Zeeshan Khan
34201be74c
* using pydantic BaseSetting * ML API takes image file as input * keeping image in memory * reducing duplicate code * using bytes instead of UploadFile & other small code improvements * removed form-multipart, using HTTP body * format code --------- Co-authored-by: Alex Tran <alex.tran1502@gmail.com>
30 lines
1.2 KiB
TypeScript
30 lines
1.2 KiB
TypeScript
import { DetectFaceResult, IMachineLearningRepository, MachineLearningInput, MACHINE_LEARNING_URL } from '@app/domain';
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import { Injectable } from '@nestjs/common';
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import axios from 'axios';
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import { createReadStream } from 'fs';
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const client = axios.create({ baseURL: MACHINE_LEARNING_URL });
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@Injectable()
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export class MachineLearningRepository implements IMachineLearningRepository {
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private post<T>(input: MachineLearningInput, endpoint: string): Promise<T> {
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return client.post<T>(endpoint, createReadStream(input.imagePath)).then((res) => res.data);
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}
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classifyImage(input: MachineLearningInput): Promise<string[]> {
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return this.post<string[]>(input, '/image-classifier/tag-image');
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}
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detectFaces(input: MachineLearningInput): Promise<DetectFaceResult[]> {
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return this.post<DetectFaceResult[]>(input, '/facial-recognition/detect-faces');
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}
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encodeImage(input: MachineLearningInput): Promise<number[]> {
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return this.post<number[]>(input, '/sentence-transformer/encode-image');
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}
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encodeText(input: string): Promise<number[]> {
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return client.post<number[]>('/sentence-transformer/encode-text', { text: input }).then((res) => res.data);
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}
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}
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