pocket-tagger 0.1.1

Description:

pockettagger 0.1.1

Auto Pocket Tagger
Use Google cloud's Natural Language Processing API to automatically analyze the webpage from articles saved in your Pocket list, derive tags/keywords based on the content of the page, and add tags to the articles in Pocket list for free.

Pocket has suggested tags service for their paid premium plans. You can find more about it here. This still requires manual work of adding the tags to each article one-by-one. This package automates all of it for free.

Features

Uses Python wrapper for Pocket API to retrieve articles in the My List
Uses Beautiful Soup to scrape webpages
Uses Google Cloud's Natural Language Processing API to generate list of categories and entities from the content of the webpage
Uses Pocket API to add tags to articles in your My List

Usage
Installation
Install published version from pypi
$ pip install pocket-tagger

Install latest version from git
$ pip install git+https://github.com/sanghviharshit/pocket-tagger

Prerequisites
Google Cloud
This package relies on Google cloud natural language processing API, which requires billing enabled on your project.
You can find the quickstart instructions here
Options:

Create a service account and download the credentials file - https://cloud.google.com/video-intelligence/docs/common/auth

tagger = PocketTagger(gcloud_credentials_file="gcloud_credentials_file.json")


or Configure gloud locally - https://cloud.google.com/sdk/gcloud/reference/init

tagger = PocketTagger()

Pocket API
To fetch the articles list and add tags, you need a developer key from here
Create a new Application with modify and retrieve permissions. Save the Consumer Key and Access Token.
tagger = PocketTagger(consumer_key='your-consumer-key',
access_token='your-access-token')

Examples
# Initialize PocketTagger with GCloud and Pocket API Credentials
tagger = PocketTagger(gcloud_credentials_file="gcloud_credentials_file.json",
consumer_key='pocket-consumer-key',
access_token='pocket-access-token')

# Check https://getpocket.com/developer/docs/v3/retrieve for additional list of options you can pass for retrieving pocket list
articles = tagger.get_articles_from_api(count=10, offset=10, detailType='complete')

# Alternatively you can load the articles from file if you saved them previously using save_articles_to_file
# articles = tagger.get_articles_from_file("20190621.json")
# Generate tags for each article
articles_with_tags = tagger.get_tags_for_articles(articles)

# Save the articles with tags to file. You can use this file to verify it looks good before running the final step to tag the articles.
tagger.save_articles_to_file(today.strftime('%Y%m%d-with-tags.json'), articles_with_tags)

# You can skip this step if you want to do a dry run. Verify the tags in the file we generated in the previous step.
tagger.add_tags_to_articles(articles_with_tags)

Optional overrides
You can override the default thresholds for entity salience and category confidence
thresholds = {
'entity_salience_threshold': 0.7
'category_confidence_threshold': 0.3
}
articles_with_tags = tagger.get_tags_for_articles(articles, thresholds)

Sample
Sample output from running it for my 490 items long Pocket list

X under Entities or Categories denotes the NLP client returned those as potential candidates, but we skipped them because it didn't meet the threshold. You can see the last line Tags: abc, xyz for list of tags pocket-tagger added for each URL.

(1/490) https://www.reddit.com/r/explainlikeimfive/comments/bvweym/eli5_why_do_coffee_drinkers_feel_more_clear/?utm_source=share&utm_medium=ios_app
Title: ELI5: Why do coffee drinkers feel more clear headed after consuming caffeine? Why do some get a headache without it? Does caffeine cause any permanent brain changes and can the brain go back to 'normal' after years of caffeine use? : explainlikeimfive
Description: r/explainlikeimfive: **Explain Like I'm Five is the best forum and archive on the internet for layperson-friendly explanations.**   Don't Panic!
Entities:
X Coffee Drinkers: 0.2438652664422989
X Eli5: 0.14941969513893127
X Caffeine: 0.12065556645393372
X Caffeine: 0.0874909833073616
X Some: 0.06917785853147507
X Headache: 0.0606028214097023
X Brain: 0.03606536239385605
X Explainlikeimfive: 0.033727116882801056
X Brain Changes: 0.03211209550499916
X Caffeine Use: 0.029848895967006683
X R: 0.02966366335749626
X Forum: 0.028598546981811523
X Internet: 0.022404097020626068
X Archive: 0.022404097020626068
X Explainlikeimfive: 0.017647551372647285
X Don'T Panic: 0.009302889928221703
X Five: 0.007013489492237568
X Five: 0.0
Categories:
/Food & Drink/Beverages/Coffee & Tea: 0.6700000166893005
Tags: Food & Drink, Beverages, Coffee & Tea
(2/490) https://www.reddit.com/r/television/comments/bnpwe3/enjoy_three_full_minutes_of_the_cast_of_game_of/?utm_source=share&utm_medium=ios_app
Title: Enjoy three full minutes of the cast of 'Game of Thrones' expressing disappointment in Season 8. : television
Description: r/television:
Entities:
X Cast: 0.31218624114990234
X Disappointment: 0.20341947674751282
X Season: 0.20341947674751282
X Game Of Thrones: 0.13265934586524963
X Television: 0.08712445199489594
X Television: 0.06119102984666824
X 8: 0.0
X Three: 0.0
Categories:
/Arts & Entertainment/TV & Video/TV Shows & Programs: 0.75
Tags: Arts & Entertainment, TV & Video, TV Shows & Programs
(3/490) https://www.reddit.com/r/homeautomation/comments/awvf5r/local_realtime_person_detection_for_rtsp_cameras/
Title: Local realtime person detection for RTSP cameras : homeautomation
Description: r/homeautomation: A subreddit focused on automating your home, housework or household activity. Sensors, switches, cameras, locks, etc. Any …
Entities:
X Realtime Person Detection: 0.3057926297187805
X Homeautomation: 0.15315502882003784
X Cameras: 0.14035314321517944
X Rtsp: 0.07461880147457123
X Homeautomation: 0.051411159336566925
X Home: 0.047811269760131836
X Housework: 0.04366889223456383
X Subreddit: 0.04183248057961464
X R: 0.04132793843746185
X Cameras: 0.032860007137060165
X Locks: 0.028899790719151497
X Household Activity: 0.012798599898815155
X Switches: 0.012735127471387386
X Sensors: 0.012735127471387386
Categories:
/Computers & Electronics: 0.7900000214576721
Tags: Computers & Electronics

References

Pocket API Wrapper for Python
Pocket API Docs
Google Cloud Natural Language Processing
Beautiful Soup
Complete list of content categories from Google Natural Language API