feat(ml)!: customizable ML settings (#3891)

* consolidated endpoints, added live configuration

* added ml settings to server

* added settings dashboard

* updated deps, fixed typos

* simplified modelconfig

updated tests

* Added ml setting accordion for admin page

updated tests

* merge `clipText` and `clipVision`

* added face distance setting

clarified setting

* add clip mode in request, dropdown for face models

* polished ml settings

updated descriptions

* update clip field on error

* removed unused import

* add description for image classification threshold

* pin safetensors for arm wheel

updated poetry lock

* moved dto

* set model type only in ml repository

* revert form-data package install

use fetch instead of axios

* added slotted description with link

updated facial recognition description

clarified effect of disabling tasks

* validation before model load

* removed unnecessary getconfig call

* added migration

* updated api

updated api

updated api

---------

Co-authored-by: Alex Tran <alex.tran1502@gmail.com>

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

@@ -1,29 +1,65 @@
-import { DetectFaceResult, IMachineLearningRepository, MachineLearningInput } from '@app/domain';
+import {
+  ClassificationConfig,
+  CLIPConfig,
+  CLIPMode,
+  DetectFaceResult,
+  IMachineLearningRepository,
+  ModelConfig,
+  ModelType,
+  RecognitionConfig,
+  TextModelInput,
+  VisionModelInput,
+} from '@app/domain';
 import { Injectable } from '@nestjs/common';
-import axios from 'axios';
-import { createReadStream } from 'fs';
-
-const client = axios.create();
+import { readFile } from 'fs/promises';
 
 @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);
+  private async post<T>(url: string, input: TextModelInput | VisionModelInput, config: ModelConfig): Promise<T> {
+    const formData = await this.getFormData(input, config);
+    const res = await fetch(`${url}/predict`, { method: 'POST', body: formData });
+    return res.json();
   }
 
-  classifyImage(url: string, input: MachineLearningInput): Promise<string[]> {
-    return this.post<string[]>(input, `${url}/image-classifier/tag-image`);
+  classifyImage(url: string, input: VisionModelInput, config: ClassificationConfig): Promise<string[]> {
+    return this.post<string[]>(url, input, { ...config, modelType: ModelType.IMAGE_CLASSIFICATION });
   }
 
-  detectFaces(url: string, input: MachineLearningInput): Promise<DetectFaceResult[]> {
-    return this.post<DetectFaceResult[]>(input, `${url}/facial-recognition/detect-faces`);
+  detectFaces(url: string, input: VisionModelInput, config: RecognitionConfig): Promise<DetectFaceResult[]> {
+    return this.post<DetectFaceResult[]>(url, input, { ...config, modelType: ModelType.FACIAL_RECOGNITION });
   }
 
-  encodeImage(url: string, input: MachineLearningInput): Promise<number[]> {
-    return this.post<number[]>(input, `${url}/sentence-transformer/encode-image`);
+  encodeImage(url: string, input: VisionModelInput, config: CLIPConfig): Promise<number[]> {
+    return this.post<number[]>(url, input, {
+      ...config,
+      modelType: ModelType.CLIP,
+      mode: CLIPMode.VISION,
+    } as CLIPConfig);
   }
 
-  encodeText(url: string, input: string): Promise<number[]> {
-    return client.post<number[]>(`${url}/sentence-transformer/encode-text`, { text: input }).then((res) => res.data);
+  encodeText(url: string, input: TextModelInput, config: CLIPConfig): Promise<number[]> {
+    return this.post<number[]>(url, input, { ...config, modelType: ModelType.CLIP, mode: CLIPMode.TEXT } as CLIPConfig);
+  }
+
+  async getFormData(input: TextModelInput | VisionModelInput, config: ModelConfig): Promise<FormData> {
+    const formData = new FormData();
+    const { modelName, modelType, ...options } = config;
+
+    formData.append('modelName', modelName);
+    if (modelType) {
+      formData.append('modelType', modelType);
+    }
+    if (options) {
+      formData.append('options', JSON.stringify(options));
+    }
+    if ('imagePath' in input) {
+      formData.append('image', new Blob([await readFile(input.imagePath)]));
+    } else if ('text' in input) {
+      formData.append('text', input.text);
+    } else {
+      throw new Error('Invalid input');
+    }
+
+    return formData;
   }
 }