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120 lines
3.4 KiB
Dart
120 lines
3.4 KiB
Dart
import 'dart:convert';
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import 'package:fluffychat/pangea/config/environment.dart';
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import 'package:fluffychat/pangea/controllers/language_detection_controller.dart';
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import 'package:fluffychat/pangea/models/language_detection_model.dart';
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import 'package:fluffychat/pangea/models/lemma.dart';
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import 'package:fluffychat/pangea/models/pangea_match_model.dart';
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import 'package:fluffychat/pangea/models/pangea_token_model.dart';
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import 'package:fluffychat/pangea/repo/span_data_repo.dart';
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import 'package:http/http.dart';
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import '../constants/model_keys.dart';
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import '../models/igc_text_data_model.dart';
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import '../network/requests.dart';
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import '../network/urls.dart';
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class IgcRepo {
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static Future<IGCTextData> getIGC(
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String? accessToken, {
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required IGCRequestBody igcRequest,
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}) async {
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final Requests req = Requests(
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accessToken: accessToken,
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choreoApiKey: Environment.choreoApiKey,
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);
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final Response res = await req.post(
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url: PApiUrls.igcLite,
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body: igcRequest.toJson(),
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);
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final Map<String, dynamic> json =
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jsonDecode(utf8.decode(res.bodyBytes).toString());
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final IGCTextData response = IGCTextData.fromJson(json);
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return response;
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}
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static Future<IGCTextData> getMockData() async {
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await Future.delayed(const Duration(seconds: 2));
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final IGCTextData igcTextData = IGCTextData(
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detections: LanguageDetectionResponse(
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detections: [LanguageDetection(langCode: "en", confidence: 0.99)],
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fullText: "This be a sample text",
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),
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tokens: [
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PangeaToken(
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text: PangeaTokenText(content: "This", offset: 0, length: 4),
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lemma: Lemma(form: "This", text: "this", saveVocab: true),
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pos: "DET",
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morph: {},
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),
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PangeaToken(
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text: PangeaTokenText(content: "be", offset: 5, length: 2),
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lemma: Lemma(form: "be", text: "be", saveVocab: true),
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pos: "VERB",
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morph: {},
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),
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PangeaToken(
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text: PangeaTokenText(content: "a", offset: 8, length: 1),
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lemma: Lemma(form: "a", text: "a", saveVocab: true),
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pos: "DET",
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morph: {},
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),
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PangeaToken(
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text: PangeaTokenText(content: "sample", offset: 10, length: 6),
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lemma: Lemma(form: "sample", text: "sample", saveVocab: true),
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pos: "NOUN",
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morph: {},
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),
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PangeaToken(
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text: PangeaTokenText(content: "text", offset: 17, length: 4),
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lemma: Lemma(form: "text", text: "text", saveVocab: true),
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pos: "NOUN",
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morph: {},
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),
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],
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matches: [
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PangeaMatch(
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match: spanDataRepomockSpan,
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status: PangeaMatchStatus.open,
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),
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],
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originalInput: "This be a sample text",
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fullTextCorrection: "This is a sample text",
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userL1: "es",
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userL2: "en",
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enableIT: true,
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enableIGC: true,
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);
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return igcTextData;
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}
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}
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class IGCRequestBody {
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String fullText;
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String userL1;
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String userL2;
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bool enableIT;
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bool enableIGC;
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IGCRequestBody({
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required this.fullText,
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required this.userL1,
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required this.userL2,
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required this.enableIGC,
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required this.enableIT,
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});
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Map<String, dynamic> toJson() => {
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ModelKey.fullText: fullText,
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ModelKey.userL1: userL1,
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ModelKey.userL2: userL2,
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"enable_it": enableIT,
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"enable_igc": enableIGC,
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};
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}
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