https://github.com/frederickroman/readgauge
Reading ease scoring web app. It calculates the US school reading level of a text client-side using neural networks.
https://github.com/frederickroman/readgauge
pwa reading-comprehension tensorflow tensorflow-node tensorflow2 tensorflowjs text-classification text-processing web-workers
Last synced: 2 months ago
JSON representation
Reading ease scoring web app. It calculates the US school reading level of a text client-side using neural networks.
- Host: GitHub
- URL: https://github.com/frederickroman/readgauge
- Owner: FrederickRoman
- License: mit
- Created: 2021-11-15T06:22:16.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-02-18T05:53:53.000Z (over 4 years ago)
- Last Synced: 2025-10-25T07:25:21.509Z (8 months ago)
- Topics: pwa, reading-comprehension, tensorflow, tensorflow-node, tensorflow2, tensorflowjs, text-classification, text-processing, web-workers
- Language: TypeScript
- Homepage: https://readscale.netlify.app
- Size: 2.81 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Readguage
### AI-powered reading scoring app.
#### It calculates the US school reading level of a text.
#### It uses Neuro-Flesch which is a neural version of the flesch scoring system created for this app.
#### It's private because all calculations get done in-brower and nothing gets ever sent to a server.
## Live website
See [Readgauge website](https://readscale.netlify.app).
## Main libraries used in this project
- React.js (TS)
- Tensorflow
- - TS - to run the neural network model client-side
- - Node - for model design (research models in python in [other repo](https://github.com/FrederickRoman/syllable-count-predictor); rewritten chosen model in node in this repo under [/ml](https://github.com/FrederickRoman/Readgauge/tree/main/ml))
- MUI
- Chart.js
- moo.js - to tokenize the input text client-side.
## Project setup
```
yarn install
```
### Compiles and hot-reloads for client development
```
yarn start
```
### Compiles and minifies for production
```
yarn build
```
### Launches the test runner in the interactive watch mode.
It runs unit tests for the main elements, and integration tests for scoring test cases.
```
yarn test
```
### Retrains the neural network
It reruns the entire process from data cleaning trough training to evaluation of the model.
```
node /ml/train
```