feat: add language selection for conversations and grammar analysis

[tbrachmann]

Summary

• Adds language selection UI supporting English, Spanish, and German
• Implements separate conversation language (for AI responses) and analysis language (for grammar feedback) options
• Updates AI service to generate language-specific responses and grammar analysis
• Adds database migrations for new language fields on Conversation model
• Creates comprehensive test coverage for all language functionality

Key Changes

• Language Selection UI: New template with clean, accessible interface for choosing both conversation and analysis languages
• Database Schema: Added language and analysis_language fields to Conversation model with migration support
• AI Service Enhancement: Multi-language support for chat responses, grammar analysis, and conversation reports
• Language-Specific Features: Conversation starters now use appropriate language based on conversation settings
• Comprehensive Testing: Added 52 test cases covering all language functionality scenarios

Test Plan

• All existing tests pass (52/52)
• Language selection UI renders correctly for authenticated users
• New conversations created with selected languages
• AI responses generated in correct conversation language
• Grammar analysis provided in selected analysis language
• Language validation handles invalid inputs gracefully
• Conversation starters display in appropriate language

🤖 Generated with Claude Code

[claude]

🔍 Code Review - Language Selection Feature

✅ Overall Assessment

This is a well-implemented feature that adds multilingual support to the chat application. The code demonstrates good Django practices, proper async usage, and comprehensive test coverage.

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🏆 Strengths

1. Code Quality & Architecture

• Excellent separation of concerns: Language-specific agents are created dynamically rather than stored as instance variables
• Proper async implementation: All AI service methods correctly use async/await patterns
• Clean database design: New language fields with appropriate choices and help text
• Type hints: Good use of type annotations throughout the codebase

2. User Experience

• Intuitive UI: The language selection template (chat/templates/chat/language_selection.html) provides a clean, accessible interface
• Dual language support: Separate conversation and analysis languages is a thoughtful UX decision
• Mobile-responsive design: CSS includes proper mobile breakpoints

3. Test Coverage

• Comprehensive testing: 52 test cases covering all language functionality scenarios
• Multiple test categories: UI tests, model tests, AI service tests, and integration tests
• Edge case coverage: Tests handle invalid inputs and error conditions

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🔧 Technical Improvements

1. Performance Considerations

# Current implementation creates new agents on each call
def _create_grammar_agent(self, language_code: str) -> Agent:
    return Agent(model=self.model, system_prompt=system_prompt)

# Consider: Agent caching for better performance
@lru_cache(maxsize=6)  # 3 languages × 2 agent types
def _get_cached_agent(self, agent_type: str, language_code: str) -> Agent:
    # Cache agents to avoid recreating them

Impact: Medium - Could improve response times for frequently used language combinations

2. Input Validation

# Missing validation in ai_service.py
def _get_language_name(self, language_code: str) -> str:
    language_map = {'en': 'English', 'es': 'Spanish', 'de': 'German'}
    return language_map.get(language_code, 'English')  # Silent fallback

Suggestion: Log warnings or raise exceptions for invalid language codes to aid debugging

3. Constants Management

# Duplicated language choices in models.py and views.py
LANGUAGE_CHOICES = [('en', 'English'), ('es', 'Spanish'), ('de', 'German')]
CONVERSATION_STARTERS = {'en': [...], 'es': [...], 'de': [...]}

Suggestion: Move to a shared constants file (e.g., chat/constants.py)

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🛡️ Security Review

✅ Secure Practices

• Proper CSRF protection in forms
• No hardcoded secrets or API keys
• Input sanitization through Django ORM
• Authenticated views only

⚠️ Minor Considerations

• Language codes are user-controlled but properly validated against LANGUAGE_CHOICES
• AI prompts include user input but are properly structured

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🐛 Potential Issues

1. Migration Dependencies

The migrations look correct, but ensure proper deployment order:

# Apply in sequence
python manage.py migrate chat 0004
python manage.py migrate chat 0005

2. Template Accessibility

The HTML template could benefit from:

<!-- Add ARIA labels for better accessibility -->
<input type="radio" aria-describedby="conversation-lang-help" ...>
<div id="conversation-lang-help" class="sr-only">Choose language for AI responses</div>

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📊 Test Coverage Analysis

Excellent coverage across:

• ✅ Language selection UI rendering
• ✅ Conversation creation with language settings
• ✅ AI service language-specific responses
• ✅ Grammar analysis in different languages
• ✅ Database model validation
• ✅ Error handling and edge cases

Test quality highlights:

• Proper use of TransactionTestCase for async operations
• Good mocking strategy for AI service calls
• Both unit and integration test coverage

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🚀 Deployment Readiness

This feature is production-ready with:

• ✅ Database migrations included
• ✅ Backward compatibility maintained
• ✅ Comprehensive test suite (52/52 passing)
• ✅ No breaking changes to existing functionality

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💡 Future Enhancements

1. Language Detection: Auto-detect user's preferred language from browser settings
2. RTL Support: Consider layout adjustments for Arabic/Hebrew if added later
3. Language Learning Analytics: Track progress per language over time

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🎯 Recommendation

APPROVE ✅ - This is a high-quality implementation that follows the project's standards. The minor suggestions above are optimizations rather than blockers.

Great work on the comprehensive testing and thoughtful UX design!