//import { Doc } from "../new_fields/Doc";
//import { StrCast } from "../new_fields/Types";
//import { List } from "../new_fields/List";
//import { CognitiveServices } from "../client/cognitive_services/CognitiveServices";

// var w2v = require('word2vec');
var assert = require('assert');
var arxivapi = require('arxiv-api-node');
import requestPromise = require("request-promise");
import * as use from '@tensorflow-models/universal-sentence-encoder';
import { Tensor } from "@tensorflow/tfjs-core/dist/tensor";
require('@tensorflow/tfjs-node');

//http://gnuwin32.sourceforge.net/packages/make.htm

export class Recommender {

    private _model: any;
    static Instance: Recommender;
    private dimension: number = 0;
    private choice: string = ""; // Tensorflow or Word2Vec

    constructor() {
        console.log("creating recommender...");
        Recommender.Instance = this;
    }

    /***
     * Loads pre-trained model from TF
     */

    public async loadTFModel() {
        let self = this;
        return new Promise(res => {
            use.load().then(model => {
                self.choice = "TF";
                self._model = model;
                self.dimension = 512;
                res(model);
            });
        }

        );
    }

    /***
     * Loads pre-trained model from word2vec
     */

    // private loadModel(): Promise<any> {
    //     let self = this;
    //     return new Promise(res => {
    //         w2v.loadModel("./node_modules/word2vec/examples/fixtures/vectors.txt", function (err: any, model: any) {
    //             self.choice = "WV";
    //             self._model = model;
    //             self.dimension = model.size;
    //             res(model);
    //         });
    //     });
    // }

    /***
     * Testing
     */

    public async testModel() {
        if (!this._model) {
            await this.loadTFModel();
        }
        if (this._model) {
            if (this.choice === "WV") {
                let similarity = this._model.similarity('father', 'mother');
                console.log(similarity);
            }
            else if (this.choice === "TF") {
                const model = this._model as use.UniversalSentenceEncoder;
                // Embed an array of sentences.
                const sentences = [
                    'Hello.',
                    'How are you?'
                ];
                const embeddings = await this.vectorize(sentences);
                if (embeddings) embeddings.print(true /*verbose*/);
                // model.embed(sentences).then(embeddings => {
                //     // `embeddings` is a 2D tensor consisting of the 512-dimensional embeddings for each sentence.
                //     // So in this example `embeddings` has the shape [2, 512].
                //     embeddings.print(true /* verbose */);
                // });
            }
        }
        else {
            console.log("model not found :(");
        }
    }

    /***
     * Uses model to convert words to vectors
     */

    public async vectorize(text: string[]): Promise<Tensor | undefined> {
        if (!this._model) {
            await this.loadTFModel();
        }
        if (this._model) {
            if (this.choice === "WV") {
                let word_vecs = this._model.getVectors(text);
                return word_vecs;
            }
            else if (this.choice === "TF") {
                const model = this._model as use.UniversalSentenceEncoder;
                return new Promise<Tensor>(res => {
                    model.embed(text).then(embeddings => {
                        res(embeddings);
                    });
                });

            }
        }
    }

    // public async trainModel() {
    //     console.log("phrasing...");
    //     w2v.word2vec("./node_modules/word2vec/examples/eng_news-typical_2016_1M-sentences.txt", './node_modules/word2vec/examples/my_phrases.txt', {
    //         cbow: 1,
    //         size: 200,
    //         window: 8,
    //         negative: 25,
    //         hs: 0,
    //         sample: 1e-4,
    //         threads: 20,
    //         iter: 200,
    //         minCount: 2
    //     });
    //     console.log("phrased!!!");
    // }

}