https://github.com/diop/kebetu
A Markov Chain generator for "believable" African proverbs.
https://github.com/diop/kebetu
flask heroku python
Last synced: about 1 month ago
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A Markov Chain generator for "believable" African proverbs.
- Host: GitHub
- URL: https://github.com/diop/kebetu
- Owner: diop
- Created: 2019-02-06T22:27:02.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-02-02T04:42:59.000Z (over 3 years ago)
- Last Synced: 2025-03-18T09:15:22.265Z (over 1 year ago)
- Topics: flask, heroku, python
- Language: Python
- Homepage: https://proverbs-generator.herokuapp.com/
- Size: 5.52 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 3
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### `Believable African Proverbs Generator`
Make School CS 1.2: How Data Structures Work - Winter 2019
### `Live Web App`
[https://proverbs-generator.herokuapp.com/](https://proverbs-generator.herokuapp.com/)
### `Project Sprints`
* Project goals:
+ [ ] Use a Python script to randomly generate words from a dictionary.
+ [ ] Build sentences by sampling these words using a Markov language model.
+ [ ] Implement grammar rules parsed from the text of a large document set.
+ [ ] Build data structures including linked lists, hash tables, stacks, queues, and heaps to store the words and sentences.
+ [ ] Analyze the inner workings and performance tradeoffs of each data structure.
+ [ ] Deploy your language model to a Flask web server on Heroku and connect it to Twitter to let users tweet their favorite results.
* Learning outcomes:
+ [ ] Master Python data structures and data processing algorithms
+ [ ] Implement data structures including linked lists, hash tables, stacks, queues and heaps.
+ [ ] Parse text documents and store information in data structures
+ [ ] Implement sampling methods and Markov chains on data sets
© Copyright 2019 Fodé Diop - MIT License