feat: implement backend image classification with TensorFlow.js model
- Extend shared types for image classification, including PredictionProbability, UploaderMetadata, and ImageClassificationRecord. - Create image_classifications table in the database with necessary fields and foreign key constraints. - Implement disease mappers to convert database rows to shared disease records. - Develop uploader client to handle image uploads to external service. - Create image model service to load and classify images using TensorFlow.js. - Add API routes for image classification, including GET for history and POST for new classifications. - Implement frontend components for image classification form and display results. - Update dashboard to integrate image classification functionality and display results. - Document implementation plan for backend image classification.
This commit is contained in:
Asep Haryana Saputra
@@ -0,0 +1,17 @@
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CREATE TABLE IF NOT EXISTS "image_classifications" (
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"id" uuid PRIMARY KEY DEFAULT gen_random_uuid() NOT NULL,
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"predicted_disease_slug" varchar(80) NOT NULL,
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"confidence" real NOT NULL,
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"probabilities" jsonb NOT NULL,
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"image_url" text NOT NULL,
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"original_file_name" varchar(240) NOT NULL,
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"uploader_public_id" varchar(160) NOT NULL,
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"uploader_payload" jsonb NOT NULL,
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"created_at" timestamp with time zone DEFAULT now() NOT NULL
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);
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--> statement-breakpoint
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DO $$ BEGIN
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ALTER TABLE "image_classifications" ADD CONSTRAINT "image_classifications_predicted_disease_slug_disease_catalog_slug_fk" FOREIGN KEY ("predicted_disease_slug") REFERENCES "public"."disease_catalog"("slug") ON DELETE no action ON UPDATE no action;
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EXCEPTION
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WHEN duplicate_object THEN null;
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END $$;
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@@ -0,0 +1,281 @@
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{
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"id": "16700e2c-ff8f-4864-a71a-6f4b98a1313a",
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"prevId": "71e2eeba-4f8f-48bc-8bcf-63201334aa65",
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"version": "7",
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"dialect": "postgresql",
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"tables": {
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"public.app_events": {
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"name": "app_events",
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"schema": "",
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"columns": {
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"id": {
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"name": "id",
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"type": "uuid",
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"primaryKey": true,
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"notNull": true,
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"default": "gen_random_uuid()"
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},
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"name": {
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"name": "name",
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"type": "varchar(120)",
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"primaryKey": false,
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"notNull": true
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},
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"created_at": {
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"name": "created_at",
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"type": "timestamp with time zone",
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"primaryKey": false,
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"notNull": true,
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"default": "now()"
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}
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},
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"indexes": {},
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"foreignKeys": {},
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"compositePrimaryKeys": {},
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"uniqueConstraints": {},
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"policies": {},
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"checkConstraints": {},
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"isRLSEnabled": false
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},
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"public.disease_catalog": {
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"name": "disease_catalog",
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"schema": "",
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"columns": {
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"slug": {
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"name": "slug",
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"type": "varchar(80)",
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"primaryKey": true,
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"notNull": true
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},
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"label": {
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"name": "label",
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"type": "varchar(80)",
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"primaryKey": false,
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"notNull": true
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},
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"common_name": {
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"name": "common_name",
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"type": "varchar(120)",
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"primaryKey": false,
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"notNull": true
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},
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"summary": {
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"name": "summary",
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"type": "text",
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"primaryKey": false,
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"notNull": true
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},
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"description": {
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"name": "description",
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"type": "text",
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"primaryKey": false,
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"notNull": true
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},
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"symptoms": {
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"name": "symptoms",
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"type": "text[]",
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"primaryKey": false,
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"notNull": true
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},
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"recommendations": {
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"name": "recommendations",
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"type": "text[]",
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"primaryKey": false,
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"notNull": true
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},
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"risk_level": {
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"name": "risk_level",
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"type": "varchar(20)",
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"primaryKey": false,
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"notNull": true
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},
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"accent_color": {
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"name": "accent_color",
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"type": "varchar(40)",
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"primaryKey": false,
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"notNull": true
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},
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"display_order": {
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"name": "display_order",
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"type": "integer",
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"primaryKey": false,
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"notNull": true
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},
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"created_at": {
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"name": "created_at",
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"type": "timestamp with time zone",
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"primaryKey": false,
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"notNull": true,
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"default": "now()"
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},
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"updated_at": {
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"name": "updated_at",
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"type": "timestamp with time zone",
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"primaryKey": false,
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"notNull": true,
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"default": "now()"
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}
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},
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"indexes": {},
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"foreignKeys": {},
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"compositePrimaryKeys": {},
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"uniqueConstraints": {},
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"policies": {},
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"checkConstraints": {},
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"isRLSEnabled": false
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},
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"public.image_classifications": {
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"name": "image_classifications",
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"schema": "",
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"columns": {
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"id": {
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"name": "id",
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"type": "uuid",
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"primaryKey": true,
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"notNull": true,
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"default": "gen_random_uuid()"
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},
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"predicted_disease_slug": {
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"name": "predicted_disease_slug",
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"type": "varchar(80)",
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"primaryKey": false,
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"notNull": true
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},
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"confidence": {
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"name": "confidence",
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"type": "real",
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"primaryKey": false,
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"notNull": true
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},
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"probabilities": {
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"name": "probabilities",
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"type": "jsonb",
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"primaryKey": false,
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"notNull": true
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},
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"image_url": {
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"name": "image_url",
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"type": "text",
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"primaryKey": false,
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"notNull": true
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},
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"original_file_name": {
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"name": "original_file_name",
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"type": "varchar(240)",
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"primaryKey": false,
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"notNull": true
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},
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"uploader_public_id": {
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"name": "uploader_public_id",
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"type": "varchar(160)",
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"primaryKey": false,
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"notNull": true
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},
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"uploader_payload": {
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"name": "uploader_payload",
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"type": "jsonb",
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"primaryKey": false,
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"notNull": true
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},
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"created_at": {
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"name": "created_at",
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"type": "timestamp with time zone",
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"primaryKey": false,
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"notNull": true,
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"default": "now()"
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}
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},
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"indexes": {},
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"foreignKeys": {
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"image_classifications_predicted_disease_slug_disease_catalog_slug_fk": {
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"name": "image_classifications_predicted_disease_slug_disease_catalog_slug_fk",
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"tableFrom": "image_classifications",
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"tableTo": "disease_catalog",
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"columnsFrom": [
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"predicted_disease_slug"
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],
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"columnsTo": [
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"slug"
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],
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"onDelete": "no action",
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"onUpdate": "no action"
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}
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},
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"compositePrimaryKeys": {},
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||||
"uniqueConstraints": {},
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||||
"policies": {},
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"checkConstraints": {},
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"isRLSEnabled": false
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},
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"public.manual_classifications": {
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"name": "manual_classifications",
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"schema": "",
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"columns": {
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"id": {
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"name": "id",
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"type": "uuid",
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"primaryKey": true,
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"notNull": true,
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"default": "gen_random_uuid()"
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},
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"disease_slug": {
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"name": "disease_slug",
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"type": "varchar(80)",
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"primaryKey": false,
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"notNull": true
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},
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"observation": {
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"name": "observation",
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"type": "text",
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"primaryKey": false,
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"notNull": true
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},
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"location": {
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"name": "location",
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"type": "varchar(160)",
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"primaryKey": false,
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"notNull": true
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},
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"created_at": {
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"name": "created_at",
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"type": "timestamp with time zone",
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"primaryKey": false,
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"notNull": true,
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"default": "now()"
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}
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},
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"indexes": {},
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"foreignKeys": {
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"manual_classifications_disease_slug_disease_catalog_slug_fk": {
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"name": "manual_classifications_disease_slug_disease_catalog_slug_fk",
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"tableFrom": "manual_classifications",
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"tableTo": "disease_catalog",
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"columnsFrom": [
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"disease_slug"
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],
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"columnsTo": [
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"slug"
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],
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"onDelete": "no action",
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"onUpdate": "no action"
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}
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},
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"compositePrimaryKeys": {},
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"uniqueConstraints": {},
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"policies": {},
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"checkConstraints": {},
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"isRLSEnabled": false
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}
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},
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"enums": {},
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"schemas": {},
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"sequences": {},
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"roles": {},
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"policies": {},
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"views": {},
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"_meta": {
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"columns": {},
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"schemas": {},
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"tables": {}
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}
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}
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@@ -8,6 +8,13 @@
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"when": 1779458573080,
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"tag": "0000_harsh_arachne",
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"breakpoints": true
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},
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{
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"idx": 1,
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"version": "7",
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"when": 1779461286348,
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"tag": "0001_cute_lyja",
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"breakpoints": true
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}
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]
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}
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@@ -11,9 +11,12 @@
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"db:migrate": "drizzle-kit migrate"
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},
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"dependencies": {
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"@tensorflow/tfjs": "4.22.0",
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"@zeavis/shared": "workspace:*",
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"drizzle-orm": "^0.36.4",
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"elysia": "^1.1.25",
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"jpeg-js": "0.4.4",
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"pngjs": "7.0.0",
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"postgres": "^3.4.5"
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},
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"devDependencies": {
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@@ -1,4 +1,4 @@
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import { pgTable, timestamp, uuid, varchar, text, integer } from 'drizzle-orm/pg-core';
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import { pgTable, timestamp, uuid, varchar, text, integer, jsonb, real } from 'drizzle-orm/pg-core';
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export const appEvents = pgTable('app_events', {
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id: uuid('id').primaryKey().defaultRandom(),
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@@ -30,3 +30,17 @@ export const manualClassifications = pgTable('manual_classifications', {
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location: varchar('location', { length: 160 }).notNull(),
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createdAt: timestamp('created_at', { withTimezone: true }).notNull().defaultNow(),
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});
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export const imageClassifications = pgTable('image_classifications', {
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id: uuid('id').primaryKey().defaultRandom(),
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predictedDiseaseSlug: varchar('predicted_disease_slug', { length: 80 })
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.notNull()
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.references(() => diseaseCatalog.slug),
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confidence: real('confidence').notNull(),
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probabilities: jsonb('probabilities').notNull(),
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imageUrl: text('image_url').notNull(),
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originalFileName: varchar('original_file_name', { length: 240 }).notNull(),
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uploaderPublicId: varchar('uploader_public_id', { length: 160 }).notNull(),
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uploaderPayload: jsonb('uploader_payload').notNull(),
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createdAt: timestamp('created_at', { withTimezone: true }).notNull().defaultNow(),
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});
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@@ -0,0 +1,17 @@
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import type { DiseaseCatalogItem, DiseaseSlug, DiseaseLabel, RiskLevel } from '@zeavis/shared';
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import type { diseaseCatalog } from '../db/schema';
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export function toDisease(row: typeof diseaseCatalog.$inferSelect): DiseaseCatalogItem {
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return {
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slug: row.slug as DiseaseSlug,
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label: row.label as DiseaseLabel,
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commonName: row.commonName,
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summary: row.summary,
|
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description: row.description,
|
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symptoms: row.symptoms,
|
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recommendations: row.recommendations,
|
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riskLevel: row.riskLevel as RiskLevel,
|
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accentColor: row.accentColor,
|
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displayOrder: row.displayOrder,
|
||||
};
|
||||
}
|
||||
@@ -12,6 +12,13 @@ export function notFound(message: string): Response {
|
||||
});
|
||||
}
|
||||
|
||||
export function badGateway(message: string): Response {
|
||||
return new Response(JSON.stringify({ error: message }), {
|
||||
status: 502,
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
});
|
||||
}
|
||||
|
||||
export function serviceUnavailable(message: string): Response {
|
||||
return new Response(JSON.stringify({ error: message }), {
|
||||
status: 503,
|
||||
|
||||
@@ -0,0 +1,134 @@
|
||||
import * as tf from '@tensorflow/tfjs';
|
||||
import * as jpeg from 'jpeg-js';
|
||||
import { PNG } from 'pngjs';
|
||||
import { existsSync } from 'fs';
|
||||
import { resolve } from 'path';
|
||||
import type { DiseaseSlug, DiseaseLabel, PredictionProbability } from '@zeavis/shared';
|
||||
|
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const DISEASE_CLASSES: Array<{ slug: DiseaseSlug; label: DiseaseLabel }> = [
|
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{ slug: 'bercak-daun', label: 'Bercak Daun' },
|
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{ slug: 'daun-sehat', label: 'Daun Sehat' },
|
||||
{ slug: 'karat-daun', label: 'Karat Daun' },
|
||||
{ slug: 'hawar-daun', label: 'Hawar Daun' },
|
||||
];
|
||||
|
||||
function resolveModelPath() {
|
||||
const candidates = [
|
||||
resolve(process.cwd(), 'Machine_Learning/model/tfjs_model/model.json'),
|
||||
resolve(process.cwd(), '../../Machine_Learning/model/tfjs_model/model.json'),
|
||||
];
|
||||
|
||||
const modelPath = candidates.find((candidate) => existsSync(candidate));
|
||||
if (!modelPath) {
|
||||
throw new Error('TFJS model file was not found');
|
||||
}
|
||||
|
||||
return modelPath;
|
||||
}
|
||||
|
||||
let modelPromise: Promise<tf.GraphModel> | null = null;
|
||||
|
||||
async function loadModel(): Promise<tf.GraphModel> {
|
||||
if (modelPromise) {
|
||||
return modelPromise;
|
||||
}
|
||||
|
||||
modelPromise = (async () => {
|
||||
try {
|
||||
const fileUrl = `file://${resolveModelPath()}`;
|
||||
return await tf.loadGraphModel(fileUrl);
|
||||
} catch (error) {
|
||||
throw new Error(`Failed to load TFJS model: ${error instanceof Error ? error.message : String(error)}`);
|
||||
}
|
||||
})();
|
||||
|
||||
return modelPromise;
|
||||
}
|
||||
|
||||
export type ClassificationResult = {
|
||||
predictedDiseaseSlug: DiseaseSlug;
|
||||
confidence: number;
|
||||
probabilities: PredictionProbability[];
|
||||
};
|
||||
|
||||
export async function classifyImage(file: File): Promise<ClassificationResult> {
|
||||
if (file.type !== 'image/jpeg' && file.type !== 'image/png') {
|
||||
throw new Error('File must be JPEG or PNG');
|
||||
}
|
||||
|
||||
const buffer = await file.arrayBuffer();
|
||||
const uint8Array = new Uint8Array(buffer);
|
||||
|
||||
let imageData: { data: Uint8Array; width: number; height: number };
|
||||
|
||||
if (file.type === 'image/jpeg') {
|
||||
const decoded = jpeg.decode(uint8Array, { useTArray: true });
|
||||
imageData = {
|
||||
data: decoded.data,
|
||||
width: decoded.width,
|
||||
height: decoded.height,
|
||||
};
|
||||
} else {
|
||||
const png = new PNG();
|
||||
await new Promise<void>((resolve, reject) => {
|
||||
png.parse(Buffer.from(uint8Array), (err: Error | null) => {
|
||||
if (err) reject(err);
|
||||
else resolve();
|
||||
});
|
||||
});
|
||||
imageData = {
|
||||
data: png.data,
|
||||
width: png.width,
|
||||
height: png.height,
|
||||
};
|
||||
}
|
||||
|
||||
const imageTensor = tf.tidy(() => {
|
||||
const rgb = new Uint8Array(imageData.width * imageData.height * 3);
|
||||
for (let source = 0, target = 0; source < imageData.data.length; source += 4, target += 3) {
|
||||
rgb[target] = imageData.data[source];
|
||||
rgb[target + 1] = imageData.data[source + 1];
|
||||
rgb[target + 2] = imageData.data[source + 2];
|
||||
}
|
||||
|
||||
return tf
|
||||
.tensor3d(rgb, [imageData.height, imageData.width, 3], 'int32')
|
||||
.resizeBilinear([224, 224])
|
||||
.toFloat()
|
||||
.expandDims(0);
|
||||
});
|
||||
|
||||
try {
|
||||
const model = await loadModel();
|
||||
const predictions = model.predict(imageTensor) as tf.Tensor;
|
||||
|
||||
try {
|
||||
const scoresArray = await predictions.data();
|
||||
|
||||
let maxScore = -Infinity;
|
||||
let maxIndex = 0;
|
||||
for (let i = 0; i < scoresArray.length; i++) {
|
||||
if (scoresArray[i] > maxScore) {
|
||||
maxScore = scoresArray[i];
|
||||
maxIndex = i;
|
||||
}
|
||||
}
|
||||
|
||||
const probabilities: PredictionProbability[] = DISEASE_CLASSES.map((disease, index) => ({
|
||||
diseaseSlug: disease.slug,
|
||||
label: disease.label,
|
||||
confidence: Math.max(0, Math.min(1, scoresArray[index])),
|
||||
})).sort((a, b) => b.confidence - a.confidence);
|
||||
|
||||
return {
|
||||
predictedDiseaseSlug: DISEASE_CLASSES[maxIndex].slug,
|
||||
confidence: Math.max(0, Math.min(1, maxScore)),
|
||||
probabilities,
|
||||
};
|
||||
} finally {
|
||||
predictions.dispose();
|
||||
}
|
||||
} finally {
|
||||
imageTensor.dispose();
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,28 @@
|
||||
import type { UploaderMetadata } from '@zeavis/shared';
|
||||
|
||||
export async function uploadImageToStorage(file: File): Promise<UploaderMetadata> {
|
||||
const formData = new FormData();
|
||||
formData.append('file', file);
|
||||
formData.append('fileName', file.name);
|
||||
|
||||
const response = await fetch('https://upload.asepharyana.tech/api/upload', {
|
||||
method: 'POST',
|
||||
body: formData,
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
throw new Error(`Upload failed with status ${response.status}`);
|
||||
}
|
||||
|
||||
const data = (await response.json()) as Record<string, unknown>;
|
||||
|
||||
if (!data.download_url) {
|
||||
throw new Error('Upload response missing download_url');
|
||||
}
|
||||
|
||||
if (!data.public_id) {
|
||||
throw new Error('Upload response missing public_id');
|
||||
}
|
||||
|
||||
return data as UploaderMetadata;
|
||||
}
|
||||
@@ -1,12 +1,215 @@
|
||||
import { Elysia } from 'elysia';
|
||||
import type { ManualClassificationRequest, ManualClassificationRecord } from '@zeavis/shared';
|
||||
import type {
|
||||
DiseaseCatalogItem,
|
||||
DiseaseSlug,
|
||||
ImageClassificationRecord,
|
||||
ManualClassificationRequest,
|
||||
ManualClassificationRecord,
|
||||
PredictionProbability,
|
||||
UploaderMetadata,
|
||||
} from '@zeavis/shared';
|
||||
import { isDiseaseSlug } from '@zeavis/shared';
|
||||
import { createDbClient } from '../db/client';
|
||||
import { diseaseCatalog, manualClassifications } from '../db/schema';
|
||||
import { badRequest, serviceUnavailable } from '../lib/http-errors';
|
||||
import { diseaseCatalog, manualClassifications, imageClassifications } from '../db/schema';
|
||||
import { badRequest, badGateway, serviceUnavailable } from '../lib/http-errors';
|
||||
import { desc, eq } from 'drizzle-orm';
|
||||
import { classifyImage } from '../lib/image-model';
|
||||
import { uploadImageToStorage } from '../lib/uploader-client';
|
||||
import { toDisease } from '../lib/disease-mappers';
|
||||
|
||||
function toImageClassificationRecord(row: {
|
||||
id: string;
|
||||
predictedDiseaseSlug: string;
|
||||
confidence: number;
|
||||
probabilities: unknown;
|
||||
imageUrl: string;
|
||||
originalFileName: string;
|
||||
uploaderPublicId: string;
|
||||
uploaderPayload: unknown;
|
||||
createdAt: Date;
|
||||
disease: {
|
||||
slug: string;
|
||||
label: string;
|
||||
commonName: string;
|
||||
summary: string;
|
||||
description: string;
|
||||
symptoms: string[];
|
||||
recommendations: string[];
|
||||
riskLevel: string;
|
||||
accentColor: string;
|
||||
displayOrder: number;
|
||||
};
|
||||
}): ImageClassificationRecord {
|
||||
return {
|
||||
id: row.id,
|
||||
predictedDiseaseSlug: row.predictedDiseaseSlug as DiseaseSlug,
|
||||
confidence: row.confidence,
|
||||
probabilities: row.probabilities as PredictionProbability[],
|
||||
imageUrl: row.imageUrl,
|
||||
originalFileName: row.originalFileName,
|
||||
uploaderPublicId: row.uploaderPublicId,
|
||||
uploader: row.uploaderPayload as UploaderMetadata,
|
||||
createdAt: row.createdAt.toISOString(),
|
||||
disease: toDisease(row.disease as typeof diseaseCatalog.$inferSelect),
|
||||
};
|
||||
}
|
||||
|
||||
export const classificationRoutes = new Elysia({ prefix: '/api/v1' })
|
||||
.get('/classifications/image', async () => {
|
||||
try {
|
||||
const db = createDbClient();
|
||||
const rows = await db
|
||||
.select({
|
||||
id: imageClassifications.id,
|
||||
predictedDiseaseSlug: imageClassifications.predictedDiseaseSlug,
|
||||
confidence: imageClassifications.confidence,
|
||||
probabilities: imageClassifications.probabilities,
|
||||
imageUrl: imageClassifications.imageUrl,
|
||||
originalFileName: imageClassifications.originalFileName,
|
||||
uploaderPublicId: imageClassifications.uploaderPublicId,
|
||||
uploaderPayload: imageClassifications.uploaderPayload,
|
||||
createdAt: imageClassifications.createdAt,
|
||||
disease: {
|
||||
slug: diseaseCatalog.slug,
|
||||
label: diseaseCatalog.label,
|
||||
commonName: diseaseCatalog.commonName,
|
||||
summary: diseaseCatalog.summary,
|
||||
description: diseaseCatalog.description,
|
||||
symptoms: diseaseCatalog.symptoms,
|
||||
recommendations: diseaseCatalog.recommendations,
|
||||
riskLevel: diseaseCatalog.riskLevel,
|
||||
accentColor: diseaseCatalog.accentColor,
|
||||
displayOrder: diseaseCatalog.displayOrder,
|
||||
},
|
||||
})
|
||||
.from(imageClassifications)
|
||||
.innerJoin(diseaseCatalog, eq(imageClassifications.predictedDiseaseSlug, diseaseCatalog.slug))
|
||||
.orderBy(desc(imageClassifications.createdAt))
|
||||
.limit(20);
|
||||
|
||||
const records: ImageClassificationRecord[] = rows.map(toImageClassificationRecord);
|
||||
return records;
|
||||
} catch (error) {
|
||||
return serviceUnavailable('Database unavailable');
|
||||
}
|
||||
})
|
||||
.post('/classifications/image', async ({ body }) => {
|
||||
let file: File | undefined;
|
||||
|
||||
if (body instanceof FormData) {
|
||||
const formFile = body.get('file');
|
||||
if (formFile instanceof File) {
|
||||
file = formFile;
|
||||
}
|
||||
} else if (typeof body === 'object' && body !== null) {
|
||||
const bodyObj = body as Record<string, unknown>;
|
||||
if (bodyObj.file instanceof File) {
|
||||
file = bodyObj.file;
|
||||
} else if (Array.isArray(bodyObj.file) && bodyObj.file.length > 0 && bodyObj.file[0] instanceof File) {
|
||||
file = bodyObj.file[0];
|
||||
}
|
||||
}
|
||||
|
||||
if (!file) {
|
||||
return badRequest('Missing required field: file');
|
||||
}
|
||||
|
||||
if (file.size === 0) {
|
||||
return badRequest('File is empty');
|
||||
}
|
||||
|
||||
if (file.type !== 'image/jpeg' && file.type !== 'image/png') {
|
||||
return badRequest('File must be JPEG or PNG image');
|
||||
}
|
||||
|
||||
try {
|
||||
let uploaderMetadata: UploaderMetadata;
|
||||
try {
|
||||
uploaderMetadata = await uploadImageToStorage(file);
|
||||
} catch (error) {
|
||||
return badGateway(
|
||||
`Upload service error: ${error instanceof Error ? error.message : 'Unknown error'}`
|
||||
);
|
||||
}
|
||||
|
||||
let classificationResult;
|
||||
try {
|
||||
classificationResult = await classifyImage(file);
|
||||
} catch (error) {
|
||||
return serviceUnavailable(
|
||||
`Model service error: ${error instanceof Error ? error.message : 'Unknown error'}`
|
||||
);
|
||||
}
|
||||
|
||||
const db = createDbClient();
|
||||
let diseaseRow;
|
||||
try {
|
||||
const diseaseRows = await db
|
||||
.select()
|
||||
.from(diseaseCatalog)
|
||||
.where(eq(diseaseCatalog.slug, classificationResult.predictedDiseaseSlug))
|
||||
.limit(1);
|
||||
|
||||
if (diseaseRows.length === 0) {
|
||||
return serviceUnavailable(
|
||||
`Predicted disease "${classificationResult.predictedDiseaseSlug}" not found in catalog`
|
||||
);
|
||||
}
|
||||
|
||||
diseaseRow = diseaseRows[0];
|
||||
} catch (error) {
|
||||
return serviceUnavailable('Database unavailable');
|
||||
}
|
||||
|
||||
let inserted;
|
||||
try {
|
||||
const result = await db
|
||||
.insert(imageClassifications)
|
||||
.values({
|
||||
predictedDiseaseSlug: classificationResult.predictedDiseaseSlug,
|
||||
confidence: classificationResult.confidence,
|
||||
probabilities: classificationResult.probabilities,
|
||||
imageUrl: uploaderMetadata.download_url,
|
||||
originalFileName: file.name,
|
||||
uploaderPublicId: uploaderMetadata.public_id,
|
||||
uploaderPayload: uploaderMetadata,
|
||||
})
|
||||
.returning();
|
||||
|
||||
inserted = result[0];
|
||||
} catch (error) {
|
||||
return serviceUnavailable('Database unavailable');
|
||||
}
|
||||
|
||||
const record: ImageClassificationRecord = toImageClassificationRecord({
|
||||
id: inserted.id,
|
||||
predictedDiseaseSlug: inserted.predictedDiseaseSlug,
|
||||
confidence: inserted.confidence,
|
||||
probabilities: inserted.probabilities,
|
||||
imageUrl: inserted.imageUrl,
|
||||
originalFileName: inserted.originalFileName,
|
||||
uploaderPublicId: inserted.uploaderPublicId,
|
||||
uploaderPayload: inserted.uploaderPayload,
|
||||
createdAt: inserted.createdAt,
|
||||
disease: {
|
||||
slug: diseaseRow.slug,
|
||||
label: diseaseRow.label,
|
||||
commonName: diseaseRow.commonName,
|
||||
summary: diseaseRow.summary,
|
||||
description: diseaseRow.description,
|
||||
symptoms: diseaseRow.symptoms,
|
||||
recommendations: diseaseRow.recommendations,
|
||||
riskLevel: diseaseRow.riskLevel,
|
||||
accentColor: diseaseRow.accentColor,
|
||||
displayOrder: diseaseRow.displayOrder,
|
||||
},
|
||||
});
|
||||
|
||||
return record;
|
||||
} catch (error) {
|
||||
return serviceUnavailable('Internal server error');
|
||||
}
|
||||
})
|
||||
.get('/classifications/manual', async () => {
|
||||
try {
|
||||
const db = createDbClient();
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import { Elysia } from 'elysia';
|
||||
import type { DashboardSummary, RiskLevel } from '@zeavis/shared';
|
||||
import { createDbClient } from '../db/client';
|
||||
import { diseaseCatalog, manualClassifications } from '../db/schema';
|
||||
import { diseaseCatalog, imageClassifications, manualClassifications } from '../db/schema';
|
||||
import { serviceUnavailable } from '../lib/http-errors';
|
||||
import { desc, eq } from 'drizzle-orm';
|
||||
|
||||
@@ -10,7 +10,8 @@ export const dashboardRoutes = new Elysia({ prefix: '/api/v1' }).get('/dashboard
|
||||
const db = createDbClient();
|
||||
|
||||
const diseaseCount = await db.select().from(diseaseCatalog);
|
||||
const classificationCount = await db.select().from(manualClassifications);
|
||||
const manualClassificationCount = await db.select().from(manualClassifications);
|
||||
const imageClassificationCount = await db.select().from(imageClassifications);
|
||||
|
||||
const latestClassificationRow = await db
|
||||
.select({
|
||||
@@ -75,7 +76,7 @@ export const dashboardRoutes = new Elysia({ prefix: '/api/v1' }).get('/dashboard
|
||||
|
||||
const summary: DashboardSummary = {
|
||||
diseaseCount: diseaseCount.length,
|
||||
classificationCount: classificationCount.length,
|
||||
classificationCount: manualClassificationCount.length + imageClassificationCount.length,
|
||||
latestClassification,
|
||||
riskDistribution,
|
||||
};
|
||||
|
||||
+8
@@ -0,0 +1,8 @@
|
||||
declare module 'pngjs' {
|
||||
export class PNG {
|
||||
width: number;
|
||||
height: number;
|
||||
data: Buffer;
|
||||
parse(data: Buffer, callback: (err: Error | null) => void): void;
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user