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.

apps/api/drizzle/0001_cute_lyja.sql

@@ -0,0 +1,17 @@
+CREATE TABLE IF NOT EXISTS "image_classifications" (
+	"id" uuid PRIMARY KEY DEFAULT gen_random_uuid() NOT NULL,
+	"predicted_disease_slug" varchar(80) NOT NULL,
+	"confidence" real NOT NULL,
+	"probabilities" jsonb NOT NULL,
+	"image_url" text NOT NULL,
+	"original_file_name" varchar(240) NOT NULL,
+	"uploader_public_id" varchar(160) NOT NULL,
+	"uploader_payload" jsonb NOT NULL,
+	"created_at" timestamp with time zone DEFAULT now() NOT NULL
+);
+--> statement-breakpoint
+DO $$ BEGIN
+ ALTER TABLE "image_classifications" ADD CONSTRAINT "image_classifications_predicted_disease_slug_disease_catalog_slug_fk" FOREIGN KEY ("predicted_disease_slug") REFERENCES "public"."disease_catalog"("slug") ON DELETE no action ON UPDATE no action;
+EXCEPTION
+ WHEN duplicate_object THEN null;
+END $$;