semvec/pypi-packages huggingface dataset:
atibotlog 0.1.4
A Python package for bot automation and database logging.
Requires Python: >=3.12

# ATI_Bot_Log

ATI Bot Log is a Python package designed to simplify bot operation logging and data management. It integrates seamlessly with SQL databases to track bot transactions, owner details, and version control.

## Features

- Insert bot owner details into the database.
- Log bot transactions with dynamic and configurable parameters.
- Automatically validate bot keys and version control.
- SQLAlchemy support for efficient database interactions.

## Installation
pip install atibotlog
sony-custom-layers 0.3.0
Sony Custom Layers package
Requires Python: <3.12,>=3.8

# Sony Custom Layers (SCL)

Sony Custom Layers (SCL) is an open-source project implementing detection post process NN layers not supported by the TensorFlow Keras API or Torch's torch.nn for the easy integration of those layers into pretrained models.

## Table of Contents

- [Getting Started](#getting-started)
  - [Installation](#installation)
  - [Supported Versions](#supported-versions)
- [API](#api)
  - [TensorFlow API](#tensorflow-api)
  - [PyTorch API](#pytorch-api)
- [License](#license)


## Getting Started

This section provides an installation and a quick starting guide.

### Installation

To install the latest stable release of SCL, run the following command:
```
pip install sony-custom-layers
```
By default, no framework dependencies are installed.
To install SCL including the latest tested dependencies (up to patch version) for TensorFlow:
```
pip install sony-custom-layers[tf]
```
To install SCL including the latest tested dependencies (up to patch version) for PyTorch/ONNX/OnnxRuntime:
```
pip install sony-custom-layers[torch]
```
### Supported Versions

#### TensorFlow

| **Tested FW versions** | **Tested Python version** | **Serialization** |
|------------------------|---------------------------|-------------------|
| 2.10                   | 3.8-3.10                  | .h5               |
| 2.11                   | 3.8-3.10                  | .h5               |
| 2.12                   | 3.8-3.11                  | .h5  .keras       |
| 2.13                   | 3.8-3.11                  | .keras            |
| 2.14                   | 3.9-3.11                  | .keras            |
| 2.15                   | 3.9-3.11                  | .keras            |

#### PyTorch

| **Tested FW versions**                                                                                                   | **Tested Python version** | **Serialization**              |
|--------------------------------------------------------------------------------------------------------------------------|---------------------------|--------------------------------|
| torch 2.0-2.4<br/>torchvision 0.15-0.19<br/>onnxruntime 1.15-1.19<br/>onnxruntime_extensions 0.8-0.12<br/>onnx 1.14-1.16 | 3.8-3.11                  | .onnx (via torch.onnx.export)  |

## API
For sony-custom-layers API see https://sony.github.io/custom_layers

### TensorFlow API
For TensorFlow layers see
[KerasAPI](https://sony.github.io/custom_layers/sony_custom_layers/keras.html)

To load a model with custom layers in TensorFlow, see [custom_layers_scope](https://sony.github.io/custom_layers/sony_custom_layers/keras.html#custom_layers_scope)

### PyTorch API
For PyTorch layers see
[PyTorchAPI](https://sony.github.io/custom_layers/sony_custom_layers/pytorch.html)

No special handling is required for torch.onnx.export and onnx.load.

For OnnxRuntime support see [load_custom_ops](https://sony.github.io/custom_layers/sony_custom_layers/pytorch.html#load_custom_ops) 

## License
[Apache License 2.0](LICENSE.md).
emojiswahili 1.0.2
Python library for working with emojis in Swahili
Requires Python: >=3.7
Keywords: emoji, swahili, emojiswahili, python, unicode

# emojiswahili

**Emojiswahili** is a Python library for working with emojis in Swahili.
---

## Features

- **Supports Swahili translations** for Unicode emojis.
- **Converts text with Swahili emoji codes** (e.g., `:nishani_ya_dhababu:`) **to Unicode emojis** (e.g., `🥇`).
- **Converts Unicode emojis** back **to Swahili emoji codes**.
- **Lists all emojis in a string** (with their positions), e.g., `emoji_list()`.
- **Extracts distinct emojis** in a string, e.g., `distinct_emoji_list()`.
- **Counts emojis** in a string (optionally only unique ones), e.g., `emoji_count()`.
- **Checks if a string is purely emoji**, e.g., `purely_emoji()`.

---


## Example Usage

Emojis defined by the [`Unicode consortium`](https://unicode.org/emoji/charts/full-emoji-list.html)  
can be easily used with Swahili translations.

```python 
>>> import emojiswahili

# Emojize a string
>>> print(emojiswahili.emojize('Mshindi wakwanza anapata :nishani_ya_dhababu:'))
Mshindi wakwanza anapata 🥇

# Demojize a string
>>> print(emojiswahili.demojize('Mshindi wakwanza anapata 🥇'))
Mshindi wakwanza anapata :nishani_ya_dhababu:

# List all emojis (with positions) in a string
>>> text = "Hii ni 🥇 na 🥈"
>>> emoji_positions = emojiswahili.emoji_list(text)
>>> print(emoji_positions)
[{'match_start': 7, 'match_end': 8, 'emoji': '🥇'},
 {'match_start': 12, 'match_end': 13, 'emoji': '🥈'}]

# Get a list of distinct emojis
>>> print(emojiswahili.distinct_emoji_list(text))
['🥇', '🥈']

# Count emojis in a string
>>> print(emojiswahili.emoji_count(text))
2
>>> print(emojiswahili.emoji_count(text, unique=True))
2

# Check if a string is purely emoji
>>> print(emojiswahili.purely_emoji('🥇🥈'))
True
>>> print(emojiswahili.purely_emoji('Hii ni 🥇 na 🥈'))
False

```

---

## Installation

Install via pip:

```console
$ python -m pip install emojiswahili --upgrade
```

---

## Development Guide

### Code Style Check

Ensure the code adheres to style guidelines using `ruff`:

```console
$ python -m pip install ruff
$ ruff check emojiswahili
```

### Type Checking

Test the type hints using `pyright` or `mypy`:

```console
$ python -m pip install pyright mypy typeguard
$ pyright emojiswahili
$ pyright tests
$ mypy emojiswahili
$ pytest --typeguard-packages=emojiswahili
```

---

## Emoji Lists

### Swahili Emoji List

You can find the complete Swahili emoji list at:

- [`Unicode Emoji List in Swahili`](https://emojiterra.com/keyboard/sw)  

---

## Authors and Maintainers

This project is developed and maintained by the eGARIDC Team / [`@ega`](https://ega.go.tz/)
dagster-contrib-weaviate 0.0.1
Dagster integration with Weaviate
Requires Python: >=3.9

# dagster-contrib-weaviate

A dagster module that provides integration with [Weaviate](https://weaviate.io/)
(both Cloud and Self-Hosted Weaviate instances).

## Installation

The `dagster_contrib_weaviate` module is available as a PyPI package - install with your preferred python 
environment manager (We recommend [uv](https://github.com/astral-sh/uv)).

```
source .venv/bin/activate
uv pip install dagster_contrib_weaviate
```

## Example Usage (Local Weaviate Instance)

(Based on the [Weaviate Quickstart Guide (Local)](https://weaviate.io/developers/weaviate/quickstart/local))

```python
from dagster import Definitions, asset
from dagster_contrib_weaviate import WeaviateResource, LocalConfig

@asset
def my_table(weaviate: WeaviateResource):
    with weaviate.get_client() as weaviate_client:
        questions = weaviate_client.collections.get("Question")
        questions.query.near_text(query="biology", limit=2)

defs = Definitions(
    assets=[my_table],
    resources={
        "weaviate": WeaviateResource(
            connection_config=LocalConfig(
                host="192.168.0.10", 
                port=8080, 
            )
        ),
    },
)
```


## Example Usage (Weaviate Cloud Instance)

Based on the [Weaviate Cloud Quickstart Guide](https://weaviate.io/developers/wcs/quickstart)

```python
from dagster import Definitions, asset
from dagster_contrib_weaviate import WeaviateResource, CloudConfig

@asset
def my_table(weaviate: WeaviateResource):
    with weaviate.get_client() as weaviate_client:
        questions = weaviate_client.collections.get("Question")
        questions.query.near_text(query="biology", limit=2)

defs = Definitions(
    assets=[my_table],
    resources={
        "weaviate": WeaviateResource(
            connection_config=CloudConfig(
                cluster_url=wcd_url
            ),
            auth_credentials={
                "api_key": wcd_apikey
            },
            headers={
                "X-Cohere-Api-Key": cohere_apikey,
            }
        ),
    },
)
```


## Development

The `Makefile` provides the tools required to test and lint your local installation

```sh
make test
make ruff
make check
```
dagster-contrib-gemini 0.0.2
Dagster integration with Gemini
Requires Python: >=3.9

# dagster-contrib-gemini

A dagster module that provides integration with [Gemini](https://deepmind.google/technologies/gemini/).

## Installation

The `dagster_contrib_gemini` module is available as a PyPI package - install with your preferred python 
environment manager (We recommend [uv](https://github.com/astral-sh/uv)).

```
source .venv/bin/activate
uv pip install dagster_contrib_gemini
```

## Example Usage

In addition to wrapping the Gemini GenerativeModel class (get_model/get_model_for_asset methods), 
this resource logs the usage of the Gemini API to to the asset metadata (both number of calls, and tokens). 
This is achieved by wrapping the GenerativeModel.generate_content method.

Note that the usage will only be logged to the asset metadata from an Asset context -
not from an Op context.
Also note that only the synchronous API usage metadata will be automatically logged - 
not the streaming or batching API.

```python
from dagster import AssetExecutionContext, Definitions, EnvVar, asset, define_asset_job
from dagster_contrib_gemini import GeminiResource


@asset(compute_kind="gemini")
def gemini_asset(context: AssetExecutionContext, gemini: GeminiResource):
    with gemini.get_model(context) as model:
        response = model.generate_content(
            "Generate a short sentence on tests"
        )

defs = Definitions(
    assets=[gemini_asset],
    resources={
        "gemini": GeminiResource(
            api_key=EnvVar("GEMINI_API_KEY"),
            generative_model_name="gemini-1.5-flash"
        ),
    },
)
```


## Development

The `Makefile` provides the tools required to test and lint your local installation

```sh
make test
make ruff
make check
```
dagster-contrib-anthropic 0.0.1
Dagster integration with Anthropic
Requires Python: >=3.9

# dagster-contrib-anthropic

A dagster module that provides integration with [Anthropic](https://www.anthropic.com//).

## Installation

The `dagster_contrib_anthropic` module is available as a PyPI package - install with your preferred python 
environment manager (We recommend [uv](https://github.com/astral-sh/uv)).

```
source .venv/bin/activate
uv pip install dagster_contrib_anthropic
```

## Example Usage

In addition to wrapping the Anthropic client (get_client/get_client_for_asset methods), 
this resource logs the usage of the Anthropic API to to the asset metadata (both number of calls, and tokens). 
This is achieved by wrapping the Anthropic.messages.create method.

Note that the usage will only be logged to the asset metadata from an Asset context -
not from an Op context.
Also note that only the synchronous API usage metadata will be automatically logged - 
not the streaming or batching API.

```python
from dagster import AssetExecutionContext, Definitions, EnvVar, asset, define_asset_job
from dagster_contrib_anthropic import AnthropicResource


@asset(compute_kind="anthropic")
def anthropic_asset(context: AssetExecutionContext, anthropic: AnthropicResource):
    with anthropic.get_client(context) as client:
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1024,
            messages=[
                {"role": "user", "content": "Say this is a test"}
            ]
        )

defs = Definitions(
    assets=[anthropic_asset],
    resources={
        "anthropic": AnthropicResource(api_key=EnvVar("ANTHROPIC_API_KEY")),
    },
)
```


## Development

The `Makefile` provides the tools required to test and lint your local installation

```sh
make test
make ruff
make check
```
dagster_contrib_gemini 0.0.1
Dagster integration with Gemini
Requires Python: >=3.9

# dagster-contrib-gemini

A dagster module that provides integration with [Gemini](https://deepmind.google/technologies/gemini/).

## Installation

The `dagster_contrib_gemini` module is available as a PyPI package - install with your preferred python 
environment manager (We recommend [uv](https://github.com/astral-sh/uv)).

```
source .venv/bin/activate
uv pip install dagster_contrib_gemini
```

## Example Usage

In addition to wrapping the Gemini GenerativeModel class (get_model/get_model_for_asset methods), 
this resource logs the usage of the Gemini API to to the asset metadata (both number of calls, and tokens). 
This is achieved by wrapping the GenerativeModel.generate_content method.

Note that the usage will only be logged to the asset metadata from an Asset context -
not from an Op context.
Also note that only the synchronous API usage metadata will be automatically logged - 
not the streaming or batching API.

```python
from dagster import AssetExecutionContext, Definitions, EnvVar, asset, define_asset_job
from dagster_contrib_gemini import GeminiResource


@asset(compute_kind="gemini")
def gemini_asset(context: AssetExecutionContext, gemini: GeminiResource):
    with gemini.get_model(context) as model:
        response = model.generate_content(
            "Generate a short sentence on tests"
        )

defs = Definitions(
    assets=[gemini_asset],
    resources={
        "gemini": GeminiResource(
            api_key=EnvVar("GEMINI_API_KEY"),
            generative_model_name="gemini-1.5-flash"
        ),
    },
)
```


## Development

The `Makefile` provides the tools required to test and lint your local installation

```sh
make test
make ruff
make check
```