在SAP Kyma的Lambda Function里调用SAP Leonardo service

module.exports = { main: async function (event, context) {
    const request = require('request-promise-native');

    if (!event.image && !event.extensions && !event.extensions.request && !event.extensions.request.query && !event.extensions.request.query.image) {
        return "Parameter missing:\nimage: image url (mandatory)\n";
    }
    const query = event.extensions.request.query;
    const image = query.image;

    //-------- Leonardo MLF Find Similarity ------------
    const lmlf_auth = Buffer.from(process.env.LMLF_CLIENT_ID + ':' + process.env.LMLF_CLIENT_SECRET).toString('base64');
    
    const tokenResp = await request({
        method: 'GET',
        headers: {
            'Authorization': `Basic ${lmlf_auth}`,
            json: true
        },
        url: `${process.env.LMLF_URL}/oauth/token?grant_type=client_credentials`,
        json: true
    });
    const apiKey = tokenResp.access_token;
    var imageFeatureExtractionData = {};

    imageFeatureExtractionData = await request({
        method: 'POST',
        headers: {
            'Authorization': 'Bearer ' + apiKey
        },
        formData: {
            files: {
                value: await request.get(image, { encoding: null }),
                options: {
                    filename: 'label.jpg',
                    contentType: 'image/jepg'
                }
            }
        },
        url: `${process.env.LMLF_IMAGE_FEATURE_EXTRACTION_URL}`,
        json: true
    });

    const similarScoring = { "0": [] };
    similarScoring["0"].push({"id": "examinee", "vector": imageFeatureExtractionData.predictions[0].featureVectors});
    var referenceLabel = JSON.parse(process.env.REFERENCE_1);
    const referenceLabels = [];
    referenceLabels.push(referenceLabel);
    similarScoring["0"].push({"id": 0, "vector": referenceLabel.featureVectors});
    referenceLabel = JSON.parse(process.env.REFERENCE_2);
    referenceLabels.push(referenceLabel);
    similarScoring["0"].push({"id": 1, "vector": referenceLabel.featureVectors});
    referenceLabel = JSON.parse(process.env.REFERENCE_3);
    referenceLabels.push(referenceLabel);
    similarScoring["0"].push({"id": 2, "vector": referenceLabel.featureVectors});
    referenceLabel = JSON.parse(process.env.REFERENCE_4);
    referenceLabels.push(referenceLabel);
    similarScoring["0"].push({"id": 3, "vector": referenceLabel.featureVectors});
    referenceLabel = JSON.parse(process.env.REFERENCE_5);
    referenceLabels.push(referenceLabel);
    similarScoring["0"].push({"id": 4, "vector": referenceLabel.featureVectors});

    const imageSimilarityScoringData = await request({
        method: 'POST',
        headers: {
            'Authorization': 'Bearer ' + apiKey
        },
        formData: {
            texts: JSON.stringify(similarScoring),
            options: '{"numSimilarVectors":1}'
        },
        url: `${process.env.LMLF_SIMILARITY_SCORING_URL}`,
        json: true
    });
    
    var similarityResult = imageSimilarityScoringData.predictions[0].similarVectors[0];
    var similar = referenceLabels[similarityResult.id];
    similarityResponse = {
        name: similar.name,
        image: similar.image,
        detail: similar.detail,
        score: similarityResult.score
    };
    
    //--------- Helper Functions GOOGLE VISION Scan Text --------------
    const regexDetectLeviTrade = /(501|502|504|505|510|511|512|513|514|517|519|527|541|550|559|560|569|705)/;
    const regexSize = /(W *\d{2,} *(I|L|1|\|) *\d{2,})|(\d{2,} *(I|L|1|\|) *\d{2,}^)|(W *\d{2,} *\d{2,})/;
    const regexSizeNumber = /[2-6]{1}\d{1}/g;
    
    const outScanGoogle = function(text) {
        var resp = {
            id: "Done!",
            type: {number: "", name: "Google Scan"},
            size: {W:0, L:0}
        };
        var regex;
        regex = regexDetectLeviTrade.exec(text);
        if (regex) {
            resp.type.number = regex[0];
        }
        regex = regexSize.exec(text);
        if (regex) {
            var textSize = regex[0];
            var isW = true;
            do {
                regex = regexSizeNumber.exec(textSize);
                if (regex) {
                    if (isW){
                        resp.size.W = parseInt(regex[0]);
                        isW = false;
                    } else {
                        resp.size.L = parseInt(regex[0]);
                    }
                }
            } while(regex);
        }
        return resp;
    };

    //--------- GOOGLE VISION Scan Text --------------
    const vision = require('@google-cloud/vision');
    const client = new vision.ImageAnnotatorClient({credentials:{
        client_email: process.env.GOOGLE_APPLICATION_CREDENTIALS_EMAIL,
        private_key: process.env.GOOGLE_APPLICATION_CREDENTIALS_KEY.replace(/\\n/g, '\n')
        
    }});
    
    const [googleResult] = await client.documentTextDetection(`${image}`);
    const fullTextAnnotation = googleResult.fullTextAnnotation;
    var text = fullTextAnnotation ? fullTextAnnotation.text : "";
    var response = outScanGoogle(text);
    response.similarity = similarityResponse;

    return {
        response
    }
}}