refactor(vision): share Python config between detection and training

Extracts TRAINING_PYTHON, TRAINING_SCRIPTS_DIR, and PYTHON_ENV to a
shared config.ts file. Both hardware detection and training now import
from the same source, making it impossible for them to diverge.

This ensures hardware detection always shows exactly what training
will use.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

apps/web/src/app/api/vision-training/config.ts

@@ -0,0 +1,34 @@
+import path from 'path'
+
+/**
+ * Shared configuration for vision training.
+ *
+ * CRITICAL: Both hardware detection and training MUST use the same Python
+ * environment so that hardware detection accurately reflects what training
+ * will actually use.
+ */
+
+const cwd = process.cwd()
+
+/**
+ * Path to the Python executable used for ALL vision training operations.
+ * This venv has tensorflow-metal installed for Apple Silicon GPU support.
+ */
+export const TRAINING_PYTHON = path.join(
+  cwd,
+  'scripts/train-column-classifier/.venv/bin/python'
+)
+
+/**
+ * Path to the training scripts directory
+ */
+export const TRAINING_SCRIPTS_DIR = path.join(cwd, 'scripts/train-column-classifier')
+
+/**
+ * Common environment variables for Python subprocesses
+ */
+export const PYTHON_ENV = {
+  ...process.env,
+  PYTHONUNBUFFERED: '1',
+  PYTHONWARNINGS: 'ignore::FutureWarning', // Suppress keras warning
+}

apps/web/src/app/api/vision-training/hardware/route.ts

@@ -1,5 +1,6 @@
 import { spawn } from 'child_process'
 import path from 'path'
+import { PYTHON_ENV, TRAINING_PYTHON, TRAINING_SCRIPTS_DIR } from '../config'
 
 /**
  * Hardware detection result from Python/TensorFlow
@@ -38,23 +39,17 @@ export async function GET(): Promise<Response> {
 
   try {
     const cwd = path.resolve(process.cwd())
-    const scriptPath = path.join(cwd, 'scripts/train-column-classifier/detect_hardware.py')
+    const scriptPath = path.join(TRAINING_SCRIPTS_DIR, 'detect_hardware.py')
 
     const result = await new Promise<HardwareInfo>((resolve, reject) => {
       let stdout = ''
       let stderr = ''
       let hasError = false
 
-      // Use the venv Python with tensorflow-metal for proper GPU detection
-      const venvPython = path.join(cwd, 'scripts/train-column-classifier/.venv/bin/python')
-
-      const childProcess = spawn(venvPython, [scriptPath], {
+      // Use shared Python config - same as training uses
+      const childProcess = spawn(TRAINING_PYTHON, [scriptPath], {
         cwd,
-        env: {
-          ...process.env,
-          PYTHONUNBUFFERED: '1',
-          PYTHONWARNINGS: 'ignore::FutureWarning', // Suppress keras warning
-        },
+        env: PYTHON_ENV,
       })
 
       childProcess.stdout?.on('data', (data: Buffer) => {

apps/web/src/app/api/vision-training/train/route.ts

@@ -1,5 +1,6 @@
 import { spawn, type ChildProcess } from 'child_process'
 import path from 'path'
+import { PYTHON_ENV, TRAINING_PYTHON } from '../config'
 
 /**
  * Training configuration options
@@ -94,17 +95,12 @@ export async function POST(request: Request): Promise<Response> {
         },
       })
 
-      // Spawn Python process using the venv with tensorflow-metal for GPU support
+      // Spawn Python process - uses shared config so hardware detection matches
       const cwd = path.resolve(process.cwd())
-      const venvPython = path.join(cwd, 'scripts/train-column-classifier/.venv/bin/python')
 
-      activeProcess = spawn(venvPython, args, {
+      activeProcess = spawn(TRAINING_PYTHON, args, {
         cwd,
-        env: {
-          ...process.env,
-          PYTHONUNBUFFERED: '1',
-          PYTHONWARNINGS: 'ignore::FutureWarning', // Suppress keras warning
-        },
+        env: PYTHON_ENV,
       })
 
       // Handle stdout (JSON progress events)