docs: Add Part 6 - Community Feedback and Model Retraining

Documents the full ML feedback loop:
- How user corrections trigger uploads (by label: Foil_, Condition_, Identity_)
- User opt-in requirement and AppConfig.isTrainingOptIn
- Firebase Storage rules (training write-only, models read-only)
- How to download collected data via Firebase CLI
- Image review and sorting process before retraining
- Create ML retraining workflow
- OTA model push via Firebase Storage models/ folder
- Recommended retraining schedule

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

README.md

@@ -405,7 +405,109 @@ Output goes to `Set_Symbol_Training/`. Drag this folder into Create ML → Image
 
 ---
 
-## Part 6: Backend & Security
+## Part 6: Community Feedback & Model Retraining
+
+The app has a built-in pipeline that collects user corrections and uses them to improve the ML models over time. This section explains how it works end-to-end.
+
+### How the Feedback System Works
+
+There are two data collection paths:
+
+**Path 1 — User Corrections (Community Data)**
+When a user corrects a mis-scan (wrong card identity, wrong foil type, or wrong condition), the app automatically uploads the cropped card image to Firebase Storage — but only if the user has opted in.
+
+The upload destination is determined by what was corrected:
+
+| What Changed | Firebase Storage Path | Used to Retrain |
+| :--- | :--- | :--- |
+| Card name or set | `training/Identity_<SETCODE>_<CollectorNum>/` | `cards.json` (re-fingerprint) |
+| Foil type | `training/Foil_<FoilType>/` | `IYmtgFoilClassifier.mlmodel` |
+| Condition grade | `training/Condition_<Grade>/` | `IYmtgConditionClassifier.mlmodel` |
+
+Example: A user corrects a card that was identified as "Traditional" foil to "Etched". The image is uploaded to `training/Foil_Etched/<UUID>.jpg`.
+
+**Path 2 — Dev Mode (Your Own Device)**
+When the `ENABLE_DEV_MODE` build flag is active and you tap the logo header 5 times, every raw scan frame is saved locally to `Documents/RawTrainingData/` on the device. Sync this folder to your Mac via Xcode's Devices window or Files app to retrieve images.
+
+### User Opt-In
+
+Users must explicitly opt in before any images are uploaded. The opt-in state is stored in `AppConfig.isTrainingOptIn` (backed by `UserDefaults`).
+
+You must expose a toggle in your app's Settings/Library UI that sets `AppConfig.isTrainingOptIn = true/false`. The app description mentions this as the "Community Data Initiative" — users are told their corrections improve the AI for everyone.
+
+**Firebase Authentication note:** `TrainingUploader` only uploads when `FirebaseApp.app() != nil` — meaning Firebase must be configured (`GoogleService-Info.plist` present) for community uploads to work. The app functions without Firebase, but no feedback is collected in that mode.
+
+### Firebase Storage Rules
+
+The rules in `IYmtg_App_iOS/Firebase/storage.rules` enforce:
+- `training/` — authenticated users can **write only** (upload corrections). No user can read others' images.
+- `models/` — anyone can **read** (required for OTA model downloads). Write access is developer-only via the Firebase Console.
+
+### Downloading Collected Training Data
+
+1.  Go to the **Firebase Console → Storage → training/**
+2.  You will see folders named by label (e.g., `Foil_Etched/`, `Condition_Near Mint (NM)/`)
+3.  Download all images in each folder — use the Firebase CLI for bulk downloads:
+    ```bash
+    # Install Firebase CLI if needed
+    npm install -g firebase-tools
+    firebase login
+
+    # Download all training data
+    firebase storage:cp gs://<your-bucket>/training ./downloaded_training --recursive
+    ```
+4.  You now have a folder of user-contributed cropped card images, organized by label.
+
+### Reviewing and Sorting Downloaded Images
+
+**Do not skip this step.** User uploads can include blurry photos, wrong cards, or bad crops. Review each image before adding it to your training set.
+
+1.  Open each label folder from the download.
+2.  Delete any images that are: blurry, poorly cropped, show background, or are clearly wrong.
+3.  Move the accepted images into the corresponding `IYmtg_Training/` subfolder:
+
+| Downloaded Folder | Move to Training Folder |
+| :--- | :--- |
+| `Foil_Traditional/` | `IYmtg_Training/Foil_Data/Traditional/` |
+| `Foil_Etched/` | `IYmtg_Training/Foil_Data/Etched/` |
+| `Foil_<Type>/` | `IYmtg_Training/Foil_Data/<Type>/` |
+| `Condition_<Grade>/` | Inspect condition grade — map to `Condition_Data/` subfolder by damage type visible |
+
+> **Identity corrections** (`training/Identity_*/`) are not used to retrain ML models. They indicate that the visual fingerprint for that card may be wrong or ambiguous. Review these separately and consider re-running the Builder for those specific cards.
+
+### Retraining the Models
+
+Once you have added new images to `IYmtg_Training/`:
+
+1.  Open **Create ML** on your Mac.
+2.  Open your existing project for the model you want to update (e.g., `IYmtgFoilClassifier`).
+3.  The new images in the training folders will be picked up automatically.
+4.  Click **Train**. Create ML will train incrementally on the expanded dataset.
+5.  Evaluate the results — check accuracy on the **Validation** tab. Aim for >90% accuracy before shipping.
+6.  Export the updated `.mlmodel` file.
+
+### Pushing the Updated Model via OTA
+
+You do not need an App Store update to ship a new model version. Use Firebase Storage:
+
+1.  In the **Firebase Console → Storage**, navigate to the `models/` folder.
+2.  Upload your new `.mlmodel` file with the **exact same filename** (e.g., `IYmtgFoilClassifier.mlmodel`).
+3.  On the next app launch, `ModelManager` detects the newer version, downloads and compiles it, and swaps it in automatically.
+
+> **Important:** The new model takes effect on the **next app launch after download**, not immediately. Users may need to relaunch once.
+
+### Recommended Retraining Schedule
+
+| Trigger | Action |
+| :--- | :--- |
+| 50+ new correction images accumulated | Review, sort, retrain affected model, push OTA |
+| New MTG set released with new foil type | Add training folder, acquire cards, retrain FoilClassifier |
+| New MTG set released | Rebuild `cards.json` via `weekly_update.sh` |
+| Significant accuracy complaints from users | Download corrections, review, retrain |
+
+---
+
+## Part 7: Backend & Security
 
 ### Cloud Storage Architecture
 The app uses a two-tier cloud strategy:
@@ -449,7 +551,7 @@ Ensure `PrivacyInfo.xcprivacy` is included in the app target to satisfy Apple's
 
 ---
 
-## Part 7: App Configuration
+## Part 8: App Configuration
 
 **CRITICAL:** Edit `IYmtg_App_iOS/AppConfig.swift` before building to ensure payments and support work correctly:
 1.  Set `contactEmail` to your real email address (required by Scryfall API policy).
@@ -458,7 +560,7 @@ Ensure `PrivacyInfo.xcprivacy` is included in the app target to satisfy Apple's
 
 ---
 
-## Part 8: Development Mode
+## Part 9: Development Mode
 
 To enable saving raw training images during scanning:
 1.  Add the compilation flag `ENABLE_DEV_MODE` in Xcode Build Settings → Swift Compiler → Active Compilation Conditions.
@@ -468,7 +570,7 @@ Saved images appear in `Documents/DevImages/` and can be used to supplement your
 
 ---
 
-## Part 9: Testing
+## Part 10: Testing
 
 The project includes a unit test suite in `IYmtgTests.swift`.
 
@@ -484,7 +586,7 @@ The project includes a unit test suite in `IYmtgTests.swift`.
 
 ---
 
-## Part 10: Release Checklist
+## Part 11: Release Checklist
 
 Perform these steps before submitting to the App Store.