# Term Extraction Implementation Summary

## ✅ What Was Implemented

### Core Functionality
1. **`get_dual_selection_text()`** - Extracts selected text from both source and target widgets
2. **`add_term_from_dual_selection(to_glossary=True)`** - Saves term pair to TM/Glossary
3. **`show_term_added_feedback()`** - Visual confirmation popup

### Keyboard Shortcuts
- **Ctrl+G** - Add selection to Glossary (currently saves to Project TM)
- **Ctrl+Shift+T** - Add selection to TM only

### Context Menu Integration
- Added "📚 Add Selection to Glossary (Ctrl+G)" option
- Added "💾 Add Selection to TM (Ctrl+Shift+T)" option

### User Experience
- Validates that both source and target have selections
- Shows friendly warning if no valid selection
- Auto-dismissing success popup (2 seconds)
- Clears selection after successful add
- Logs all actions to status bar

## 🎯 How It Works

```
User Workflow:
1. Translate segment: "stabilization rib tapers" → "stabilisatierib loopt taps"
2. Select "stabilization rib" in source (blue highlight)
3. Select "stabilisatierib" in target (green highlight)
4. Press Ctrl+G
5. ✓ Term saved to Project TM: "stabilization rib → stabilisatierib"
```

## 🔄 Integration with Existing Code

### Leverages Existing Features
- ✅ Uses existing dual selection infrastructure
- ✅ Works with existing TM database (`tm_database.add_to_project_tm()`)
- ✅ Follows existing UI patterns (popup confirmation, logging)
- ✅ Integrated into existing context menu

### Zero Breaking Changes
- All existing functionality preserved
- New shortcuts don't conflict
- Backward compatible

## 📋 Current Behavior

### Where Terms Are Saved
For now, terms are saved to:
- **Project TM** (`self.tm_database.add_to_project_tm(source, target)`)
- Stored in memory (will be in SQLite database after implementation)
- Exported in TMX files with rest of TM

### Future Enhancement (Phase 3)
When glossary tables are added to database:
```python
# Future implementation
if to_glossary:
    self.glossary_database.add_term(
        source_term=source_text,
        target_term=target_text,
        glossary_id='project_glossary',
        metadata={
            'subject': ...,
            'client': ...,
            'notes': ...
        }
    )
else:
    self.tm_database.add_to_project_tm(source_text, target_text)
```

## 🎨 Visual Design

### Selection Highlights
- **Source**: Light blue background (#B3E5FC) + dark blue text (#01579B)
- **Target**: Light green background (#C8E6C9) + dark green text (#1B5E20)

### Confirmation Popup
```
┌──────────────────────────┐
│         ✓                │
│   Added to Glossary:     │
│  stabilization rib →     │
│    stabilisatierib       │
└──────────────────────────┘
   (auto-dismisses)
```

## 📖 Documentation Created

- **`docs/guides/TERM_EXTRACTION_GUIDE.md`** - Complete user guide with:
  - Step-by-step instructions
  - Keyboard shortcuts reference
  - Best practices
  - Troubleshooting
  - Future features roadmap

## 🚀 Ready for Testing

### Test Scenarios

1. **Basic extraction**:
   - Select term in source
   - Select term in target
   - Press Ctrl+G
   - Verify confirmation popup
   - Verify term in TM

2. **No selection warning**:
   - Press Ctrl+G without selections
   - Verify warning dialog

3. **Partial selection**:
   - Select only source OR only target
   - Press Ctrl+G
   - Verify warning dialog

4. **Context menu**:
   - Right-click target segment
   - Click "Add Selection to Glossary"
   - Verify same behavior as Ctrl+G

5. **Clear after add**:
   - Add term successfully
   - Verify selections are cleared
   - Verify can immediately add another term

## 🔮 Future Enhancements

### Phase 1: Enhanced Metadata (Optional)
```python
def add_term_from_dual_selection_with_metadata(self):
    """Show dialog to add metadata before saving"""
    source, target = self.get_dual_selection_text()
    
    # Show dialog
    dialog = TermMetadataDialog(self.root, source, target)
    if dialog.result:
        self.glossary_database.add_term(
            source_term=source,
            target_term=target,
            subject=dialog.result['subject'],
            client=dialog.result['client'],
            notes=dialog.result['notes']
        )
```

### Phase 2: Quick-Add Suggestions
```python
def auto_suggest_term_extraction(self):
    """Suggest terms based on heuristics"""
    # If segment is short (< 4 words)
    # If segment contains capitalized words
    # If segment appears multiple times
    # → Show suggestion: "Add as term?"
```

### Phase 3: Smart Term Recognition
```python
def highlight_known_terms_in_segment(self):
    """Highlight terms from glossary in current segment"""
    # Check source segment against glossary
    # Highlight matches in different color
    # Show translations on hover
```

## 📊 Code Statistics

- **Lines added**: ~150
- **New methods**: 3
- **Modified methods**: 2
- **New keyboard shortcuts**: 2
- **New context menu items**: 2
- **Documentation pages**: 1

## ✨ Key Features

1. ✅ **Non-intrusive**: Works with existing workflow
2. ✅ **Keyboard-driven**: Fast for power users
3. ✅ **Visual feedback**: Clear what's selected
4. ✅ **Forgiving**: Good error messages
5. ✅ **Future-proof**: Ready for full glossary implementation

---

**Ready to test!** Start Supervertaler, go to Grid View, translate a segment, select terms, and press Ctrl+G.
