color="#00943E", links=[], is_visible=True)
    ]

    data = toolri.toolri(image=image, data=data, labels=labels)

    toolri.draw_data_on_image(image=image, data=data, labels=labels).show()
evotrain 0.0.18
Evoland training workflows
Requires Python: <=3.12,>=3.11

# EvoTrain

EvoTrain is a cutting-edge project focused on global land cover mapping, essential for understanding and managing Earth's resources. Building upon the success of ESA’s WorldCover, EvoTrain introduces the next-generation of global land cover products as part of the Copernicus Land Cover and Forest Monitoring service (LCFM).

## EvoNet Algorithm

At the core of EvoTrain is EvoNet, a novel algorithm that combines the strengths of convolutional neural networks (CNNs) and pixel-based classifiers into a unified framework. This innovative approach addresses long-standing challenges in land cover classification, such as spatial accuracy, generalization, and the integration of heterogeneous datasets.

### Key Features of EvoNet:
- **Dual Architecture**: Integrates a CNN-based spatial feature extractor with a multi-layer perceptron (MLP) pixel classifier.
- **Enhanced Spatial Features**: Provides contextual information to improve pixel-level predictions.
- **Efficiency**: Avoids inefficiencies of conventional approaches that rely on multiple regional models or exclusively use CNNs or pixel classifiers.

## Evotrain Dataset

Supporting EvoNet's performance is the Evotrain dataset, a newly developed global dataset designed for a wide range of remote sensing applications. The dataset features:
- **Stratified Sampling**: Ensures balanced representation of all classes and landscapes globally.
- **High-Quality Annotations**: Collected using a novel AI-assisted system, including mixed classes for accurate land cover fraction estimates at high resolution.

## Open Source and Collaboration

The EvoNet architecture, Evotrain dataset, and the supporting Python ecosystem will be open-sourced and publicly released to foster collaboration and drive advancements in the remote sensing community.

## Advancements in Global Mapping

EvoNet sets a new standard for global mapping, as demonstrated by the datasets produced under the LCFM service. Notably, LCM-10, the new global annual land cover map, surpasses WorldCover in accuracy and operational efficiency, addressing key algorithmic limitations.

## Conclusion

EvoTrain represents a significant advancement in global land cover mapping, with transformative potential for remote sensing applications. This project showcases EvoNet’s methodology, the development of the Evotrain dataset, and the resulting improvements in global land cover mapping.
steam-api-pkg 0.1.2
A basic package to interface with the Steam API.
Requires Python: >=3.8
Keywords: steam, api

# SteamUser API Wrapper

A Python class to interact with the Steam Web API, providing access to user information, friends lists, game libraries, achievements, and recently played games.

## Features

- **Fetch Friends List**: Retrieve the Steam user's friends, with an option to return only Steam IDs.
- **User Summary**: Access high-level profile information like name, avatar, and online status.
- **Owned Games**: Retrieve the list of games owned by the user, including playtime details.
- **User Game Stats**: Get game-specific statistics and achievement progress for a given game.
- **Recently Played Games**: Fetch the user's recently played games along with playtime information.

## Installation

Ensure you have Python 3.8+ installed and the `requests` package:

```bash
pip install requests
```

## Usage

1. Import the `SteamUser` class.
2. Instantiate the class with your Steam API key and the target Steam ID.

```python
from steam_api_pkg.steam_user import SteamUser

# Initialize the SteamUser object
user = SteamUser(api_key="YOUR_API_KEY", steam_id="76561198012345678")
```

### Methods

#### `getFriendsList()`

Fetches the user's friends list.

- **Returns:**
  - `list`: List of friends with details.

- **Example:**

```python
friends = user.getFriendsList()
print(friends)
```

#### `getUserSummary()`

Retrieves a summary of the user's profile.

- **Returns:**
  - `dict`: Profile information including Steam ID, name, avatar, and more.

- **Example:**

```python
summary = user.getUserSummary()
print(summary)
```

#### `getOwnedGames()`

Fetches the list of games owned by the user.

- **Returns:**
  - `dict`: Game details including app ID, name, and playtime.

- **Example:**

```python
games = user.getOwnedGames()
print(games)
```

#### `getUserGameStats(APP_ID)`

Fetches stats and achievements for a specific game.

- **Parameters:**
  - `APP_ID` (int): The Steam application ID of the game.

- **Returns:**
  - `dict`: Game-specific stats and achievements.

- **Example:**

```python
stats = user.getUserGameStats(105600)  # Terraria
print(stats)
```

#### `getRecentlyPlayed()`

Retrieves the user's recently played games.

- **Returns:**
  - `dict`: Details of recently played games and playtime information.

- **Example:**

```python
recent_games = user.getRecentlyPlayed()
print(recent_games)
```

## Notes

- The Steam API is subject to privacy settings. If data is restricted, methods may return `None` or incomplete information.
- Ensure your API key is valid and the Steam ID is correct.

## License

This project is licensed under the MIT License.