semvec/pypi-packages huggingface dataset:
autodistill-blip 0.1.3
BLIP module for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png?4"
      >
    </a>
  </p>
</div>

# Autodistill BLIP Module

This repository contains the code supporting the BLIP base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[BLIP](https://github.com/salesforce/LAVIS), developed by Salesforce, is a computer vision model that supports visual question answering and zero-shot classification. Autodistill supports classifying images using BLIP.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [BLIP Autodistill documentation](https://autodistill.github.io/autodistill/base_models/blip/).

## Installation

To use BLIP with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-blip
```

## Quickstart

```python
from autodistill_blip import BLIP

# define an ontology to map class names to our BLIP prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = BLIP(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)
base_model.label("./context_images", extension=".jpeg")
```


## License

This project is licensed under a [3-Clause BSD license](LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
aiolavapy 0.1.2
Async library for Lava API
Requires Python: >=3.10
Keywords: lava aiolava lavaapi

# Async Lava API library

### Example to use

```python
import asyncio

from aiolava import LavaBusinessClient


async def main():
    client = LavaBusinessClient(
        private_key="INSERT_PRIVATE_KEY",
        shop_id="INSERT_SHOP_ID"  # optional
    )
    
    invoice = await client.create_invoice(
        sum_=10,
        order_id="order#10"
    )
    print(invoice.data.url)
    
    status = await client.check_invoice_status(
        order_id="order#10"
    )
    print(status.data.status)


if __name__ == '__main__':
    asyncio.run(main())

```

### All documentation you can find [here](https://dev.lava.ru/)
optipack 0.0.27.dev0
The optimal ML package
Requires Python: >=3.8,<4.0

![optipack](asset/image/logo.png)

# What optipack do: 
- Workspace management: 
    - Generate folder structure for ML project 
    - Generate files following the given environment in yaml format, including: 
        - tool connection configuration 
        - hyperparameter configuration 
        - model run config
-  Logging: 🔥 all-in-one logger
    - Support console/file logger for users' log! 🔥 No more uggly print, no more terminal scrolling
    - Support tensorboard metric log. 

# Installation:  
## 1. Installation from PYPI: 
- Run `pip install optipack`
## 2. Installation from source: 
- Clone this repo 
- Change directory to optipack
- Run `pip install . `

# How to use: 
## 1. Use as CLI 
- All you need is running `optipack` on terminal :) 

## 2. Use as library
- ...
autodistill-detic 0.1.6
DETIC module for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill DETIC Module

This repository contains the code supporting the DETIC base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[DETIC](https://github.com/facebookresearch/Detic) is a transformer-based object detection and segmentation model developed by Meta Research.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [DETIC Autodistill documentation](https://autodistill.github.io/autodistill/base_models/detic/).

## Installation

To use DETIC with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-detic
```

## Quickstart

```python
from autodistill_detic import DETIC

# define an ontology to map class names to our DETIC prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = DETIC(
    ontology=CaptionOntology(
        {
            "person": "person",
        }
    )
)
base_model.label("./context_images", extension=".jpg")
```

## License

The code in this repository is licensed under an [MIT license](LICENSE).

See the Meta Research DETIC repository for more information on the [DETIC license](https://github.com/facebookresearch/Detic).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
aiolava 0.1.0
Async library for Lava API
Requires Python: >=3.10
Keywords: lava aiolava lavaapi

# Async Lava API library

### Example to use

```python
import asyncio

from aiolava import BusinessClient


async def main():
    client = BusinessClient(
        private_key="INSERT_PRIVATE_KEY",
        mics_key="INSERT_MICS_KEY",
        shop_id="INSERT_SHOP_ID"  # optional
    )
    
    invoice = await client.create_invoice(
        sum_=10,
        order_id="order#10"
    )
    print(invoice.data.url)
    
    status = await client.check_invoice_status(
        order_id="order#10"
    )
    print(status.data.status)


if __name__ == '__main__':
    asyncio.run(main())

```

### All documentation you can find [here](https://dev.lava.ru/)
micromind 0.2.1
MicroMind
Requires Python: >=3.8
Keywords: feed,reader,tutorial

[![Python version: 3.8 | 3.9 | 3.10](https://img.shields.io/badge/python-3.8%20|3.9%20|%203.10-blue)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://github.com/fpaissan/micromind/blob/main/LICENSE)
[![PyPI version](https://img.shields.io/pypi/v/micromind)](https://pypi.org/project/micromind/)

This is the official repo of `micromind`, a toolkit that aims at bridging two communities: artificial intelligence and embedded systems. `micromind` is based on [PyTorch](https://pytorch.org) and provides exportability for the supported models in ONNX, Intel OpenVINO, and TFLite.

---------------------------------------------------------------------------------------------------------

## 💡 Key features

- Smooth flow from research to deployment;
- Support for multimedia analytics recipes (image classification, sound event detection, etc);
- Detailed API documentation;
- Tutorials for embedded deployment;

---------------------------------------------------------------------------------------------------------

## 🛠️️ Installation

### Using Pip

First of all, install [Python 3.8 or later](https://www.python.org). Open a terminal and run:

```
pip install micromind
```
for the basic install. To install `micromind` with the full exportability features, run

```
pip install micromind[conversion]
```

### From source

First of all, install [Python 3.9 or later](https://www.python.org).
Clone or download and extract the repository, navigate to `<path-to-repository>`, open a
terminal and run:

```
pip install -e .
```
for the basic install. To install `micromind` with the full exportability features, run

```
pip install -e .[conversion]
```

### Training networks with recipes

After the installation, get started looking at the examples and the docs!

### Export your model and run it on your MCU
Check out [this](https://docs.google.com/document/d/1zt5urvNtI9VSJcoJdIeo10YrdH-tZNcS4JHbT1z5udI/edit?usp=sharing)
tutorial and have fun deploying your network on MCU!

---------------------------------------------------------------------------------------------------------

## 📧 Contact

[francescopaissan@gmail.com](mailto:francescopaissan@gmail.com)

---------------------------------------------------------------------------------------------------------
autodistill-sam-hq 0.1.1
Segment Anything High Quality (SAM HQ) model for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill Segment Anything HQ Module

This repository contains the code supporting the Segment Anything base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[SAM HQ](https://github.com/SysCV/sam-hq) is a zero-shot segmentation model capable of producing detailed masks, developed by [ETH VIS](https://github.com/SysCV). SAM HQ can segment an entire image into masks, or use points to segment specific parts of an object. You can use Segment Anything with Autodistill to segment objects. Segment Anything does not assign classes, so you should use SAM HQ model with a tool like Grounding DINO or GPT-4V.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [SAM HQ Autodistill documentation](https://autodistill.github.io/autodistill/base_models/samhq/).

## Installation

To use SAM HQ with autodistill, you need to install the following dependency:

```bash
pip3 install autodistill-sam-hq
```

## Quickstart

```python
from autodistill_sam_hq import HQSAM

base_model = HQSAM(None)

masks = base_model.predict("./image.jpeg")

print(masks)
```

## License

This project is licensed under an [Apache 2.0 license](LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
autodistill-segment-anything 0.1.1
Segment Anything model for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill Segment Anything Module

This repository contains the code supporting the Segment Anything base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[Segment Anything (SAM)](https://github.com/facebookresearch/segment-anything) is a segmentation model developed by Meta AI Research. Segment Anything can segment an entire image into masks, or use points to segment specific parts of an object. You can use Segment Anything with Autodistill to segment objects. Segment Anything does not assign classes, so you should use the model with a tool like Grounding DINO or GPT-4V.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [SAM Autodistill documentation](https://autodistill.github.io/autodistill/base_models/SAM/).

## Installation

To use SAM with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-segment-anything
```

## Quickstart

```python
from autodistill_segment_anything import SegmentAnything

base_model = SegmentAnything(None)

masks = base_model.predict("./image.jpeg")

print(masks)
```


## License

This project is licensed under an [Apache 2.0 license](LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
ite10stringutils 1.0.0
A framework for strings
Requires Python: >=3.6, <4
Keywords: strings,unittest,development

# A sample Python project

![Python Logo](https://www.python.org/static/community_logos/python-logo.png "Sample inline image")

A sample project that exists as an aid to the [Python Packaging User
Guide][packaging guide]'s [Tutorial on Packaging and Distributing
Projects][distribution tutorial].

This project does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.

[The source for this project is available here][src].

Most of the configuration for a Python project is done in the `setup.py` file,
an example of which is included in this project. You should edit this file
accordingly to adapt this sample project to your needs.

----

This is the README file for the project.

The file should use UTF-8 encoding and can be written using
[reStructuredText][rst] or [markdown][md use] with the appropriate [key set][md
use]. It will be used to generate the project webpage on PyPI and will be
displayed as the project homepage on common code-hosting services, and should be
written for that purpose.

Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not a
good idea, although a simple “What's New” section for the most recent version
may be appropriate.

[packaging guide]: https://packaging.python.org
[distribution tutorial]: https://packaging.python.org/tutorials/packaging-projects/
[src]: https://github.com/pypa/sampleproject
[rst]: http://docutils.sourceforge.net/rst.html
[md]: https://tools.ietf.org/html/rfc7764#section-3.5 "CommonMark variant"
[md use]: https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
automate-office 0.3.1
Offic自动化工具-自用，在原有包的基础上二次开发, PPT部分暂时告一段落, Word暂时用处不大, Excel做了一些函数和例子

# PPT
### 空slide
1. 创建空PPT, 支持inches和cm
    ```python
    from automate_office.automate_ppt.MyPPTCreator import PPTCreator
    my_ppt = PPTCreator(size_params={"unit_type": "inches", "width": 10, "height": 5.625})
    ```
2. 存储PPT，默认存储在Downloads目录下
    ```python
    my_ppt.save_pptx(filename=None, save_path=None)
    ```

3. 创建slide
    ```python
    my_slide = my_ppt.create_slide(slide_idx=6)
    ``` 

4. 创建文本框
    ```python
    my_ppt.add_textbox(
        self, 
        cur_slide: slide.Slide, 
        # left top width height, 相对于长宽的比例，0-1之间
        rel_coordinate=(0, 0, 1, 1), 
        paragraph_infos=None
    )
    ``` 
    - paragraph_infos: 下面是一个例子
    ```python
    paragraph_infos = [
        {
            "对齐": "居中",
            "文字": [
                ["段落1第1部分文字", "微软雅黑", 12, False, False, (0, 255, 0)],
                ["段落1第2部分文字", "微软雅黑", 12, False, False, (0, 0, 255)],
                ...
            ],
            "段落间距": 8
        },
        {
            "对齐": "左对齐",
            "文字": [
                ["段落2第1部分文字", "微软雅黑", 12, False, False, (0, 255, 0)],
                ["段落2第2部分文字", "微软雅黑", 12, False, False, (0, 0, 255)],
                ...
            ],
            "段落间距": 8
        },
    ]
    ```

5. 创建矩形
    ```python
    my_ppt.add_rectangle_shape
    ```
6. 创建图片
    ```python
    my_ppt.add_picture
    ```
7. 创建柱状图
    ```python
    my_ppt.add_bar_chart
    ```
8. 创建折线图
    ```python
    my_ppt.add_line_chart
    ```
9. 创建饼图
    ```python
    my_ppt.add_pie_chart
    ```
10. 创建表格
    ```python
    my_ppt.add_table
    ```
11. 修改已有PPT的文字
    ```python
    from automate_office.automate_ppt.modify_ppt import set_textframe
    set_textframe(text_frame, paragraph_infos=None, vertical_anchor = MSO_ANCHOR.MIDDLE)
    ```
autodistill-fastsam 0.1.1
FastSAM module for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill FastSAM Module

This repository contains the code supporting the FastSAM base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[FastSAM](https://github.com/CASIA-IVA-Lab/FastSAM) is a segmentation model trained on 2% of the SA-1B dataset used to train the [Segment Anything Model](https://github.com/facebookresearch/segment-anything).

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [FastSAM Autodistill documentation](https://autodistill.github.io/autodistill/base_models/fastsam/).

## Installation

To use FastSAM with autodistill, you need to install the following dependency:

```bash
pip3 install autodistill-fastsam
```

## Quickstart

> [!NOTE]

> When you first run this model, the installation process will start. Inference may take a few seconds (in testing, up to 30 seconds) while the model is downloaded and installed. Once the model is installed, inference will be much faster.

```python
from autodistill_fastsam import FastSAM

# define an ontology to map class names to our FastSAM prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = FastSAM(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)
base_model.label("./context_images", extension=".jpeg")
```


## License

This project is licensed under an [Apache 2.0 license](LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
autodistill-owl-vit 0.1.2
OWL-ViT module for use with Autodistill
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill OWL-ViT Module

This repository contains the code supporting the OWL-ViT base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[OWL-ViT](https://huggingface.co/google/owlvit-base-patch32) is a transformer-based object detection model developed by Google Research.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [OWL-ViT Autodistill documentation](https://autodistill.github.io/autodistill/base_models/owlvit/).

## Installation

To use OWL-ViT with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-owl-vit
```

## Quickstart

```python
from autodistill_owl_vit import OWLViT

# define an ontology to map class names to our OWLViT prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = OWLViT(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)
base_model.label("./context_images", extension=".jpg")
```

## License

The code in this repository is licensed under an [Apache 2.0 license](LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
mactop 0.1.11
Htop for macbook.
Requires Python: >=3.10,<4.0

# mactop

Mactop is a tool like htop, but you can decide the data you want and decide the
layout. It is more like a Grafana for your macbook, but in terminal.

It looks like this:

![](./assets/mactop.png)

## Installation

```shell
pip install mactop
```

## Usage

```
sudo mactop
```

It requires `sudo` because `powermetrics` requires `sudo`, you can run `mactop`
without `sudo` but some metrics will be missing.

For M1 Macbook users, please run:

```
sudo mactop -t m1.xml
```

What is `-t` here? It's for "theme"! And you can have your own theme!

## Design Your Own Mactop

We use HTML + CSS style to setup the layout.

You can use `id` or `class` to select the element, like this:

```html
<Mactop>
  <layout>
    <Horizontal id="row-1">
      <SensorsPanel></SensorsPanel>
    </Horizontal>
  </layout>

  <style>
    #row-1 {
      color: red;
    }
  </style>
</Mactop>
```

Save your content to a file, for example, `my-theme.xml`, then run `mactop` with
`mactop -t my-theme.xml`.

Components do not support inline-css, but you can set attributes on components.

Common attributes that every components support:

- `id`;
- `class` or `classes`, separated by space;
- `refresh_interval`: set this will overwrite command line arguments
  `--refresh-interval` for that component.

For component's supported attributes and component's name, please refer to
`mactop/panels/__init__.py` and check the source code. Please bare with me, it
is messy for now, I am working on documentations. If you have any questions,
feel free to open an issue.

For examples of layouts, you can refer `mactop/themes/` directory.

If you made some beautiful layout, please send it to me! By open a PR or issue,
I can merge it into this repo, thanks.

## Debug

Mactop comes with verbose log support.

`-v` means enable `info` log, and more `v` means more logs, max `-vvv`.

```shell
mactop -vvv -l mactop.log
```

Then you can open another terminal `tail -f mactop.log` to see the logs.

Mactop use `powermetrics` to get metrics from your mactop, `powermetrics` is
different on different Macbooks. If you met some issue, better submit a
`powermetrics` sample in the issue, thanks.

Use this command (add `--debug`), Mactop will write json formatted powermetrics
file on your current `$(PWD)/debug_json`. (If you decide to paste it, only one
sample (one file) is enough).

```shell
$ mactop -vvv -l mactop.log --debug
$ ls debug_json
mactop_debug_20231206_16:34:28.json  mactop_debug_20231206_16:41:55.json  mactop_debug_20231206_16:46:21.json
mactop_debug_20231206_16:34:29.json  mactop_debug_20231206_16:44:46.json
```

## Development

This project use [poetry]() to manage dependencies.

Clone this project and make sure you have poetry.

```shell
pip install poetry
git clone git@github.com:laixintao/mactop.git
```

Then install dependencies:

```shell
poetry install
```

You can then make changes, and test with `poetry run mactop`.