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/http-errors.ts

@@ -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,