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/src/lib/disease-mappers.ts

@@ -0,0 +1,17 @@
+import type { DiseaseCatalogItem, DiseaseSlug, DiseaseLabel, RiskLevel } from '@zeavis/shared';
+import type { diseaseCatalog } from '../db/schema';
+
+export function toDisease(row: typeof diseaseCatalog.$inferSelect): DiseaseCatalogItem {
+  return {
+    slug: row.slug as DiseaseSlug,
+    label: row.label as DiseaseLabel,
+    commonName: row.commonName,
+    summary: row.summary,
+    description: row.description,
+    symptoms: row.symptoms,
+    recommendations: row.recommendations,
+    riskLevel: row.riskLevel as RiskLevel,
+    accentColor: row.accentColor,
+    displayOrder: row.displayOrder,
+  };
+}