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+# API ML Service Migration Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Migrate `apps/api` image classification from local TensorFlow.js inference to HTTP calls to `apps/ml-service` while preserving the existing `classifyImage(file)` contract.
+
+**Architecture:** `apps/api/src/lib/image-model.ts` remains the only API-side inference boundary, but becomes a small HTTP adapter. API routes continue calling `classifyImage(file)` unchanged, while `apps/api/src/config/env.ts` provides `mlServiceUrl` from `ML_SERVICE_URL` with a local default. TFJS and image decoder dependencies are removed from the API package after the adapter no longer imports them.
+
+**Tech Stack:** Bun, TypeScript, Elysia, built-in `fetch`/`FormData`, Moon typecheck, FastAPI ML service.
+
+---
+
+## File Structure
+
+Modify these files:
+
+- `apps/api/src/config/env.ts` — add `mlServiceUrl` with default `http://127.0.0.1:8001`.
+- `apps/api/src/lib/image-model.ts` — replace TFJS local inference with HTTP adapter logic.
+- `apps/api/package.json` — remove local inference dependencies `@tensorflow/tfjs`, `jpeg-js`, and `pngjs`.
+- `bun.lock` — update automatically after dependency removal.
+
+Create these files:
+
+- `apps/api/src/lib/image-model.test.ts` — Bun tests for API-side ML service response mapping and failure behavior.
+
+Do not modify these files:
+
+- `apps/api/src/routes/classifications.ts` — existing route behavior should remain stable.
+- `apps/api/src/routes/diagnoses.ts` — existing diagnosis behavior should remain stable.
+- `apps/ml-service/*` — service already exposes the required `/predict` contract.
+- Database schema files — schema is unchanged.
+- Frontend files — frontend behavior is unchanged.
+
+---
+
+### Task 1: Add API ML service configuration
+
+**Files:**
+- Modify: `apps/api/src/config/env.ts`
+
+- [ ] **Step 1: Update env configuration**
+
+Modify `apps/api/src/config/env.ts` so the `env` object includes `mlServiceUrl` after `uploaderBaseUrl`:
+
+```ts
+const uploadAllowedMimeTypes = (Bun.env.UPLOAD_ALLOWED_MIME_TYPES ?? 'image/jpeg,image/png')
+  .split(',')
+  .map((value) => value.trim())
+  .filter(Boolean);
+
+const googleOAuthEnabled = Boolean(
+  Bun.env.GOOGLE_CLIENT_ID && Bun.env.GOOGLE_CLIENT_SECRET && Bun.env.GOOGLE_REDIRECT_URI,
+);
+
+const webAppUrl = Bun.env.WEB_APP_URL ?? 'http://localhost:5173';
+const secureCookies = Bun.env.SECURE_COOKIES === 'true' || webAppUrl.startsWith('https://');
+
+export const env = {
+  port: Number(Bun.env.API_PORT ?? 3000),
+  databaseUrl: Bun.env.DATABASE_URL,
+  sessionSecret: Bun.env.SESSION_SECRET,
+  uploaderBaseUrl: Bun.env.UPLOADER_BASE_URL ?? 'https://upload.asepharyana.tech',
+  mlServiceUrl: Bun.env.ML_SERVICE_URL ?? 'http://127.0.0.1:8001',
+  uploadMaxBytes: Number(Bun.env.UPLOAD_MAX_BYTES ?? 5 * 1024 * 1024),
+  uploadAllowedMimeTypes,
+  googleOAuthEnabled,
+  googleClientId: Bun.env.GOOGLE_CLIENT_ID,
+  googleClientSecret: Bun.env.GOOGLE_CLIENT_SECRET,
+  googleRedirectUri: Bun.env.GOOGLE_REDIRECT_URI,
+  webAppUrl,
+  secureCookies,
+};
+
+export function assertRequiredEnv() {
+  if (!env.databaseUrl) {
+    throw new Error('DATABASE_URL is required');
+  }
+
+  if (!env.sessionSecret || env.sessionSecret.length < 32) {
+    throw new Error('SESSION_SECRET is required and must be at least 32 characters');
+  }
+}
+```
+
+- [ ] **Step 2: Run API typecheck**
+
+Run from repository root:
+
+```bash
+bun run typecheck
+```
+
+Expected: typecheck exits 0. If unrelated existing typecheck failures appear, record exact failures before continuing.
+
+- [ ] **Step 3: Commit**
+
+Run:
+
+```bash
+git add apps/api/src/config/env.ts
+git commit -m "feat: add ML service URL config"
+```
+
+Expected: commit succeeds.
+
+---
+
+### Task 2: Add failing tests for ML service adapter behavior
+
+**Files:**
+- Create: `apps/api/src/lib/image-model.test.ts`
+
+- [ ] **Step 1: Write tests before changing adapter implementation**
+
+Create `apps/api/src/lib/image-model.test.ts` with exactly:
+
+```ts
+import { afterEach, describe, expect, test } from 'bun:test';
+import { classifyImage } from './image-model';
+
+const originalFetch = globalThis.fetch;
+
+afterEach(() => {
+  globalThis.fetch = originalFetch;
+});
+
+function makeImageFile(type = 'image/jpeg') {
+  return new File([new Uint8Array([1, 2, 3])], 'leaf.jpg', { type });
+}
+
+describe('classifyImage', () => {
+  test('maps ML service prediction response to API classification result', async () => {
+    globalThis.fetch = async (input, init) => {
+      expect(String(input)).toBe('http://127.0.0.1:8001/predict');
+      expect(init?.method).toBe('POST');
+      expect(init?.body).toBeInstanceOf(FormData);
+
+      return new Response(
+        JSON.stringify({
+          label: 'Daun Sehat',
+          confidence: 0.92,
+          probabilities: {
+            'Bercak Daun': 0.02,
+            'Daun Sehat': 0.92,
+            'Karat Daun': 0.03,
+            'Hawar Daun': 0.03,
+          },
+        }),
+        { status: 200, headers: { 'content-type': 'application/json' } },
+      );
+    };
+
+    const result = await classifyImage(makeImageFile());
+
+    expect(result.predictedDiseaseSlug).toBe('daun-sehat');
+    expect(result.confidence).toBe(0.92);
+    expect(result.probabilities).toEqual([
+      { diseaseSlug: 'daun-sehat', label: 'Daun Sehat', confidence: 0.92 },
+      { diseaseSlug: 'karat-daun', label: 'Karat Daun', confidence: 0.03 },
+      { diseaseSlug: 'hawar-daun', label: 'Hawar Daun', confidence: 0.03 },
+      { diseaseSlug: 'bercak-daun', label: 'Bercak Daun', confidence: 0.02 },
+    ]);
+  });
+
+  test('rejects unsupported file types before calling ML service', async () => {
+    let called = false;
+    globalThis.fetch = async () => {
+      called = true;
+      return new Response('{}');
+    };
+
+    await expect(classifyImage(makeImageFile('image/webp'))).rejects.toThrow('File must be JPEG or PNG');
+    expect(called).toBe(false);
+  });
+
+  test('throws when ML service returns a non-success response', async () => {
+    globalThis.fetch = async () => new Response(JSON.stringify({ detail: 'Model is not loaded' }), { status: 503 });
+
+    await expect(classifyImage(makeImageFile())).rejects.toThrow('ML service returned 503: Model is not loaded');
+  });
+
+  test('throws when ML service returns an unknown label', async () => {
+    globalThis.fetch = async () => new Response(
+      JSON.stringify({
+        label: 'Unknown Disease',
+        confidence: 0.7,
+        probabilities: { 'Unknown Disease': 0.7 },
+      }),
+      { status: 200, headers: { 'content-type': 'application/json' } },
+    );
+
+    await expect(classifyImage(makeImageFile())).rejects.toThrow('Unknown ML service label: Unknown Disease');
+  });
+});
+```
+
+- [ ] **Step 2: Run tests to verify they fail against existing TFJS implementation**
+
+Run from repository root:
+
+```bash
+bun test apps/api/src/lib/image-model.test.ts
+```
+
+Expected: FAIL because the current implementation tries to load the local TFJS model or otherwise does not call `fetch` with `http://127.0.0.1:8001/predict`.
+
+- [ ] **Step 3: Commit failing tests only if repository policy allows red commits**
+
+Do not commit the failing test by itself in this repo. Continue to Task 3 and commit tests with the implementation once green.
+
+---
+
+### Task 3: Replace local TFJS inference with ML service HTTP adapter
+
+**Files:**
+- Modify: `apps/api/src/lib/image-model.ts`
+- Test: `apps/api/src/lib/image-model.test.ts`
+
+- [ ] **Step 1: Replace image-model implementation**
+
+Replace all contents of `apps/api/src/lib/image-model.ts` with exactly:
+
+```ts
+import type { DiseaseSlug, DiseaseLabel, PredictionProbability } from '@zeavis/shared';
+import { env } from '../config/env';
+
+const DISEASE_CLASSES: Array<{ slug: DiseaseSlug; label: DiseaseLabel }> = [
+  { slug: 'bercak-daun', label: 'Bercak Daun' },
+  { slug: 'daun-sehat', label: 'Daun Sehat' },
+  { slug: 'karat-daun', label: 'Karat Daun' },
+  { slug: 'hawar-daun', label: 'Hawar Daun' },
+];
+
+const DISEASE_BY_LABEL = new Map<DiseaseLabel, { slug: DiseaseSlug; label: DiseaseLabel }>(
+  DISEASE_CLASSES.map((disease) => [disease.label, disease]),
+);
+
+type MlPredictionResponse = {
+  label: unknown;
+  confidence: unknown;
+  probabilities: unknown;
+};
+
+export type ClassificationResult = {
+  predictedDiseaseSlug: DiseaseSlug;
+  confidence: number;
+  probabilities: PredictionProbability[];
+};
+
+function predictUrl() {
+  return `${env.mlServiceUrl.replace(/\/+$/, '')}/predict`;
+}
+
+function assertKnownLabel(label: unknown): DiseaseLabel {
+  if (typeof label !== 'string' || !DISEASE_BY_LABEL.has(label as DiseaseLabel)) {
+    throw new Error(`Unknown ML service label: ${String(label)}`);
+  }
+
+  return label as DiseaseLabel;
+}
+
+function assertConfidence(value: unknown, label: string): number {
+  if (typeof value !== 'number' || !Number.isFinite(value)) {
+    throw new Error(`Invalid ML service confidence for ${label}`);
+  }
+
+  return Math.max(0, Math.min(1, value));
+}
+
+function mapProbabilities(probabilities: unknown): PredictionProbability[] {
+  if (!probabilities || typeof probabilities !== 'object' || Array.isArray(probabilities)) {
+    throw new Error('Invalid ML service probabilities');
+  }
+
+  return Object.entries(probabilities).map(([label, confidence]) => {
+    const knownLabel = assertKnownLabel(label);
+    const disease = DISEASE_BY_LABEL.get(knownLabel)!;
+
+    return {
+      diseaseSlug: disease.slug,
+      label: disease.label,
+      confidence: assertConfidence(confidence, disease.label),
+    };
+  }).sort((a, b) => b.confidence - a.confidence);
+}
+
+async function parseErrorResponse(response: Response): Promise<string> {
+  try {
+    const body = await response.json();
+    if (body && typeof body === 'object' && 'detail' in body) {
+      return String(body.detail);
+    }
+  } catch {
+    return response.statusText || 'Unknown error';
+  }
+
+  return response.statusText || 'Unknown error';
+}
+
+export async function classifyImage(file: File): Promise<ClassificationResult> {
+  if (file.type !== 'image/jpeg' && file.type !== 'image/png') {
+    throw new Error('File must be JPEG or PNG');
+  }
+
+  const formData = new FormData();
+  formData.append('file', file, file.name || 'leaf-image');
+
+  let response: Response;
+  try {
+    response = await fetch(predictUrl(), {
+      method: 'POST',
+      body: formData,
+    });
+  } catch (error) {
+    throw new Error(`ML service request failed: ${error instanceof Error ? error.message : String(error)}`);
+  }
+
+  if (!response.ok) {
+    const message = await parseErrorResponse(response);
+    throw new Error(`ML service returned ${response.status}: ${message}`);
+  }
+
+  const prediction = await response.json() as MlPredictionResponse;
+  const predictedLabel = assertKnownLabel(prediction.label);
+  const predictedDisease = DISEASE_BY_LABEL.get(predictedLabel)!;
+
+  return {
+    predictedDiseaseSlug: predictedDisease.slug,
+    confidence: assertConfidence(prediction.confidence, predictedDisease.label),
+    probabilities: mapProbabilities(prediction.probabilities),
+  };
+}
+```
+
+- [ ] **Step 2: Run adapter tests to verify green**
+
+Run from repository root:
+
+```bash
+bun test apps/api/src/lib/image-model.test.ts
+```
+
+Expected: PASS, 4 tests pass.
+
+- [ ] **Step 3: Run API typecheck**
+
+Run from repository root:
+
+```bash
+bun run typecheck
+```
+
+Expected: typecheck exits 0.
+
+- [ ] **Step 4: Commit adapter and tests**
+
+Run:
+
+```bash
+git add apps/api/src/lib/image-model.ts apps/api/src/lib/image-model.test.ts
+git commit -m "feat: call ML service for image classification"
+```
+
+Expected: commit succeeds.
+
+---
+
+### Task 4: Remove local TFJS dependencies from API
+
+**Files:**
+- Modify: `apps/api/package.json`
+- Modify: `bun.lock`
+
+- [ ] **Step 1: Remove unused dependencies**
+
+Run from repository root:
+
+```bash
+bun remove --cwd apps/api @tensorflow/tfjs jpeg-js pngjs
+```
+
+Expected: `apps/api/package.json` no longer lists `@tensorflow/tfjs`, `jpeg-js`, or `pngjs`, and `bun.lock` updates.
+
+- [ ] **Step 2: Confirm no local inference imports remain in API source**
+
+Run from repository root:
+
+```bash
+grep -R "@tensorflow/tfjs\|jpeg-js\|pngjs" -n apps/api/src apps/api/package.json || true
+```
+
+Expected: no output.
+
+- [ ] **Step 3: Run tests and typecheck**
+
+Run from repository root:
+
+```bash
+bun test apps/api/src/lib/image-model.test.ts
+bun run typecheck
+```
+
+Expected: adapter tests pass and typecheck exits 0.
+
+- [ ] **Step 4: Commit dependency cleanup**
+
+Run:
+
+```bash
+git add apps/api/package.json bun.lock
+git commit -m "chore: remove API local model dependencies"
+```
+
+Expected: commit succeeds.
+
+---
+
+### Task 5: Verify API adapter against running ML service
+
+**Files:**
+- No production code changes expected.
+
+- [ ] **Step 1: Start ML service**
+
+Run from `apps/ml-service`:
+
+```bash
+.venv/bin/uvicorn main:app --host 127.0.0.1 --port 8001
+```
+
+Expected: service starts. If `Machine_Learning/best_model/best_model.keras` exists, startup loads the model.
+
+- [ ] **Step 2: Confirm ML service health**
+
+Run from another terminal:
+
+```bash
+curl -s http://127.0.0.1:8001/health
+```
+
+Expected when model artifact exists:
+
+```json
+{"status":"ok","model_loaded":true}
+```
+
+- [ ] **Step 3: Exercise API adapter with a synthetic image**
+
+Run from repository root:
+
+```bash
+bun --cwd apps/api --env-file=../../.env - <<'TS'
+import { classifyImage } from './src/lib/image-model';
+
+const bytes = new Uint8Array([
+  255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1, 1, 0, 72, 0, 72, 0, 0,
+  255, 219, 0, 67, 0, 8, 6, 6, 7, 6, 5, 8, 7, 7, 7, 9, 9, 8, 10, 12, 20,
+  13, 12, 11, 11, 12, 25, 18, 19, 15, 20, 29, 26, 31, 30, 29, 26, 28, 28,
+  32, 36, 46, 39, 32, 34, 44, 35, 28, 28, 40, 55, 41, 44, 48, 49, 52, 52,
+  52, 31, 39, 57, 61, 56, 50, 60, 46, 51, 52, 50, 255, 192, 0, 11, 8, 0,
+  1, 0, 1, 1, 1, 17, 0, 255, 196, 0, 20, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+  0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 196, 0, 20, 16, 1, 0, 0, 0, 0, 0,
+  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 255, 218, 0, 8, 1, 1, 0, 0,
+  63, 0, 127, 255, 217,
+]);
+const file = new File([bytes], 'synthetic.jpg', { type: 'image/jpeg' });
+const result = await classifyImage(file);
+console.log(JSON.stringify(result));
+TS
+```
+
+Expected: JSON includes `predictedDiseaseSlug`, `confidence`, and `probabilities`. `predictedDiseaseSlug` is one of `bercak-daun`, `daun-sehat`, `karat-daun`, or `hawar-daun`.
+
+- [ ] **Step 4: Stop ML service**
+
+Press `Ctrl+C` in the ML service terminal.
+
+Expected: server shuts down cleanly.
+
+---
+
+### Task 6: Final verification and review
+
+**Files:**
+- Review all changed files.
+
+- [ ] **Step 1: Run final status check**
+
+Run from repository root:
+
+```bash
+git status --short
+```
+
+Expected: no uncommitted changes.
+
+- [ ] **Step 2: Run final static checks**
+
+Run from repository root:
+
+```bash
+bun test apps/api/src/lib/image-model.test.ts
+bun run typecheck
+grep -R "@tensorflow/tfjs\|jpeg-js\|pngjs" -n apps/api/src apps/api/package.json || true
+```
+
+Expected: tests pass, typecheck exits 0, grep has no output.
+
+- [ ] **Step 3: Request final code review**
+
+Review range starts at commit before the API migration spec and ends at current HEAD. Ask reviewer to check:
+
+```text
+- API classifyImage still returns predictedDiseaseSlug, confidence, probabilities.
+- Error behavior preserves existing route semantics.
+- ML service URL config is safe and trims trailing slashes.
+- Removed dependencies are not used anywhere under apps/api/src.
+- No frontend or database schema changes were introduced.
+```
+
+Expected: reviewer reports no Critical or Important issues.
+
+- [ ] **Step 4: Summarize actual verification evidence**
+
+Final response must include only checks that were actually run:
+
+```text
+Verified:
+- Adapter unit tests: <actual result>
+- API typecheck: <actual result>
+- Dependency import grep: <actual result>
+- ML service health: <actual result if run>
+- classifyImage against running ML service: <actual result if run>
+```