project added
This commit is contained in:
Dawid Kubicki
commit
b55e2a272b
|
|
@ -0,0 +1,68 @@
|
|||
This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
|
||||
|
||||
## Available Scripts
|
||||
|
||||
In the project directory, you can run:
|
||||
|
||||
### `npm start`
|
||||
|
||||
Runs the app in the development mode.<br>
|
||||
Open [http://localhost:3000](http://localhost:3000) to view it in the browser.
|
||||
|
||||
The page will reload if you make edits.<br>
|
||||
You will also see any lint errors in the console.
|
||||
|
||||
### `npm test`
|
||||
|
||||
Launches the test runner in the interactive watch mode.<br>
|
||||
See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
|
||||
|
||||
### `npm run build`
|
||||
|
||||
Builds the app for production to the `build` folder.<br>
|
||||
It correctly bundles React in production mode and optimizes the build for the best performance.
|
||||
|
||||
The build is minified and the filenames include the hashes.<br>
|
||||
Your app is ready to be deployed!
|
||||
|
||||
See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
|
||||
|
||||
### `npm run eject`
|
||||
|
||||
**Note: this is a one-way operation. Once you `eject`, you can’t go back!**
|
||||
|
||||
If you aren’t satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
|
||||
|
||||
Instead, it will copy all the configuration files and the transitive dependencies (Webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own.
|
||||
|
||||
You don’t have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it.
|
||||
|
||||
## Learn More
|
||||
|
||||
You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
|
||||
|
||||
To learn React, check out the [React documentation](https://reactjs.org/).
|
||||
|
||||
### Code Splitting
|
||||
|
||||
This section has moved here: https://facebook.github.io/create-react-app/docs/code-splitting
|
||||
|
||||
### Analyzing the Bundle Size
|
||||
|
||||
This section has moved here: https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size
|
||||
|
||||
### Making a Progressive Web App
|
||||
|
||||
This section has moved here: https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app
|
||||
|
||||
### Advanced Configuration
|
||||
|
||||
This section has moved here: https://facebook.github.io/create-react-app/docs/advanced-configuration
|
||||
|
||||
### Deployment
|
||||
|
||||
This section has moved here: https://facebook.github.io/create-react-app/docs/deployment
|
||||
|
||||
### `npm run build` fails to minify
|
||||
|
||||
This section has moved here: https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
{
|
||||
"name": "gpt-2-frontend",
|
||||
"version": "0.1.0",
|
||||
"dependencies": {
|
||||
"@material-ui/core": "^4.4.2",
|
||||
"axios": "^0.19.0",
|
||||
"node-sass": "^4.12.0",
|
||||
"react": "^16.9.0",
|
||||
"react-dom": "^16.9.0",
|
||||
"react-scripts": "3.1.2"
|
||||
},
|
||||
"scripts": {
|
||||
"start": "react-scripts start",
|
||||
"build": "react-scripts build",
|
||||
"test": "react-scripts test",
|
||||
"eject": "react-scripts eject"
|
||||
},
|
||||
"eslintConfig": {
|
||||
"extends": "react-app"
|
||||
},
|
||||
"browserslist": {
|
||||
"production": [
|
||||
">0.2%",
|
||||
"not dead",
|
||||
"not op_mini all"
|
||||
],
|
||||
"development": [
|
||||
"last 1 chrome version",
|
||||
"last 1 firefox version",
|
||||
"last 1 safari version"
|
||||
]
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
|
|
@ -0,0 +1,43 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<link rel="shortcut icon" href="%PUBLIC_URL%/favicon.ico" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1" />
|
||||
<meta name="theme-color" content="#000000" />
|
||||
<meta
|
||||
name="description"
|
||||
content="Web site created using create-react-app"
|
||||
/>
|
||||
<link rel="apple-touch-icon" href="logo192.png" />
|
||||
<!--
|
||||
manifest.json provides metadata used when your web app is installed on a
|
||||
user's mobile device or desktop. See https://developers.google.com/web/fundamentals/web-app-manifest/
|
||||
-->
|
||||
<link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
|
||||
<!--
|
||||
Notice the use of %PUBLIC_URL% in the tags above.
|
||||
It will be replaced with the URL of the `public` folder during the build.
|
||||
Only files inside the `public` folder can be referenced from the HTML.
|
||||
|
||||
Unlike "/favicon.ico" or "favicon.ico", "%PUBLIC_URL%/favicon.ico" will
|
||||
work correctly both with client-side routing and a non-root public URL.
|
||||
Learn how to configure a non-root public URL by running `npm run build`.
|
||||
-->
|
||||
<title>React App</title>
|
||||
</head>
|
||||
<body>
|
||||
<noscript>You need to enable JavaScript to run this app.</noscript>
|
||||
<div id="root"></div>
|
||||
<!--
|
||||
This HTML file is a template.
|
||||
If you open it directly in the browser, you will see an empty page.
|
||||
|
||||
You can add webfonts, meta tags, or analytics to this file.
|
||||
The build step will place the bundled scripts into the <body> tag.
|
||||
|
||||
To begin the development, run `npm start` or `yarn start`.
|
||||
To create a production bundle, use `npm run build` or `yarn build`.
|
||||
-->
|
||||
</body>
|
||||
</html>
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 8.4 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 22 KiB |
|
|
@ -0,0 +1,25 @@
|
|||
{
|
||||
"short_name": "React App",
|
||||
"name": "Create React App Sample",
|
||||
"icons": [
|
||||
{
|
||||
"src": "favicon.ico",
|
||||
"sizes": "64x64 32x32 24x24 16x16",
|
||||
"type": "image/x-icon"
|
||||
},
|
||||
{
|
||||
"src": "logo192.png",
|
||||
"type": "image/png",
|
||||
"sizes": "192x192"
|
||||
},
|
||||
{
|
||||
"src": "logo512.png",
|
||||
"type": "image/png",
|
||||
"sizes": "512x512"
|
||||
}
|
||||
],
|
||||
"start_url": ".",
|
||||
"display": "standalone",
|
||||
"theme_color": "#000000",
|
||||
"background_color": "#ffffff"
|
||||
}
|
||||
|
|
@ -0,0 +1,2 @@
|
|||
# https://www.robotstxt.org/robotstxt.html
|
||||
User-agent: *
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
.App {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.App-logo {
|
||||
height: 40vmin;
|
||||
}
|
||||
|
||||
.App-header {
|
||||
background-color: #282c34;
|
||||
min-height: 100vh;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
font-size: calc(10px + 2vmin);
|
||||
color: white;
|
||||
}
|
||||
|
||||
.App-link {
|
||||
color: #09d3ac;
|
||||
}
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
import React, { useState } from 'react';
|
||||
import Button from './components/Button';
|
||||
import SelectBox from './components/SelectBox';
|
||||
import TextBox from './components/TextBox';
|
||||
import './styles.scss';
|
||||
import { postGenerateTextEndpoint } from './utils';
|
||||
|
||||
function App() {
|
||||
const [text, setText] = useState("");
|
||||
const [model, setModel] = useState('gpt2');
|
||||
const [generatedText, postGenerateText] = postGenerateTextEndpoint();
|
||||
|
||||
const generateText = () => {
|
||||
postGenerateText({ text, model, userId: 1 });
|
||||
}
|
||||
|
||||
return (
|
||||
<div className='app-container'>
|
||||
<form noValidate autoComplete='off'>
|
||||
<h1>React GPT-2</h1>
|
||||
<SelectBox model={model} setModel={setModel} />
|
||||
<TextBox text={text} setText={setText} />
|
||||
<Button onClick={generateText} />
|
||||
</form>
|
||||
|
||||
{generatedText.pending &&
|
||||
<div className='result pending'>Please wait</div>}
|
||||
|
||||
{generatedText.complete &&
|
||||
(generatedText.error ?
|
||||
<div className='result error'>Bad Request</div> :
|
||||
<div className='result valid'>
|
||||
{generatedText.data.result}
|
||||
</div>)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
export default App;
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
import React from 'react';
|
||||
import ReactDOM from 'react-dom';
|
||||
import App from './App';
|
||||
|
||||
it('renders without crashing', () => {
|
||||
const div = document.createElement('div');
|
||||
ReactDOM.render(<App />, div);
|
||||
ReactDOM.unmountComponentAtNode(div);
|
||||
});
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
import { Button as MaterialButton } from '@material-ui/core';
|
||||
import React from 'react';
|
||||
|
||||
|
||||
const Button = ({ onClick }) => (
|
||||
<MaterialButton
|
||||
style={{ marginTop: '1em', width: 'fit-content' }}
|
||||
onClick={onClick}
|
||||
variant="outlined"
|
||||
color="primary">
|
||||
Generate
|
||||
</MaterialButton>
|
||||
)
|
||||
|
||||
export default Button;
|
||||
|
|
@ -0,0 +1,35 @@
|
|||
import { FormControl, InputLabel, MenuItem, Select } from '@material-ui/core';
|
||||
import React from 'react';
|
||||
|
||||
|
||||
const SelectBox = ({ model, setModel }) => {
|
||||
const inputLabel = React.useRef(null);
|
||||
const [labelWidth, setLabelWidth] = React.useState(0);
|
||||
React.useEffect(() => {
|
||||
setLabelWidth(inputLabel.current.offsetWidth);
|
||||
}, []);
|
||||
|
||||
return (
|
||||
<FormControl
|
||||
margin='normal'
|
||||
style={{ width: '200px' }}
|
||||
variant='outlined'>
|
||||
<InputLabel ref={inputLabel} htmlFor="model-select">Model</InputLabel>
|
||||
<Select
|
||||
value={model}
|
||||
labelWidth={labelWidth}
|
||||
onChange={e => setModel(e.target.value)}
|
||||
inputProps={{
|
||||
name: 'model',
|
||||
id: 'model-select',
|
||||
}}
|
||||
>
|
||||
<MenuItem value={'gpt2'}>Small (117M)</MenuItem>
|
||||
<MenuItem value={'gpt2-medium'}>Medium (345M)</MenuItem>
|
||||
<MenuItem value={'gpt2-large'}>Large (774M)</MenuItem>
|
||||
</Select>
|
||||
</FormControl>
|
||||
)
|
||||
};
|
||||
|
||||
export default SelectBox;
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
import React from 'react';
|
||||
import { TextField } from '@material-ui/core';
|
||||
|
||||
const TextBox = ({ text, setText }) => (
|
||||
<TextField
|
||||
margin='normal'
|
||||
label="Write something..."
|
||||
variant="outlined"
|
||||
fullWidth
|
||||
multiline
|
||||
rows='4'
|
||||
value={text}
|
||||
onChange={e => setText(e.target.value)}
|
||||
/>
|
||||
);
|
||||
|
||||
export default TextBox;
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
body {
|
||||
margin: 0;
|
||||
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Roboto", "Oxygen",
|
||||
"Ubuntu", "Cantarell", "Fira Sans", "Droid Sans", "Helvetica Neue",
|
||||
sans-serif;
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-moz-osx-font-smoothing: grayscale;
|
||||
}
|
||||
|
||||
code {
|
||||
font-family: source-code-pro, Menlo, Monaco, Consolas, "Courier New",
|
||||
monospace;
|
||||
}
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
import React from 'react';
|
||||
import ReactDOM from 'react-dom';
|
||||
import './index.css';
|
||||
import App from './App';
|
||||
import * as serviceWorker from './serviceWorker';
|
||||
|
||||
ReactDOM.render(<App />, document.getElementById('root'));
|
||||
|
||||
// If you want your app to work offline and load faster, you can change
|
||||
// unregister() to register() below. Note this comes with some pitfalls.
|
||||
// Learn more about service workers: https://bit.ly/CRA-PWA
|
||||
serviceWorker.unregister();
|
||||
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 8.0 KiB |
|
|
@ -0,0 +1,2 @@
|
|||
/*! normalize.css v8.0.1 | MIT License | github.com/necolas/normalize.css */html{line-height:1.15;-webkit-text-size-adjust:100%}body{margin:0}main{display:block}h1{font-size:2em;margin:.67em 0}hr{box-sizing:content-box;height:0;overflow:visible}pre{font-family:monospace,monospace;font-size:1em}a{background-color:transparent}abbr[title]{border-bottom:none;text-decoration:underline;text-decoration:underline dotted}b,strong{font-weight:bolder}code,kbd,samp{font-family:monospace,monospace;font-size:1em}small{font-size:80%}sub,sup{font-size:75%;line-height:0;position:relative;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}img{border-style:none}button,input,optgroup,select,textarea{font-family:inherit;font-size:100%;line-height:1.15;margin:0}button,input{overflow:visible}button,select{text-transform:none}[type=button],[type=reset],[type=submit],button{-webkit-appearance:button}[type=button]::-moz-focus-inner,[type=reset]::-moz-focus-inner,[type=submit]::-moz-focus-inner,button::-moz-focus-inner{border-style:none;padding:0}[type=button]:-moz-focusring,[type=reset]:-moz-focusring,[type=submit]:-moz-focusring,button:-moz-focusring{outline:1px dotted ButtonText}fieldset{padding:.35em .75em .625em}legend{box-sizing:border-box;color:inherit;display:table;max-width:100%;padding:0;white-space:normal}progress{vertical-align:baseline}textarea{overflow:auto}[type=checkbox],[type=radio]{box-sizing:border-box;padding:0}[type=number]::-webkit-inner-spin-button,[type=number]::-webkit-outer-spin-button{height:auto}[type=search]{-webkit-appearance:textfield;outline-offset:-2px}[type=search]::-webkit-search-decoration{-webkit-appearance:none}::-webkit-file-upload-button{-webkit-appearance:button;font:inherit}details{display:block}summary{display:list-item}template{display:none}[hidden]{display:none}
|
||||
/*# sourceMappingURL=normalize.min.css.map */
|
||||
|
|
@ -0,0 +1,135 @@
|
|||
// This optional code is used to register a service worker.
|
||||
// register() is not called by default.
|
||||
|
||||
// This lets the app load faster on subsequent visits in production, and gives
|
||||
// it offline capabilities. However, it also means that developers (and users)
|
||||
// will only see deployed updates on subsequent visits to a page, after all the
|
||||
// existing tabs open on the page have been closed, since previously cached
|
||||
// resources are updated in the background.
|
||||
|
||||
// To learn more about the benefits of this model and instructions on how to
|
||||
// opt-in, read https://bit.ly/CRA-PWA
|
||||
|
||||
const isLocalhost = Boolean(
|
||||
window.location.hostname === 'localhost' ||
|
||||
// [::1] is the IPv6 localhost address.
|
||||
window.location.hostname === '[::1]' ||
|
||||
// 127.0.0.1/8 is considered localhost for IPv4.
|
||||
window.location.hostname.match(
|
||||
/^127(?:\.(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)){3}$/
|
||||
)
|
||||
);
|
||||
|
||||
export function register(config) {
|
||||
if (process.env.NODE_ENV === 'production' && 'serviceWorker' in navigator) {
|
||||
// The URL constructor is available in all browsers that support SW.
|
||||
const publicUrl = new URL(process.env.PUBLIC_URL, window.location.href);
|
||||
if (publicUrl.origin !== window.location.origin) {
|
||||
// Our service worker won't work if PUBLIC_URL is on a different origin
|
||||
// from what our page is served on. This might happen if a CDN is used to
|
||||
// serve assets; see https://github.com/facebook/create-react-app/issues/2374
|
||||
return;
|
||||
}
|
||||
|
||||
window.addEventListener('load', () => {
|
||||
const swUrl = `${process.env.PUBLIC_URL}/service-worker.js`;
|
||||
|
||||
if (isLocalhost) {
|
||||
// This is running on localhost. Let's check if a service worker still exists or not.
|
||||
checkValidServiceWorker(swUrl, config);
|
||||
|
||||
// Add some additional logging to localhost, pointing developers to the
|
||||
// service worker/PWA documentation.
|
||||
navigator.serviceWorker.ready.then(() => {
|
||||
console.log(
|
||||
'This web app is being served cache-first by a service ' +
|
||||
'worker. To learn more, visit https://bit.ly/CRA-PWA'
|
||||
);
|
||||
});
|
||||
} else {
|
||||
// Is not localhost. Just register service worker
|
||||
registerValidSW(swUrl, config);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
function registerValidSW(swUrl, config) {
|
||||
navigator.serviceWorker
|
||||
.register(swUrl)
|
||||
.then(registration => {
|
||||
registration.onupdatefound = () => {
|
||||
const installingWorker = registration.installing;
|
||||
if (installingWorker == null) {
|
||||
return;
|
||||
}
|
||||
installingWorker.onstatechange = () => {
|
||||
if (installingWorker.state === 'installed') {
|
||||
if (navigator.serviceWorker.controller) {
|
||||
// At this point, the updated precached content has been fetched,
|
||||
// but the previous service worker will still serve the older
|
||||
// content until all client tabs are closed.
|
||||
console.log(
|
||||
'New content is available and will be used when all ' +
|
||||
'tabs for this page are closed. See https://bit.ly/CRA-PWA.'
|
||||
);
|
||||
|
||||
// Execute callback
|
||||
if (config && config.onUpdate) {
|
||||
config.onUpdate(registration);
|
||||
}
|
||||
} else {
|
||||
// At this point, everything has been precached.
|
||||
// It's the perfect time to display a
|
||||
// "Content is cached for offline use." message.
|
||||
console.log('Content is cached for offline use.');
|
||||
|
||||
// Execute callback
|
||||
if (config && config.onSuccess) {
|
||||
config.onSuccess(registration);
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
};
|
||||
})
|
||||
.catch(error => {
|
||||
console.error('Error during service worker registration:', error);
|
||||
});
|
||||
}
|
||||
|
||||
function checkValidServiceWorker(swUrl, config) {
|
||||
// Check if the service worker can be found. If it can't reload the page.
|
||||
fetch(swUrl)
|
||||
.then(response => {
|
||||
// Ensure service worker exists, and that we really are getting a JS file.
|
||||
const contentType = response.headers.get('content-type');
|
||||
if (
|
||||
response.status === 404 ||
|
||||
(contentType != null && contentType.indexOf('javascript') === -1)
|
||||
) {
|
||||
// No service worker found. Probably a different app. Reload the page.
|
||||
navigator.serviceWorker.ready.then(registration => {
|
||||
registration.unregister().then(() => {
|
||||
window.location.reload();
|
||||
});
|
||||
});
|
||||
} else {
|
||||
// Service worker found. Proceed as normal.
|
||||
registerValidSW(swUrl, config);
|
||||
}
|
||||
})
|
||||
.catch(() => {
|
||||
console.log(
|
||||
'No internet connection found. App is running in offline mode.'
|
||||
);
|
||||
});
|
||||
}
|
||||
|
||||
export function unregister() {
|
||||
if ('serviceWorker' in navigator) {
|
||||
navigator.serviceWorker.ready.then(registration => {
|
||||
registration.unregister();
|
||||
});
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
@import url("https://fonts.googleapis.com/css?family=IBM+Plex+Mono&display=swap");
|
||||
@import "./normalize.css";
|
||||
|
||||
* {
|
||||
font-family: "IBM Plex Mono", monospace !important;
|
||||
}
|
||||
|
||||
html,
|
||||
body,
|
||||
#root {
|
||||
margin: 0 !important;
|
||||
padding: 0 !important;
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
color: rgba(0, 0, 0, 0.8);
|
||||
}
|
||||
|
||||
.app-container {
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
flex-direction: column;
|
||||
}
|
||||
|
||||
form {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
width: 50%;
|
||||
}
|
||||
|
||||
.result {
|
||||
border-radius: 4px;
|
||||
padding: 2em;
|
||||
width: calc(50% - 4em);
|
||||
margin-top: 2em;
|
||||
line-height: 27px;
|
||||
}
|
||||
|
||||
.pending {
|
||||
border: 1px solid #ffeb3b;
|
||||
background-color: #fffde7;
|
||||
}
|
||||
|
||||
.valid {
|
||||
background-color: #e1f5fe;
|
||||
border: 1px solid #03a9f4;
|
||||
}
|
||||
|
||||
.error {
|
||||
background-color: #ffebee;
|
||||
color: #f44336;
|
||||
}
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
import React from 'react';
|
||||
import axios from 'axios';
|
||||
|
||||
const ENDPOINT_URL = "http://localhost:5000/generate";
|
||||
|
||||
const useAsyncEndpoint = fn => {
|
||||
const [res, setRes] = React.useState({ data: null, complete: false, pending: false, error: false });
|
||||
const [req, setReq] = React.useState();
|
||||
|
||||
React.useEffect(() => {
|
||||
if (!req) return;
|
||||
setRes({ data: null, pending: true, error: false, complete: false });
|
||||
axios(req)
|
||||
.then(res =>
|
||||
setRes({ data: res.data, pending: false, error: false, complete: true })
|
||||
)
|
||||
.catch(() =>
|
||||
setRes({ data: null, pending: false, error: true, complete: true })
|
||||
);
|
||||
}, [req]);
|
||||
|
||||
return [res, (...args) => setReq(fn(...args))];
|
||||
}
|
||||
|
||||
export const postGenerateTextEndpoint = () => {
|
||||
/* eslint-disable react-hooks/rules-of-hooks */
|
||||
return useAsyncEndpoint(data => ({ url: ENDPOINT_URL, method: "POST", data }));
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
|
|
@ -0,0 +1,33 @@
|
|||
#!/usr/local/bin/python3
|
||||
|
||||
from flask import Flask, abort, jsonify, request
|
||||
from flask_cors import CORS, cross_origin
|
||||
|
||||
|
||||
from run_generation import generate_text
|
||||
|
||||
app = Flask(__name__)
|
||||
cors = CORS(app)
|
||||
app.config['CORS_HEADERS'] = 'Content-Type'
|
||||
app.run()
|
||||
|
||||
|
||||
@app.route("/generate", methods=['POS'])
|
||||
@cross_origin()
|
||||
def get_gen():
|
||||
data = request.get_json()
|
||||
|
||||
if 'text' not in data or len(data['text']) == 0 or 'model' not in data:
|
||||
abort(400)
|
||||
else:
|
||||
text = data['text']
|
||||
model = data['model']
|
||||
|
||||
result = generate_text(
|
||||
model_type='gpt2',
|
||||
length=100,
|
||||
prompt=text,
|
||||
model_name_or_path=model
|
||||
)
|
||||
|
||||
return jsonify({'result': result})
|
||||
|
|
@ -0,0 +1,195 @@
|
|||
#!/usr/bin/env python3
|
||||
# coding=utf-8
|
||||
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
|
||||
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" Conditional text generation with the auto-regressive models of the library (GPT/GPT-2/Transformer-XL/XLNet)
|
||||
"""
|
||||
from __future__ import absolute_import, division, print_function, unicode_literals
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
from tqdm import trange
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import numpy as np
|
||||
|
||||
from pytorch_transformers import GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig
|
||||
|
||||
from pytorch_transformers import GPT2LMHeadModel, GPT2Tokenizer
|
||||
from pytorch_transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer
|
||||
from pytorch_transformers import XLNetLMHeadModel, XLNetTokenizer
|
||||
from pytorch_transformers import TransfoXLLMHeadModel, TransfoXLTokenizer
|
||||
|
||||
|
||||
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
|
||||
datefmt='%m/%d/%Y %H:%M:%S',
|
||||
level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MAX_LENGTH = int(10000) # Hardcoded max length to avoid infinite loop
|
||||
|
||||
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (
|
||||
GPT2Config, OpenAIGPTConfig, XLNetConfig, TransfoXLConfig)), ())
|
||||
|
||||
MODEL_CLASSES = {
|
||||
'gpt2': (GPT2LMHeadModel, GPT2Tokenizer),
|
||||
'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
|
||||
'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
|
||||
'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
|
||||
}
|
||||
|
||||
# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
|
||||
# in https://github.com/rusiaaman/XLNet-gen#methodology
|
||||
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
|
||||
PADDING_TEXT = """ In 1991, the remains of Russian Tsar Nicholas II and his family
|
||||
(except for Alexei and Maria) are discovered.
|
||||
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
|
||||
remainder of the story. 1883 Western Siberia,
|
||||
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
|
||||
Rasputin has a vision and denounces one of the men as a horse thief. Although his
|
||||
father initially slaps him for making such an accusation, Rasputin watches as the
|
||||
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
|
||||
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
|
||||
with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
|
||||
|
||||
|
||||
def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
|
||||
""" Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
|
||||
Args:
|
||||
logits: logits distribution shape (vocabulary size)
|
||||
top_k > 0: keep only top k tokens with highest probability (top-k filtering).
|
||||
top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
|
||||
Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
|
||||
From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
|
||||
"""
|
||||
assert logits.dim() == 1 # batch size 1 for now - could be updated for more but the code would be less clear
|
||||
top_k = min(top_k, logits.size(-1)) # Safety check
|
||||
if top_k > 0:
|
||||
# Remove all tokens with a probability less than the last token of the top-k
|
||||
indices_to_remove = logits < torch.topk(logits, top_k)[
|
||||
0][..., -1, None]
|
||||
logits[indices_to_remove] = filter_value
|
||||
|
||||
if top_p > 0.0:
|
||||
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
|
||||
cumulative_probs = torch.cumsum(
|
||||
F.softmax(sorted_logits, dim=-1), dim=-1)
|
||||
|
||||
# Remove tokens with cumulative probability above the threshold
|
||||
sorted_indices_to_remove = cumulative_probs > top_p
|
||||
# Shift the indices to the right to keep also the first token above the threshold
|
||||
sorted_indices_to_remove[...,
|
||||
1:] = sorted_indices_to_remove[..., :-1].clone()
|
||||
sorted_indices_to_remove[..., 0] = 0
|
||||
|
||||
indices_to_remove = sorted_indices[sorted_indices_to_remove]
|
||||
logits[indices_to_remove] = filter_value
|
||||
return logits
|
||||
|
||||
|
||||
def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, is_xlnet=False, device='cpu'):
|
||||
context = torch.tensor(context, dtype=torch.long, device=device)
|
||||
context = context.unsqueeze(0).repeat(num_samples, 1)
|
||||
generated = context
|
||||
with torch.no_grad():
|
||||
for _ in trange(length):
|
||||
|
||||
inputs = {'input_ids': generated}
|
||||
if is_xlnet:
|
||||
# XLNet is a direct (predict same token, not next token) and bi-directional model by default
|
||||
# => need one additional dummy token in the input (will be masked), attention mask and target mapping (see model docstring)
|
||||
input_ids = torch.cat((generated, torch.zeros(
|
||||
(1, 1), dtype=torch.long, device=device)), dim=1)
|
||||
perm_mask = torch.zeros(
|
||||
(1, input_ids.shape[1], input_ids.shape[1]), dtype=torch.float, device=device)
|
||||
# Previous tokens don't see last token
|
||||
perm_mask[:, :, -1] = 1.0
|
||||
target_mapping = torch.zeros(
|
||||
(1, 1, input_ids.shape[1]), dtype=torch.float, device=device)
|
||||
target_mapping[0, 0, -1] = 1.0 # predict last token
|
||||
inputs = {'input_ids': input_ids, 'perm_mask': perm_mask,
|
||||
'target_mapping': target_mapping}
|
||||
|
||||
# Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states)
|
||||
outputs = model(**inputs)
|
||||
next_token_logits = outputs[0][0, -1, :] / temperature
|
||||
filtered_logits = top_k_top_p_filtering(
|
||||
next_token_logits, top_k=top_k, top_p=top_p)
|
||||
next_token = torch.multinomial(
|
||||
F.softmax(filtered_logits, dim=-1), num_samples=1)
|
||||
generated = torch.cat((generated, next_token.unsqueeze(0)), dim=1)
|
||||
return generated
|
||||
|
||||
|
||||
def generate_text(
|
||||
padding_text=None,
|
||||
model_type='gpt2',
|
||||
model_name_or_path='gpt2',
|
||||
prompt='',
|
||||
length=20,
|
||||
temperature=1.0,
|
||||
top_k=0,
|
||||
top_p=0.9,
|
||||
no_cuda=True,
|
||||
seed=42,
|
||||
):
|
||||
device = torch.device(
|
||||
"cuda" if torch.cuda.is_available() and not no_cuda else "cpu")
|
||||
n_gpu = torch.cuda.device_count()
|
||||
|
||||
np.random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
if n_gpu > 0:
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
|
||||
model_type = model_type.lower()
|
||||
model_class, tokenizer_class = MODEL_CLASSES[model_type]
|
||||
tokenizer = tokenizer_class.from_pretrained(model_name_or_path)
|
||||
model = model_class.from_pretrained(model_name_or_path)
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
if length < 0 and model.config.max_position_embeddings > 0:
|
||||
length = model.config.max_position_embeddings
|
||||
elif 0 < model.config.max_position_embeddings < length:
|
||||
# No generation bigger than model size
|
||||
length = model.config.max_position_embeddings
|
||||
elif length < 0:
|
||||
length = MAX_LENGTH # avoid infinite loop
|
||||
|
||||
while True:
|
||||
raw_text = prompt if prompt else input("Model prompt >>> ")
|
||||
if model_type in ["transfo-xl", "xlnet"]:
|
||||
# Models with memory likes to have a long prompt for short inputs.
|
||||
raw_text = (
|
||||
padding_text if padding_text else PADDING_TEXT) + raw_text
|
||||
context_tokens = tokenizer.encode(raw_text)
|
||||
out = sample_sequence(
|
||||
model=model,
|
||||
context=context_tokens,
|
||||
length=length,
|
||||
temperature=temperature,
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
device=device,
|
||||
is_xlnet=bool(model_type == "xlnet"),
|
||||
)
|
||||
out = out[0, len(context_tokens):].tolist()
|
||||
text = tokenizer.decode(out, clean_up_tokenization_spaces=True)
|
||||
print(text)
|
||||
if prompt:
|
||||
break
|
||||
return text
|
||||
Loading…
Reference in New Issue