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/
2017110273
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Authored by
sdy
2021-05-10 18:44:59 +0900
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Commit
e3b3bf977573c1cc5e3a59b442b6c7f8c97a1564
e3b3bf97
1 parent
9317f00b
update app.js
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src/App.js
src/App.js
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e3b3bf9
import
React
,
{
Fragment
,
useRef
,
useEffect
,
useState
}
from
"react"
;
import
*
as
tf
from
"@tensorflow/tfjs"
;
import
*
as
qna
from
"@tensorflow-models/qna"
;
import
"react-loader-spinner/dist/loader/css/react-spinner-loader.css"
;
import
Loader
from
"react-loader-spinner"
;
import
React
,
{
Fragment
,
useState
}
from
"react"
;
import
{
Client
}
from
"elasticsearch"
import
"./App.css"
;
import
{
paragraph
}
from
"./passage"
;
function
App
()
{
// setup references, states
//const passageRef = useRef(null);
const
questionRef
=
useRef
(
null
);
const
[
answer
,
setAnswer
]
=
useState
();
const
[
model
,
setModel
]
=
useState
(
null
);
const
[
answer
,
setAnswer
]
=
useState
(
''
);
const
[
inputValue
,
setInputValue
]
=
useState
(
''
);
// load tensorflow models
const
loadModel
=
async
()
=>
{
const
loadedModel
=
await
qna
.
load
();
setModel
(
loadedModel
);
console
.
log
(
"Model is loaded"
);
}
const
client
=
new
Client
({
host
:
'http://localhost:9200'
});
useEffect
(()
=>
{
loadModel
()
},
[]);
const
onKeyUpHandler
=
async
(
event
)
=>
{
if
(
event
.
charCode
===
13
)
{
setInputValue
(
event
.
target
.
value
);
// define question function
const
answerQuestion
=
async
(
e
)
=>
{
if
(
e
.
which
===
13
&&
model
!==
null
)
{
// if key is enter
console
.
log
(
"Question Submitted."
);
const
passage
=
paragraph
;
const
question
=
questionRef
.
current
.
value
;
if
(
inputValue
!==
''
||
inputValue
!==
undefined
||
inputValue
.
length
!==
0
)
{
console
.
log
(
inputValue
);
try
{
const
result
=
await
client
.
search
({
index
:
"wiki-qna2"
,
body
:
{
query
:
{
match
:
{
"질문(원문, 하 난이도)"
:
inputValue
}
}
}
});
const
answers
=
await
model
.
findAnswers
(
question
,
passage
);
setAnswer
(
answers
);
console
.
log
(
answers
);
if
(
result
!==
null
||
result
!==
undefined
||
result
.
length
!==
0
)
{
setAnswer
(
result
.
hits
.
hits
);
answer
.
forEach
(
e
=>
{
console
.
log
(
e
.
_source
.
정답
);
});
}
}
catch
(
error
)
{
console
.
log
(
error
);
}
}
}
}
const
showAnswer
=
(
answer
)
=>
{
return
(
<
div
className
=
"App"
>
<
header
className
=
"App-header"
>
{
model
==
null
?
// if model isn't loaded, react loader spinner will be upload in browser
<
div
>
<
div
>
Model
Loading
<
/div
>
<
Loader
type
=
"Puff"
color
=
"#00BFFF"
height
=
{
100
}
width
=
{
100
}
/
>
<
b
>
{
answer
.
_source
.
정답
}
<
/b
>
<
/div
>
:
// if model is loaded, passage and answer will be shown in browser
)
}
return
(
<
div
className
=
"App"
>
<
header
className
=
"App-header"
>
<
Fragment
>
Ask
a
Question
<
input
ref
=
{
questionRef
}
onKeyPress
=
{
answerQuestion
}
size
=
"80"
/>
Answers
{
answer
?
answer
.
map
((
ans
,
idx
)
=>
<
div
><
b
>
Answer
{
idx
+
1
}
-
<
/b> {ans.text} </
div
>
)
:
" : Can't Find any answers !"
}
<
input
size
=
"80"
onKeyPress
=
{
onKeyUpHandler
}
/
>
<
div
>
--
Answers
--
<
div
>
{
answer
===
''
?
" loading answer..."
:
showAnswer
(
answer
[
0
])}
<
/div
>
<
/div
>
<
/Fragment
>
}
<
/header
>
<
/div
>
);
}
export
default
App
;
/*
//import AppSearchAPIConnector from "@elastic/search-ui-app-search-connector";
import { Client } from "@elastic/elasticsearch";
*/
/*
import {
SearchProvider,
Results,
SearchBox,
PagingInfo,
ResultsPerPage,
Paging,
Facet,
Sorting
} from "@elastic/react-search-ui";
import { Layout } from "@elastic/react-search-ui-views";
*/
/*
import "@elastic/react-search-ui-views/lib/styles/styles.css";
import './App.css';
*/
/*
const connector = new AppSearchAPIConnector({
searchKey: "search-4td7gan5kcasgygjcyexksrz",
engineName: "video-games",
endpointBase: "http://localhost:3002"
});
*/
/*
const client = new Client({
node: "http://localhost:9200"
})
console.log(client);
*/
/*
const configurationOptions = { // search 쿼리와 api 연결선을 작성한 객체
apiConnector: client,
searchQuery: {
search_fields: {
name: {}
},
result_fields: {
name: {
snippet: {
size: 75
}
},
genre: {
snippet: {
size: 50,
fallback: true
}
},
publisher: {
snippet: {
size: 50,
fallback: true
}
},
critic_score: {
raw: {}
},
user_score: {
raw: {}
},
platform: {
snippet: {
size: 50,
fallback: true
}
},
image_url: {
raw: {}
}
},
facets: {
user_score: {
type: "range",
ranges: [
{ from: 0, to: 5, name: "Not good" },
{ from: 5, to: 7, name: "Not bad" },
{ from: 7, to: 9, name: "Pretty good" },
{ from: 9, to: 10, name: "Must play!" }
]
},
critic_score: {
type: "range",
ranges: [
{ from: 0, to: 50, name: "Not good" },
{ from: 50, to: 70, name: "Not bad" },
{ from: 70, to: 90, name: "Pretty good" },
{ from: 90, to: 100, name: "Must play!" }
]
},
genre: { type: "value", size: 100 },
publisher: { type: "value", size: 100},
platform: { type: "value", size: 100}
}
},
autocompleteQuery: {
suggestions: {
types: {
documents: {
fields: ["name"]
}
},
size: 5
}
}
};
*/
/* <SearchProvider config={configurationOptions}>
<div className="App">
<Layout
header={<SearchBox autocompleteSuggestions={true} />}
bodyContent={<Results titleField="name" urlField="image_url"/>}
sideContent={
<div>
<Sorting
label={"Sort by"}
sortOptions={[
{
name: "Relevance",
value: "",
direction: ""
},
{
name: "Name",
value: "name",
direction: "asc"
}
]}
/>
<Facet field="user_score" label="User Score" />
</div>
}
bodyHeader={
<>
<PagingInfo />
<ResultsPerPage />
</>
}
bodyFooter={<Paging />}
/>
</div>
</SearchProvider>
*/
...
...
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