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/uploader-client.ts

@@ -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;
+}