feat(ml) backend takes image over HTTP (#2783)

* 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>

server/src/infra/repositories/machine-learning.repository.ts

@@ -1,21 +1,26 @@
 import { DetectFaceResult, IMachineLearningRepository, MachineLearningInput, MACHINE_LEARNING_URL } from '@app/domain';
 import { Injectable } from '@nestjs/common';
 import axios from 'axios';
+import { createReadStream } from 'fs';
 
 const client = axios.create({ baseURL: MACHINE_LEARNING_URL });
 
 @Injectable()
 export class MachineLearningRepository implements IMachineLearningRepository {
+  private post<T>(input: MachineLearningInput, endpoint: string): Promise<T> {
+    return client.post<T>(endpoint, createReadStream(input.imagePath)).then((res) => res.data);
+  }
+
   classifyImage(input: MachineLearningInput): Promise<string[]> {
-    return client.post<string[]>('/image-classifier/tag-image', input).then((res) => res.data);
+    return this.post<string[]>(input, '/image-classifier/tag-image');
   }
 
   detectFaces(input: MachineLearningInput): Promise<DetectFaceResult[]> {
-    return client.post<DetectFaceResult[]>('/facial-recognition/detect-faces', input).then((res) => res.data);
+    return this.post<DetectFaceResult[]>(input, '/facial-recognition/detect-faces');
   }
 
   encodeImage(input: MachineLearningInput): Promise<number[]> {
-    return client.post<number[]>('/sentence-transformer/encode-image', input).then((res) => res.data);
+    return this.post<number[]>(input, '/sentence-transformer/encode-image');
   }
 
   encodeText(input: string): Promise<number[]> {