sist2/sist2-vue/src/components/LazyContentDiv.vue
2023-07-24 19:36:20 -04:00

167 lines
5.6 KiB
Vue

<template>
<Preloader v-if="loading"></Preloader>
<div v-else-if="content">
<b-form inline class="my-2" v-if="ModelsRepo.getOptions().length > 0">
<b-checkbox class="ml-auto mr-2" :checked="optAutoAnalyze"
@input="setOptAutoAnalyze($event); $store.dispatch('updateConfiguration')">
{{ $t("ml.auto") }}
</b-checkbox>
<b-button :disabled="mlPredictionsLoading || mlLoading" @click="mlAnalyze" variant="primary"
>{{ $t("ml.analyzeText") }}
</b-button>
<b-select :disabled="mlPredictionsLoading || mlLoading" class="ml-2" v-model="nerModel">
<b-select-option :value="opt.value" v-for="opt of ModelsRepo.getOptions()">{{ opt.text }}
</b-select-option>
</b-select>
</b-form>
<b-progress v-if="mlLoading" variant="warning" show-progress :max="1" class="mb-3"
>
<b-progress-bar :value="modelLoadingProgress">
<strong>{{ ((modelLoadingProgress * modelSize) / (1024 * 1024)).toFixed(1) }}MB / {{
(modelSize / (1024 * 1024)).toFixed(1)
}}MB</strong>
</b-progress-bar>
</b-progress>
<b-progress v-if="mlPredictionsLoading" variant="primary" :value="modelPredictionProgress"
:max="content.length" class="mb-3"></b-progress>
<AnalyzedContentSpansContainer v-if="analyzedContentSpans.length > 0"
:spans="analyzedContentSpans" :text="rawContent"></AnalyzedContentSpansContainer>
<div v-else class="content-div" v-html="content"></div>
</div>
</template>
<script>
import Sist2Api from "@/Sist2Api";
import Preloader from "@/components/Preloader";
import Sist2Query from "@/Sist2ElasticsearchQuery";
import store from "@/store";
import BertNerModel from "@/ml/BertNerModel";
import AnalyzedContentSpansContainer from "@/components/AnalyzedContentSpanContainer.vue";
import ModelsRepo from "@/ml/modelsRepo";
import {mapGetters, mapMutations} from "vuex";
export default {
name: "LazyContentDiv",
components: {AnalyzedContentSpansContainer, Preloader},
props: ["docId"],
data() {
return {
ModelsRepo,
content: "",
rawContent: "",
loading: true,
modelLoadingProgress: 0,
modelPredictionProgress: 0,
mlPredictionsLoading: false,
mlLoading: false,
nerModel: null,
analyzedContentSpans: []
}
},
mounted() {
if (this.$store.getters.optMlDefaultModel) {
this.nerModel = this.$store.getters.optMlDefaultModel
} else {
this.nerModel = ModelsRepo.getDefaultModel();
}
Sist2Api
.getDocument(this.docId, this.$store.state.optHighlight, this.$store.state.fuzzy)
.then(doc => {
this.loading = false;
if (doc) {
this.content = this.getContent(doc)
}
if (this.optAutoAnalyze) {
this.mlAnalyze();
}
});
},
computed: {
...mapGetters(["optAutoAnalyze"]),
modelSize() {
const modelData = ModelsRepo.data[this.nerModel];
if (!modelData) {
return 0;
}
return modelData.size;
}
},
methods: {
...mapMutations(["setOptAutoAnalyze"]),
getContent(doc) {
this.rawContent = doc._source.content;
if (!doc.highlight) {
return doc._source.content;
}
if (doc.highlight["content.nGram"]) {
return doc.highlight["content.nGram"][0];
}
if (doc.highlight.content) {
return doc.highlight.content[0];
}
},
async getMlModel() {
if (this.$store.getters.nerModel.name !== this.nerModel) {
this.mlLoading = true;
this.modelLoadingProgress = 0;
const modelInfo = ModelsRepo.data[this.nerModel];
const model = new BertNerModel(
modelInfo.vocabUrl,
modelInfo.modelUrl,
modelInfo.id2label,
)
await model.init(progress => this.modelLoadingProgress = progress);
this.$store.commit("setNerModel", {model, name: this.nerModel});
this.mlLoading = false;
return model
}
return this.$store.getters.nerModel.model;
},
async mlAnalyze() {
if (!this.content) {
return;
}
const modelInfo = ModelsRepo.data[this.nerModel];
if (modelInfo === undefined) {
return;
}
this.$store.commit("setOptMlDefaultModel", this.nerModel);
await this.$store.dispatch("updateConfiguration");
const model = await this.getMlModel();
this.analyzedContentSpans = [];
this.mlPredictionsLoading = true;
await model.predict(this.rawContent, results => {
results.forEach(result => result.label = modelInfo.humanLabels[result.label]);
this.analyzedContentSpans.push(...results);
this.modelPredictionProgress = results[results.length - 1].wordIndex;
});
this.mlPredictionsLoading = false;
}
}
}
</script>
<style>
.progress-bar {
transition: none;
}
</style>