If you're like me and use Twitter to keep up with researchers, blog posts and members of the Data Science & ML community in general, you might find yourself liking tweets to read later or for when you need a post when you're working on a relevant problem.
Well then you'll also know, the number of likes start to grow quickly and searching/filtering through all the likes to find the relevant likes you need, when you need them, is a pain. Hence, the use case for this quick app. Due to the simplicity of this app, I decided to build it with quickly with Streamlit.
- Generate topics from tweets
- Filter based on tweet topic
- Regex search
- Click directly on links to blogs, links, pdfs, etc.
You will need an ElasticSearch instance with all your twitter data.
To edit the number of times between api requests to twitter, change the REQUEST_TIME_LIMIT variable (default is 60min).
Add your twitter api keys in the source code or as environment variables and running:
pip install -r requirements.txt
streamlit run toot/toot.pyTo background the process nohup streamlit run toot/toot.py & or a schtask on Windows.
Then access it by going to http://localhost:8501.
I'll update this repo periodically and will add the following features:
- Reduced loading time
- Topic Filtering
- Add account of tweet that is favourited
- Image on Dockerhub