semvec/pypi-packages huggingface dataset:
UFC-Data-Scraper 1.0.0
Packages uses BeautifulSoup API to scrape historical data from ufcstat.com
Requires Python: >=3.6

UFC_Data_Scraper
===============
UFC_Data_Scraper uses Beautiful Soup API to scrape fight and fighter data from ufcstats.com and returns data in a JSON format.


Install
--------
To install UFC_Data_Scraper:
```bash
pip install UFC_Data_Scraper
```


Usage
-----

### Step 1
Create instance of Ufc_Data_Scraper object:  
`scraper = s.Ufc_Data_Scraper()`
### Step 2
Choose class method for getting fighter or Fights data:

`list_of_fighters = scraper.get_all_fighters()`

or

`list_of_fights =  scraper.scrape_all_fights()`

### Step 3
Convert from list of fighter or fight objects into list of dictionaries:

`json_fighter_list = s.Ufc_Data_Scraper().fighter_to_Json(list_of_fighters)`

 or
 
`json_fights_list = s.Ufc_Data_Scraper().fights_to_Json(list_of_fights)`

### Step 4
Save dictionaries into Json:

`s.Ufc_Data_Scraper().save_json("<name of JSON file>", json_fighter_list)`

JSON Format
------------
The format of both fighter and fight data saved to .json files are described in JSON_spec folder
telesto-base 0.0.4
Base tools for telesto.ai models
Requires Python: >=3.6

# telesto-base
Base Docker image and tools for telesto.ai models.

# Instructions

`telesto-base` contains a pip-installable Python package and a Docker image, allowing you to
easily package your models for telesto.ai competitions.

## telesto-base package
To install the module, you can simply use pip:
```
pip install telesto-base
```
If you would like to use the latest not yet released version, you can install the one in the 
`develop` branch.
```
pip install git+https://github.com/telesto-ai/telesto-base.git@develop
```

## The base image
The base image contains the pre-installed `telesto-base` module. Your submissions will use this
as a base, so you'll only have to worry about the algorithms and not the packaging. To use it
locally, you can pull the image from Docker Hub:
```
docker pull telestoai/model-api-base:latest
```

Alternatively, the image can also be built locally with the command 
```
docker build -t telestoai/model-api-base -f Dockerfile .
```

## An example model
If you are stuck on how to prepare your model for submission, we have prepared a concrete example
for you. The example is available in the [telesto-models](https://github.com/telesto-ai/telesto-models) repository with further instructions
on the usage.

## Test classification model API

Build and start a container
```
docker build -t telestoai/model-api-base -f Dockerfile .
docker run -p 9876:9876 --name model-api-base --rm --env USE_FALLBACK_MODEL=1 \
    telestoai/model-api-base classification
```

Send a sample input
```
curl -X POST -H "Content-Type:application/json" --data-binary @tests/data/class/example-input.json -i \
    http://localhost:9876/
...
{
    "predictions": [
        {"probs": {"cat": 0.32015, "dog": 0.67985}, "prediction": "dog"},
        {"probs": {"cat": 0.81545, "dog": 0.18455}, "prediction": "cat"}
    ]
}
```

## Test segmentation model API

Build and start a container
```
docker build -t telestoai/model-api-base -f Dockerfile .
docker run -p 9876:9876 --name model-api-base --rm --env USE_FALLBACK_MODEL=1 \
    telestoai/model-api-base segmentation
```

Post a sample input
```
curl -X POST -H "Content-Type:application/json" --data-binary @tests/data/segm/example-input.json -i \
    http://localhost:9876/jobs
...
{
    "job_id": "b741bd19767441f6b7abd022744083c9"
}
```

Get the result
```
curl -H "Content-Type:application/json" -i http://localhost:9876/jobs/b741bd19767441f6b7abd022744083c9
...
{
    "mask": {
        "content": "<BASE_64_IMAGE>"
    }
}
```
tdpt 1.3
Torrent downloading progress on Telegram
Requires Python: >=3.6
Keywords: telegram bot transmission liveupdates torrents rtorrent torrent

TDPT
====

Torrent downloading progress on Telegram

![Screencast](https://i.imgur.com/yTsldua.gif "Screencast")


## Features
* Support for two most popular torrent clients `Transmission`, `rTorrent` and `deluge`
* Multithreading, each downloading item is handled separately
* Running post download script in separate process
* Self-adjustment of `Telegram` bot limits based on number of torrents
  tracked
* Easy configuration
* Uploading new torrent - just send torrent file to chat


## Installation
You need Python 3 to run this program.

```
$ pip install --user tdpt
```

Create configuration file
```
$ cp tdpt.ini.template tdpt.ini
```
Edit it and save.


## FAQ

### `Transmission` already supports running script upon finishing download
Yes, that is true.  However the script that `transmission` will run will
be executed in the same thread.  That means any long running process
would freeze it for that amount of time which is highly undesirable.

### `tdpt` picks up new items so slow
Currently it fetches new items every 20 seconds, because this operation
can be quite expensive.  I might add option to specify this interval
based on your preferences.

### I am downloading 20 items and `tdpt` updates them very rarely
`Telegram` puts limit of how often bot can make request to one channel.
Currently the limit is 20 updates per minute.  `tdpt` makes time
adjustment based on number of torrents that it tracks to avoid hitting
limits.