semvec/pypi-packages huggingface dataset:
indico-plugin-payment-razorpay 3.0.5.1
Razorpay payments for Indico event registration fees
License: MIT
Requires Python: ~=3.9.0

# Razorpay Payment Plugin


This plugin provides a payment option for Indico's payment module using the
Razorpay Web Checkout API
SIXPay Saferpay API.

When used, the user will be sent to Razorpay to make the payment, and afterwards
they are automatically sent back to Indico.

## Changelog

### 1.0

- Initial release for Indico 3.0

## Credits

This plugin is based on [Indico SIXPay payment plugin](https://github.com/indico/indico-plugins/tree/master/payment_sixpay) which was 
Originally developed by Max Fischer for Indico 1.2 and 2.x. Updated to use the
latest SIXPay API by Martin Claus. Adapted to Indico 3.0 and Python 3 by the
CERN Indico Team.
teedoc-plugin-gitalk 1.3.0
gitalk comment plugin for teedoc
License: MIT
Keywords: gitalk comment teedoc plugin

teedoc-plugin-gitalk
====


[gitalk](https://github.com/gitalk/gitalk) comment support for [teedoc](https://github.com/Neutree/teedoc)

Create [applications](https://github.com/settings/applications/new) first, callback url set page that have gitalk(default doc and blog pages),`https://teedoc.github.io/get_started/zh/` for example.

config example:
```json
"teedoc-plugin-gitalk": {
            "from": "pypi",
            "config": {
                "classes": [
                    "type_doc",
                    "type_blog"
                ],
                "env": {
                    "clientID": "*****",
                    "clientSecret": "********",
                    "repo": "teedoc.github.io",
                    "owner": "teedoc",
                    "admin": ["Neutree"]
                }
            }
        }
```

admin can directly comment, it will automatically create issue
and you can create issue manually for one page, and then set issue number in markdown metadata:
```markdown
---
title: *****
gitalk-number: 2
---
```

or create label for issue, need `Gitalk` and one other lable like `Demo`, like [official issue](https://github.com/gitalk/gitalk/issues/1), and set id for page
```markdown
---
title: *****
id: Demo
---
```
or
```markdown
---
title: *****
gitalk-id: Demo
---
```
simpledatasetlabeler 0.1.1
Simple labeling tool
License: MIT

# simpledataset-labeler
Simple browser-based interface for dataset labeling.

Currently only Object Detection task is supported.

## Setup
```
pip install simpledatasetlabeler
```

## Usage
```
dataset_labeler <input_filepath> <output_filepath> [--host <hostname>] [--port <port_number>] [--dataset_type {image_classification,object_detection}]
```
Then, open your favorite browser and access to the "http://localhost:5000". (If you specified --port option, access the specified port instead.)

## How to build a package
```
make
```
cvsutils 0.1.11
Unofficial utility scripts for Microsoft Custom Vision Service
License: MIT

# cvsutils
Unofficial utility scripts for Microsoft Custom Vision Service.

## Setup
```sh
pip install cvsutils
export CVS_ENDPOINT='<endpoint>'
export CVS_TRAINING_KEY='<training_key>'
export CVS_PREDICTION_KEY='<prediction_key>'
export CVS_PREDICTION_RESOURCE_ID='<resource_id>'
```

Those keys and endpoint information can be found in the Custom Vision's settings page.

## Available commands

```sh
# Show a list of projects
cvs_list_projects [--verbose]

# Create a new project
cvs_create_project <dataset_filepath> [--project_name <name>] [--domain_id <domain_id>]

# Download dataset from a project
cvs_download_project <project_id> <output_dir> [--filter_tag <tag_id> [<tag_id> ...]]

# Train a model
cvs_train_project <project_id> [--domain_id <domain_id>] [--type {multiclass,multilabel}] [--force]

# Export a model
cvs_export_model <project_id> <iteration_id> {tensorflow,coreml,onnx} [--output_filepath <filepath>]
```

And
* cvs_add_images
* cvs_download_predictions
* cvs_evaluate_project
* cvs_get_domains
* cvs_predict_dataset
* cvs_predict_image
* cvs_remove_iteration
* cvs_show_project
* cvs_validate_dataset

To see the detailed help, please run the command with "-h" option.

## Dataset file format
This tool uses the SIMPLE dataset format to upload/download datasets from Custom Vision Service.

For details, please see the [simpledataset](https://github.com/shonohs/simpledataset) repository.