https://github.com/ashton-sidhu/toot

Twitter like filter built with Streamlit
https://github.com/ashton-sidhu/toot

automation datascience streamlit toolkit twitter

Last synced: about 21 hours ago
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Twitter like filter built with Streamlit

• Host: GitHub
• URL: https://github.com/ashton-sidhu/toot
• Owner: Ashton-Sidhu
• License: mit
• Created: 2019-11-11T01:49:50.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-12-09T05:28:07.000Z (almost 2 years ago)
• Last Synced: 2023-03-02T22:16:21.487Z (over 1 year ago)
• Topics: automation, datascience, streamlit, toolkit, twitter
• Language: Python
• Size: 268 KB
• Stars: 8
• Watchers: 0
• Forks: 1
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
Toot

---

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.

![image](https://user-images.githubusercontent.com/9558507/71452195-7874aa00-2750-11ea-81f8-51153e593eb3.png)

Features

--------

- Generate topics from tweets

- Filter based on tweet topic

- Regex search

- Click directly on links to blogs, links, pdfs, etc.

Usage

---

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).

### Locally

Add your twitter api keys in the source code or as environment variables and running:

```bash

pip install -r requirements.txt

streamlit run toot/toot.py

```

To background the process `nohup streamlit run toot/toot.py &` or a schtask on Windows.

Then access it by going to http://localhost:8501.

Coming Soon

---

I'll update this repo periodically and will add the following features:

- [x] Reduced loading time

- [x] Topic Filtering

- [x] Add account of tweet that is favourited

- [x] Image on Dockerhub