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/package.json

@@ -11,9 +11,12 @@
     "db:migrate": "drizzle-kit migrate"
   },
   "dependencies": {
+    "@tensorflow/tfjs": "4.22.0",
     "@zeavis/shared": "workspace:*",
     "drizzle-orm": "^0.36.4",
     "elysia": "^1.1.25",
+    "jpeg-js": "0.4.4",
+    "pngjs": "7.0.0",
     "postgres": "^3.4.5"
   },
   "devDependencies": {