Comprehensive improvements and modernization

- Upgrade to Gradio 5.35.0 with proper message formatting
- Add comprehensive CLAUDE.md development documentation
- Improve security with environment-based access code storage
- Enhance README with detailed feature descriptions and architecture
- Add vector RAG testing capabilities and documentation
- Update dependency management and version constraints
- Remove generated zip files and update .gitignore
- Improve UI layout and template selection workflow
- Fix access control implementation with global state management
- Add development testing commands and component isolation

.gitignore

@@ -23,4 +23,7 @@ ENV/
 Thumbs.db
 
 # Logs
-*.log
+*.log
+
+# Generated files
+*.zip

CLAUDE.md

@@ -0,0 +1,155 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Chat UI Helper is a Gradio-based tool for generating and configuring chat interfaces for HuggingFace Spaces. It creates deployable packages with custom assistants, web scraping capabilities, and optional vector RAG functionality.
+
+## Core Architecture
+
+### Main Application (`app.py`)
+- **Primary Interface**: Two-tab Gradio application - "Spaces Configuration" for generating chat interfaces and "Chat Support" for getting help
+- **Template System**: `SPACE_TEMPLATE` generates complete HuggingFace Space apps with embedded configuration
+- **Web Scraping**: Integration with Crawl4AI for URL content fetching and context grounding
+- **Vector RAG**: Optional document processing pipeline for course materials and knowledge bases
+
+### Document Processing Pipeline
+- **RAGTool** (`rag_tool.py`): Main orchestrator for document upload and processing
+- **DocumentProcessor** (`document_processor.py`): Handles PDF, DOCX, TXT, MD file parsing and chunking
+- **VectorStore** (`vector_store.py`): FAISS-based similarity search and embedding management
+- **ScrapingService** (`scraping_service.py`): Crawl4AI integration for web content extraction
+
+### Package Generation
+The tool generates complete HuggingFace Spaces with:
+- `app.py`: Chat interface with OpenRouter API integration
+- `requirements.txt`: Gradio 5.x and dependencies 
+- `README.md`: Deployment instructions with security setup
+- `config.json`: Configuration backup
+- Optional embedded RAG data for document-aware responses
+
+## Development Commands
+
+### Running the Application
+```bash
+python app.py
+```
+
+### Testing Vector Database Functionality
+```bash
+python test_vector_db.py
+```
+
+### Testing Individual Components
+```bash
+# Test document processing only
+python -c "from test_vector_db import test_document_processing; test_document_processing()"
+
+# Test vector store only
+python -c "from test_vector_db import test_vector_store; test_vector_store()"
+
+# Test full RAG pipeline
+python -c "from test_vector_db import test_rag_tool; test_rag_tool()"
+```
+
+### Dependencies Management
+```bash
+pip install -r requirements.txt
+```
+
+### Key Dependencies
+- **Gradio 5.35.0+**: Main UI framework
+- **Crawl4AI 0.4.0+**: Web scraping with async support
+- **sentence-transformers**: Embeddings for RAG (optional)
+- **faiss-cpu**: Vector similarity search (optional)
+- **PyMuPDF**: PDF text extraction (optional)
+- **python-docx**: DOCX document processing (optional)
+- **beautifulsoup4**: HTML parsing for web scraping
+- **python-dotenv**: Environment variable management
+
+## Configuration Patterns
+
+### Template Variables
+Generated spaces use these template substitutions:
+- `{name}`, `{description}`: Basic space metadata
+- `{system_prompt}`: Combined assistant configuration
+- `{model}`: OpenRouter model selection
+- `{grounding_urls}`: Static URL list for context
+- `{enable_dynamic_urls}`: Runtime URL fetching capability
+- `{enable_vector_rag}`: Document search integration
+- `{rag_data_json}`: Serialized embeddings and chunks
+
+### Access Control
+- Environment variable `SPACE_ACCESS_CODE` for student access control
+- Global state management for session-based access in generated spaces
+- Security-first approach storing credentials as HuggingFace Spaces secrets
+
+### RAG Integration
+- Modular design with optional imports (`HAS_RAG` flag in app.py:23)
+- FAISS index serialization for deployment portability
+- 10MB file size limits with validation
+- Semantic chunking (800 chars, 100 overlap) for optimal retrieval
+- Graceful degradation when vector dependencies unavailable
+
+## Architecture Notes
+
+### State Management
+- Extensive use of `gr.State()` for maintaining session data
+- Global variables for access control in generated templates
+- URL content caching to prevent redundant web requests
+
+### Template Generation Pattern
+All generated HuggingFace Spaces follow consistent structure:
+1. Configuration section with environment variable loading
+2. Web scraping functions (sync/async Crawl4AI wrappers)  
+3. RAG context retrieval (if enabled)
+4. OpenRouter API integration with conversation history
+5. Gradio ChatInterface with access control
+
+### Error Handling
+- Graceful degradation when optional dependencies unavailable
+- Comprehensive validation for file uploads and URL processing
+- User-friendly error messages with specific guidance
+
+### Security Considerations
+- Never embed API keys or access codes in generated templates
+- Environment variable pattern for all sensitive configuration
+- Input validation for uploaded files and URL processing
+- Content length limits for web scraping operations
+
+### Dependency Management Pattern
+The codebase uses conditional imports with feature flags:
+```python
+try:
+    from rag_tool import RAGTool
+    HAS_RAG = True
+except ImportError:
+    HAS_RAG = False
+    RAGTool = None
+```
+This pattern allows the main application to function even when optional vector database dependencies are unavailable.
+
+## Important Implementation Details
+
+### Gradio 5.x Compatibility
+- Uses `type="messages"` for chat history format
+- `gr.ChatInterface` for modern chat UI components
+- Proper message format handling for OpenRouter API
+
+### Dynamic URL Fetching
+When enabled, generated spaces can extract URLs from user messages and fetch content dynamically using regex pattern matching and Crawl4AI processing.
+
+### Vector RAG Workflow
+1. Documents uploaded through Gradio File component
+2. Processed via DocumentProcessor (PDF/DOCX/TXT/MD support)
+3. Chunked and embedded using sentence-transformers
+4. FAISS index created and serialized to base64
+5. Embedded in generated template for deployment portability
+6. Runtime similarity search for context-aware responses
+
+### Mock vs Production Web Scraping
+The application has two modes for web scraping:
+- **Mock mode** (lines 14-18 in app.py): Returns placeholder content for testing
+- **Production mode**: Uses Crawl4AI via scraping_service.py for actual web content extraction
+
+Switch between modes by commenting/uncommenting the imports and function definitions.

CLAUDE_DESKTOP_DEVELOPMENT.md

@@ -0,0 +1,411 @@
+# Claude Desktop Development Guidelines
+
+## Overview
+This document provides comprehensive guidelines for all-purpose software architecting and development when working with Claude Desktop. These instructions optimize collaboration between developers and Claude for efficient, high-quality software delivery.
+
+## Core Principles
+
+### 1. Context-First Development
+- **Always provide context**: Before asking Claude to work on code, ensure it has adequate context about the project structure, technologies used, and existing patterns
+- **Use file exploration**: Leverage Claude's file reading capabilities to understand codebases before making changes
+- **Reference existing patterns**: Point Claude to similar implementations in the codebase to maintain consistency
+
+### 2. Incremental and Iterative Approach
+- **Break down complex tasks**: Divide large features into smaller, manageable components
+- **Test frequently**: Implement and test individual components before moving to the next
+- **Use TodoWrite**: Track progress on complex tasks to maintain visibility and ensure nothing is missed
+
+### 3. Documentation-Driven Development
+- **CLAUDE.md integration**: Maintain project-specific instructions in CLAUDE.md for consistent behavior
+- **Code documentation**: Ensure all complex logic is well-documented for future maintenance
+- **Architecture decisions**: Document architectural choices and trade-offs
+
+## Project Architecture Guidelines
+
+### File Organization
+```
+project-root/
+├── CLAUDE.md                 # Claude-specific project instructions
+├── README.md                 # Project overview and setup
+├── .env.example             # Environment variable template
+├── src/
+│   ├── components/          # Reusable UI components
+│   ├── services/            # Business logic and API calls
+│   ├── utils/               # Helper functions and utilities
+│   ├── types/               # Type definitions (TypeScript)
+│   └── tests/               # Test files
+├── docs/                    # Additional documentation
+├── scripts/                 # Build and deployment scripts
+└── config/                  # Configuration files
+```
+
+### Configuration Management
+- **Environment-based configs**: Use environment variables for deployment-specific settings
+- **Type-safe configurations**: Define configuration schemas with validation
+- **Hierarchical configs**: Support development, staging, and production configurations
+- **Secret management**: Never commit secrets; use environment variables or secret management tools
+
+### Error Handling Strategy
+- **Graceful degradation**: Design systems to handle failures gracefully
+- **Comprehensive logging**: Implement structured logging for debugging and monitoring
+- **User-friendly errors**: Provide meaningful error messages to end users
+- **Recovery mechanisms**: Implement retry logic and fallback strategies where appropriate
+
+## Development Workflow
+
+### 1. Project Initialization
+```bash
+# Set up project structure
+mkdir project-name && cd project-name
+git init
+touch CLAUDE.md README.md .env.example
+mkdir -p src/{components,services,utils,types,tests}
+```
+
+### 2. CLAUDE.md Configuration
+Create project-specific instructions:
+```markdown
+# Project: [Project Name]
+
+## Tech Stack
+- Framework: [React/Vue/Angular/etc.]
+- Language: [TypeScript/JavaScript/Python/etc.]
+- Database: [PostgreSQL/MongoDB/etc.]
+- Testing: [Jest/Pytest/etc.]
+
+## Coding Standards
+- Use TypeScript for all new code
+- Follow ESLint configuration
+- Write tests for all business logic
+- Document complex functions
+
+## Architecture Patterns
+- Use custom hooks for React state logic
+- Implement repository pattern for data access
+- Follow MVC pattern for API endpoints
+
+## Deployment
+- Test commands: npm test
+- Build commands: npm run build
+- Lint commands: npm run lint
+```
+
+### 3. Development Process
+1. **Analysis Phase**
+   - Understand requirements thoroughly
+   - Review existing codebase patterns
+   - Identify potential integration points
+   - Plan architecture approach
+
+2. **Implementation Phase**
+   - Start with core functionality
+   - Build incrementally with frequent testing
+   - Maintain consistent code style
+   - Document as you go
+
+3. **Testing Phase**
+   - Unit tests for individual components
+   - Integration tests for workflows
+   - End-to-end tests for critical paths
+   - Performance testing where relevant
+
+4. **Documentation Phase**
+   - Update README if necessary
+   - Document API changes
+   - Update configuration guides
+   - Record architectural decisions
+
+## Tool Usage Best Practices
+
+### File Operations
+- **Read before edit**: Always read files before making changes to understand context
+- **Batch operations**: Use MultiEdit for multiple changes to the same file
+- **Glob patterns**: Use Glob tool for finding files by patterns
+- **Grep for search**: Use Grep tool for content searches across files
+
+### Code Quality
+- **Linting**: Run linters before committing code
+- **Type checking**: Ensure TypeScript compilation succeeds
+- **Testing**: Run test suites and ensure they pass
+- **Security**: Never commit secrets or sensitive information
+
+### Git Integration
+- **Atomic commits**: Make focused commits with clear messages
+- **Branch strategy**: Use feature branches for development
+- **Pull requests**: Create PRs with comprehensive descriptions
+- **Commit messages**: Follow conventional commit format
+
+## Technology-Specific Guidelines
+
+### Frontend Development
+```typescript
+// Component structure
+interface Props {
+  // Define all props with types
+}
+
+export const Component: React.FC<Props> = ({ prop1, prop2 }) => {
+  // Custom hooks for state management
+  const { state, actions } = useCustomHook();
+  
+  // Event handlers
+  const handleSubmit = useCallback((event: FormEvent) => {
+    // Implementation
+  }, [dependencies]);
+  
+  return (
+    // JSX with proper accessibility
+  );
+};
+```
+
+### Backend Development
+```python
+# Service layer pattern
+class UserService:
+    def __init__(self, repository: UserRepository):
+        self.repository = repository
+    
+    async def create_user(self, user_data: UserCreateSchema) -> User:
+        # Validation
+        # Business logic
+        # Persistence
+        return await self.repository.create(user_data)
+
+# API endpoint
+@router.post("/users", response_model=UserResponse)
+async def create_user(
+    user_data: UserCreateSchema,
+    service: UserService = Depends(get_user_service)
+):
+    return await service.create_user(user_data)
+```
+
+### Database Design
+- **Normalization**: Design normalized schemas to avoid data duplication
+- **Indexing**: Add indexes for frequently queried columns
+- **Migrations**: Use migration scripts for schema changes
+- **Relationships**: Define clear foreign key relationships
+
+## Security Guidelines
+
+### Authentication & Authorization
+- **JWT tokens**: Use short-lived access tokens with refresh tokens
+- **Role-based access**: Implement granular permission systems
+- **Input validation**: Validate all user inputs server-side
+- **Rate limiting**: Implement rate limiting for API endpoints
+
+### Data Protection
+- **Encryption**: Encrypt sensitive data at rest and in transit
+- **Environment variables**: Store secrets in environment variables
+- **HTTPS**: Always use HTTPS in production
+- **CORS**: Configure CORS policies appropriately
+
+## Performance Optimization
+
+### Frontend
+- **Code splitting**: Implement route-based code splitting
+- **Lazy loading**: Lazy load components and images
+- **Memoization**: Use React.memo and useMemo for expensive operations
+- **Bundle analysis**: Regularly analyze bundle sizes
+
+### Backend
+- **Caching**: Implement Redis caching for frequently accessed data
+- **Database optimization**: Use connection pooling and query optimization
+- **Async operations**: Use async/await for I/O operations
+- **Monitoring**: Implement application performance monitoring
+
+## Testing Strategy
+
+### Unit Tests
+```typescript
+describe('UserService', () => {
+  it('should create user with valid data', async () => {
+    // Arrange
+    const userData = { name: 'John', email: '[email protected]' };
+    
+    // Act
+    const result = await userService.createUser(userData);
+    
+    // Assert
+    expect(result).toMatchObject(userData);
+  });
+});
+```
+
+### Integration Tests
+- Test API endpoints with real database
+- Test component integration with services
+- Test external service integrations
+- Verify error handling scenarios
+
+### E2E Tests
+```typescript
+test('user registration flow', async ({ page }) => {
+  await page.goto('/register');
+  await page.fill('[data-testid="email"]', '[email protected]');
+  await page.fill('[data-testid="password"]', 'password123');
+  await page.click('[data-testid="submit"]');
+  await expect(page).toHaveURL('/dashboard');
+});
+```
+
+## Deployment Guidelines
+
+### Environment Configuration
+```bash
+# Development
+NODE_ENV=development
+DATABASE_URL=postgresql://localhost:5432/myapp_dev
+API_URL=http://localhost:3000
+
+# Production
+NODE_ENV=production
+DATABASE_URL=${DATABASE_URL}
+API_URL=https://api.myapp.com
+```
+
+### CI/CD Pipeline
+```yaml
+# .github/workflows/deploy.yml
+name: Deploy
+on:
+  push:
+    branches: [main]
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - run: npm ci
+      - run: npm test
+      - run: npm run lint
+      - run: npm run build
+  deploy:
+    needs: test
+    runs-on: ubuntu-latest
+    steps:
+      - run: echo "Deploy to production"
+```
+
+## Monitoring and Maintenance
+
+### Application Monitoring
+- **Error tracking**: Use services like Sentry for error monitoring
+- **Performance monitoring**: Track application performance metrics
+- **User analytics**: Monitor user behavior and feature usage
+- **Infrastructure monitoring**: Monitor server resources and uptime
+
+### Maintenance Tasks
+- **Dependency updates**: Regularly update dependencies
+- **Security patches**: Apply security updates promptly
+- **Database maintenance**: Regular backups and performance tuning
+- **Documentation updates**: Keep documentation current
+
+## Collaboration Guidelines
+
+### Code Reviews
+- **Review scope**: Focus on logic, security, and maintainability
+- **Constructive feedback**: Provide specific, actionable feedback
+- **Testing verification**: Ensure tests cover new functionality
+- **Documentation check**: Verify documentation is updated
+
+### Communication
+- **Clear requirements**: Provide detailed specifications
+- **Progress updates**: Regular status updates on complex tasks
+- **Technical discussions**: Use pull request comments for technical discussions
+- **Knowledge sharing**: Document learnings and solutions
+
+## Common Patterns
+
+### State Management
+```typescript
+// Custom hook pattern
+export const useUserData = () => {
+  const [user, setUser] = useState<User | null>(null);
+  const [loading, setLoading] = useState(true);
+  const [error, setError] = useState<string | null>(null);
+  
+  const fetchUser = useCallback(async (id: string) => {
+    try {
+      setLoading(true);
+      const userData = await userService.getUser(id);
+      setUser(userData);
+    } catch (err) {
+      setError(err.message);
+    } finally {
+      setLoading(false);
+    }
+  }, []);
+  
+  return { user, loading, error, fetchUser };
+};
+```
+
+### API Integration
+```typescript
+// Repository pattern
+export class ApiRepository {
+  constructor(private httpClient: HttpClient) {}
+  
+  async get<T>(endpoint: string): Promise<T> {
+    try {
+      const response = await this.httpClient.get(endpoint);
+      return response.data;
+    } catch (error) {
+      throw new ApiError(error.message, error.status);
+    }
+  }
+}
+```
+
+### Configuration
+```typescript
+// Type-safe configuration
+interface Config {
+  api: {
+    baseUrl: string;
+    timeout: number;
+  };
+  features: {
+    enableNewFeature: boolean;
+  };
+}
+
+export const config: Config = {
+  api: {
+    baseUrl: process.env.API_URL || 'http://localhost:3000',
+    timeout: parseInt(process.env.API_TIMEOUT || '5000'),
+  },
+  features: {
+    enableNewFeature: process.env.ENABLE_NEW_FEATURE === 'true',
+  },
+};
+```
+
+## Troubleshooting Guide
+
+### Common Issues
+1. **Build failures**: Check dependency versions and environment variables
+2. **Test failures**: Verify test data and mock configurations
+3. **Performance issues**: Profile code and check for memory leaks
+4. **Security vulnerabilities**: Run security audits and update dependencies
+
+### Debugging Strategies
+- **Structured logging**: Use consistent log levels and formats
+- **Debug tools**: Leverage browser dev tools and IDE debuggers
+- **Error boundaries**: Implement React error boundaries for graceful failures
+- **Health checks**: Implement endpoint health checks for monitoring
+
+## Conclusion
+
+These guidelines provide a comprehensive framework for developing high-quality software with Claude Desktop. Adapt these patterns to fit your specific project needs while maintaining the core principles of clarity, maintainability, and security.
+
+Remember to:
+- Keep documentation updated
+- Test thoroughly at each stage
+- Follow security best practices
+- Maintain consistent code quality
+- Collaborate effectively with clear communication
+
+For project-specific guidance, always reference the CLAUDE.md file in your project root.

README.md

@@ -4,7 +4,7 @@ emoji: 💻
 colorFrom: gray
 colorTo: red
 sdk: gradio
-sdk_version: 5.34.0
+sdk_version: 5.35.0
 app_file: app.py
 pinned: true
 thumbnail: >-
@@ -14,17 +14,49 @@ short_description: Configure, download, and deploy a simple chat interface
 
 # Chat UI Helper
 
-A tool to help you create and configure chat interfaces for HuggingFace Spaces.
+A Gradio-based tool for generating and configuring chat interfaces for HuggingFace Spaces. Create deployable packages with custom assistants, web scraping capabilities, and optional vector RAG functionality.
 
 ## Features
 
-1. **Spaces Configuration**: Generate ready-to-deploy packages for custom chat interfaces
-2. **Chat Support**: Get chat configuration support from Gemini Flash
+### Spaces Configuration
+- **Custom Assistant Creation**: Define role, purpose, audience, and tasks
+- **Template System**: Choose from research assistant template or build from scratch
+- **Tool Integration**: Optional dynamic URL fetching and document RAG
+- **Access Control**: Secure access code protection for educational use
+- **Complete Deployment Package**: Generates app.py, requirements.txt, README.md, and config.json
 
-## Setup
+### Chat Support
+- **Expert Guidance**: Get personalized help with Gradio configurations
+- **Context-Aware**: URL grounding for informed responses about HuggingFace Spaces
+- **Deployment Assistance**: Troubleshooting and best practices
 
+## Quick Start
+
+### Running Locally
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+### For Chat Support (Optional)
 Set your OpenRouter API key as a secret:
 - Go to Settings → Variables and secrets
 - Add secret: `OPENROUTER_API_KEY`
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Generated Space Features
+
+Each generated space includes:
+- **OpenRouter API Integration**: Support for multiple LLM models
+- **Web Scraping**: Crawl4AI integration for URL content fetching
+- **Document RAG**: Optional upload and search through PDF, DOCX, TXT, MD files
+- **Access Control**: Environment-based student access codes
+- **Modern UI**: Gradio 5.x ChatInterface with proper message formatting
+
+## Architecture
+
+- **Main Application**: `app.py` with two-tab interface
+- **Document Processing**: RAG pipeline with FAISS vector search
+- **Web Scraping**: Async Crawl4AI integration
+- **Template Generation**: Complete HuggingFace Space creation
+
+For detailed development guidance, see [CLAUDE.md](CLAUDE.md).

app.py

@@ -42,7 +42,8 @@ SPACE_DESCRIPTION = "{description}"
 SYSTEM_PROMPT = """{system_prompt}"""
 MODEL = "{model}"
 GROUNDING_URLS = {grounding_urls}
-ACCESS_CODE = "{access_code}"
+# Get access code from environment variable for security
+ACCESS_CODE = os.environ.get("SPACE_ACCESS_CODE", "{access_code}")
 ENABLE_DYNAMIC_URLS = {enable_dynamic_urls}
 ENABLE_VECTOR_RAG = {enable_vector_rag}
 RAG_DATA = {rag_data_json}
@@ -224,20 +225,26 @@ def generate_response(message, history):
 
 # Access code verification
 access_granted = gr.State(False)
+_access_granted_global = False  # Global fallback
 
 def verify_access_code(code):
     \"\"\"Verify the access code\"\"\"
+    global _access_granted_global
     if not ACCESS_CODE:
+        _access_granted_global = True
         return gr.update(visible=False), gr.update(visible=True), True
     
     if code == ACCESS_CODE:
+        _access_granted_global = True
         return gr.update(visible=False), gr.update(visible=True), True
     else:
+        _access_granted_global = False
         return gr.update(visible=True, value="❌ Incorrect access code. Please try again."), gr.update(visible=False), False
 
-def protected_generate_response(message, history, access_state):
+def protected_generate_response(message, history):
     \"\"\"Protected response function that checks access\"\"\"
-    if not access_state:
+    # Check if access is granted via the global variable
+    if ACCESS_CODE and not _access_granted_global:
         return "Please enter the access code to continue."
     return generate_response(message, history)
 
@@ -262,7 +269,7 @@ with gr.Blocks(title=SPACE_NAME) as demo:
     # Main chat interface (hidden until access granted)
     with gr.Column(visible=not bool(ACCESS_CODE)) as chat_section:
         chat_interface = gr.ChatInterface(
-            fn=lambda msg, hist: protected_generate_response(msg, hist, access_granted.value),
+            fn=protected_generate_response,
             title="",  # Title already shown above
             description="",  # Description already shown above
             examples={examples}
@@ -344,7 +351,7 @@ emoji: 🤖
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 4.32.0
+sdk_version: 5.35.0
 app_file: app.py
 pinned: false
 ---
@@ -378,15 +385,22 @@ pinned: false
 5. Value: Your OpenRouter API key
 6. Click "Add"
 
-{f'''### Step 4: Configure Access Control (Optional)
+{f'''### Step 4: Configure Access Control
 Your Space is configured with access code protection. Students will need to enter the access code to use the chatbot.
 
-**Access Code**: `{config['access_code']}`
+1. Go to Settings (gear icon) 
+2. Click "Variables and secrets"
+3. Click "New secret"
+4. Name: `SPACE_ACCESS_CODE`
+5. Value: `{config['access_code']}`
+6. Click "Add"
+
+**Important**: The access code is now stored securely as an environment variable and is not visible in your app code.
 
 To disable access protection:
-1. Edit `app.py` in your Space
-2. Change `ACCESS_CODE = "{config['access_code']}"` to `ACCESS_CODE = ""`
-3. The Space will rebuild automatically
+1. Go to Settings → Variables and secrets
+2. Delete the `SPACE_ACCESS_CODE` secret
+3. The Space will rebuild automatically with no access protection
 
 ''' if config['access_code'] else ''}
 
@@ -448,7 +462,7 @@ Generated on {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} with Chat U/I Helper
 
 def create_requirements(enable_vector_rag=False):
     """Generate requirements.txt"""
-    base_requirements = "gradio==4.44.1\nrequests==2.32.3\ncrawl4ai==0.4.245"
+    base_requirements = "gradio>=5.35.0\nrequests>=2.32.3\ncrawl4ai>=0.4.0\naiofiles>=24.0"
     
     if enable_vector_rag:
         base_requirements += "\nfaiss-cpu==1.7.4\nnumpy==1.24.3"
@@ -499,15 +513,18 @@ def generate_zip(name, description, role_purpose, intended_audience, key_tasks,
         'max_tokens': int(max_tokens),
         'examples': examples_json,
         'grounding_urls': json.dumps(grounding_urls),
-        'access_code': access_code or "",
+        'access_code': "",  # Access code stored in environment variable for security
         'enable_dynamic_urls': enable_dynamic_urls,
         'enable_vector_rag': enable_vector_rag,
-        'rag_data_json': json.dumps(rag_data) if rag_data else 'null'
+        'rag_data_json': json.dumps(rag_data) if rag_data else 'None'
     }
     
     # Generate files
     app_content = SPACE_TEMPLATE.format(**config)
-    readme_content = create_readme(config)
+    # Pass original access_code to README for documentation
+    readme_config = config.copy()
+    readme_config['access_code'] = access_code or ""
+    readme_content = create_readme(readme_config)
     requirements_content = create_requirements(enable_vector_rag)
     
     # Create zip file with clean naming
@@ -658,7 +675,8 @@ def respond(message, chat_history, url1="", url2="", url3="", url4=""):
     
     if not api_key:
         response = "Please set your OPENROUTER_API_KEY in the Space settings to use the chat support."
-        chat_history.append([message, response])
+        chat_history.append({"role": "user", "content": message})
+        chat_history.append({"role": "assistant", "content": response})
         return "", chat_history
     
     # Get grounding context from URLs using cached approach
@@ -789,15 +807,7 @@ def remove_chat_urls(count):
 
 def update_template_fields(choice):
     """Update assistant configuration fields based on template choice"""
-    if choice == "Use the research assistant template":
-        return (
-            gr.update(value="You are a research assistant that provides link-grounded information through Crawl4AI web fetching. Use MLA documentation for parenthetical citations and bibliographic entries."),
-            gr.update(value="This assistant is designed for students and researchers conducting academic inquiry."),
-            gr.update(value="Your main responsibilities include: analyzing academic sources, fact-checking claims with evidence, providing properly cited research summaries, and helping users navigate scholarly information."),
-            gr.update(value="Ground all responses in provided URL contexts and any additional URLs you're instructed to fetch. Never rely on memory for factual claims."),
-            gr.update(value=True)  # Enable dynamic URL fetching for research template
-        )
-    else:  # Custom assistant from scratch
+    if choice == "System Prompt (Custom)":
         return (
             gr.update(value=""),
             gr.update(value=""),
@@ -805,6 +815,14 @@ def update_template_fields(choice):
             gr.update(value=""),
             gr.update(value=False)  # Disable dynamic URL fetching for custom template
         )
+    else:  # Research Assistant Template (Extended)
+        return (
+            gr.update(value="You are a research assistant that provides link-grounded information through Crawl4AI web fetching. Use MLA documentation for parenthetical citations and bibliographic entries."),
+            gr.update(value="This assistant is designed for students and researchers conducting academic inquiry."),
+            gr.update(value="Your main responsibilities include: analyzing academic sources, fact-checking claims with evidence, providing properly cited research summaries, and helping users navigate scholarly information."),
+            gr.update(value="Ground all responses in provided URL contexts and any additional URLs you're instructed to fetch. Never rely on memory for factual claims."),
+            gr.update(value=True)  # Enable dynamic URL fetching for research template
+        )
 
 # Create Gradio interface with proper tab structure
 with gr.Blocks(title="Chat U/I Helper") as demo:
@@ -824,7 +842,7 @@ with gr.Blocks(title="Chat U/I Helper") as demo:
                     label="Space Description", 
                     placeholder="A customizable AI chat interface for...",
                     lines=2,
-                    value="An AI research assistant tailored for academic inquiry and scholarly dialogue"
+                    value=""
                 )
                 
                 model = gr.Dropdown(
@@ -853,10 +871,10 @@ with gr.Blocks(title="Chat U/I Helper") as demo:
                     template_choice = gr.Radio(
                         label="How would you like to get started?",
                         choices=[
-                            "Use the research assistant template",
-                            "Create a custom assistant from scratch"
+                            "System Prompt (Custom)",
+                            "Research Assistant Template (Extended)"
                         ],
-                        value="Use the research assistant template",
+                        value="System Prompt (Custom)",
                         info="Choose a starting point for your assistant configuration"
                     )
                     
@@ -864,7 +882,7 @@ with gr.Blocks(title="Chat U/I Helper") as demo:
                         label="Role and Purpose",
                         placeholder="You are a research assistant that...",
                         lines=2,
-                        value="You are a research assistant that provides link-grounded information through Crawl4AI web fetching. Use MLA documentation for parenthetical citations and bibliographic entries.",
+                        value="",
                         info="Define what the assistant is and its primary function"
                     )
                     
@@ -872,7 +890,7 @@ with gr.Blocks(title="Chat U/I Helper") as demo:
                         label="Intended Audience",
                         placeholder="This assistant is designed for undergraduate students...",
                         lines=2,
-                        value="This assistant is designed for students and researchers conducting academic inquiry.",
+                        value="",
                         info="Specify who will be using this assistant and their context"
                     )
                     
@@ -880,7 +898,7 @@ with gr.Blocks(title="Chat U/I Helper") as demo:
                         label="Key Tasks",
                         placeholder="Your main responsibilities include...",
                         lines=3,
-                        value="Your main responsibilities include: analyzing academic sources, fact-checking claims with evidence, providing properly cited research summaries, and helping users navigate scholarly information.",
+                        value="",
                         info="List the specific tasks and capabilities the assistant should focus on"
                     )
                     
@@ -888,11 +906,13 @@ with gr.Blocks(title="Chat U/I Helper") as demo:
                         label="Additional Context",
                         placeholder="Remember to always...",
                         lines=2,
-                        value="Ground all responses in provided URL contexts and any additional URLs you're instructed to fetch. Never rely on memory for factual claims.",
+                        value="",
                         info="Any additional instructions, constraints, or behavioral guidelines"
                     )
+                
+                with gr.Accordion("Tool Settings", open=True):
+                    gr.Markdown("### Configure available tools and capabilities")
                     
-                    gr.Markdown("### Tool Settings")
                     enable_dynamic_urls = gr.Checkbox(
                         label="Enable Dynamic URL Fetching",
                         value=False,
@@ -920,13 +940,6 @@ with gr.Blocks(title="Chat U/I Helper") as demo:
                         # State to store RAG tool
                         rag_tool_state = gr.State(None)
                 
-                examples_text = gr.Textbox(
-                    label="Example Prompts (one per line)",
-                    placeholder="Can you analyze this research paper: https://example.com/paper.pdf\nWhat are the latest findings on climate change adaptation?\nHelp me fact-check claims about renewable energy efficiency",
-                    lines=3,
-                    info="These will appear as clickable examples in the chat interface"
-                )
-                
                 with gr.Accordion("URL Grounding (Optional)", open=False):
                     gr.Markdown("Add URLs to provide context. Content will be fetched and added to the system prompt.")
                     
@@ -964,6 +977,13 @@ with gr.Blocks(title="Chat U/I Helper") as demo:
                         remove_url_btn = gr.Button("- Remove URLs", size="sm", visible=False)
                     url_count = gr.State(2)  # Track number of visible URLs
                 
+                examples_text = gr.Textbox(
+                    label="Example Prompts (one per line)",
+                    placeholder="Can you analyze this research paper: https://example.com/paper.pdf\nWhat are the latest findings on climate change adaptation?\nHelp me fact-check claims about renewable energy efficiency",
+                    lines=3,
+                    info="These will appear as clickable examples in the chat interface"
+                )
+                
                 with gr.Row():
                     temperature = gr.Slider(
                         label="Temperature",
@@ -1036,7 +1056,8 @@ with gr.Blocks(title="Chat U/I Helper") as demo:
                 chatbot = gr.Chatbot(
                     value=[],
                     label="Chat Support Assistant", 
-                    height=400
+                    height=400,
+                    type="messages"
                 )
                 msg = gr.Textbox(
                     label="Ask about configuring chat UIs for courses, research, or custom HuggingFace Spaces",

requirements.txt

@@ -1,8 +1,9 @@
-gradio>=4.44.0
+gradio>=5.35.0
 requests>=2.32.3
 beautifulsoup4>=4.12.3
 python-dotenv>=1.0.0
-crawl4ai>=0.4.245
+crawl4ai>=0.4.0
+aiofiles>=24.0
 
 # Vector RAG dependencies (optional)
 sentence-transformers>=2.2.2

test_document.txt

@@ -0,0 +1,24 @@
+Vector Database Test Document
+
+This is a test document for evaluating the vector database functionality. 
+
+Section 1: Introduction to Vector Databases
+Vector databases store and query high-dimensional vector representations of data. They enable semantic search by finding vectors similar to a query vector in an embedding space.
+
+Section 2: Use Cases
+Common applications include:
+- Document retrieval and question answering
+- Similarity search for products or content
+- Recommendation systems
+- Semantic search in chatbots
+
+Section 3: Technical Implementation
+Vector databases typically use embedding models to convert text into dense vectors, then use algorithms like cosine similarity or approximate nearest neighbor search to find relevant results.
+
+Section 4: Benefits
+- Semantic understanding beyond keyword matching
+- Scalable retrieval for large document collections
+- Integration with modern AI systems and large language models
+- Support for multi-modal data (text, images, audio)
+
+This document should generate multiple chunks when processed by the system.

test_vector_db.py

@@ -0,0 +1,196 @@
+#!/usr/bin/env python3
+"""
+Test script to verify vector database creation functionality
+"""
+
+import sys
+import os
+from pathlib import Path
+
+# Add current directory to path to import modules
+sys.path.append(str(Path(__file__).parent))
+
+try:
+    from rag_tool import RAGTool
+    from vector_store import VectorStore
+    from document_processor import DocumentProcessor
+    print("✅ Successfully imported all RAG modules")
+except ImportError as e:
+    print(f"❌ Failed to import RAG modules: {e}")
+    sys.exit(1)
+
+def test_document_processing():
+    """Test document processing functionality"""
+    print("\n=== Testing Document Processing ===")
+    
+    processor = DocumentProcessor(chunk_size=200, chunk_overlap=50)
+    
+    # Test with our test document
+    test_file = "test_document.txt"
+    if not os.path.exists(test_file):
+        print(f"❌ Test file {test_file} not found")
+        return False
+    
+    try:
+        chunks = processor.process_file(test_file)
+        print(f"✅ Processed {test_file} into {len(chunks)} chunks")
+        
+        # Show first chunk
+        if chunks:
+            first_chunk = chunks[0]
+            print(f"First chunk preview: {first_chunk.text[:100]}...")
+            print(f"Chunk metadata: {first_chunk.metadata}")
+        
+        return True
+    except Exception as e:
+        print(f"❌ Failed to process document: {e}")
+        return False
+
+def test_vector_store():
+    """Test vector store functionality"""
+    print("\n=== Testing Vector Store ===")
+    
+    try:
+        # Initialize vector store
+        vector_store = VectorStore()
+        print("✅ Initialized vector store")
+        
+        # Create test data
+        test_chunks = [
+            {
+                'text': 'Vector databases are used for semantic search',
+                'chunk_id': 'test1',
+                'metadata': {'file_name': 'test.txt', 'chunk_index': 0}
+            },
+            {
+                'text': 'Machine learning models convert text to embeddings',
+                'chunk_id': 'test2', 
+                'metadata': {'file_name': 'test.txt', 'chunk_index': 1}
+            },
+            {
+                'text': 'FAISS provides efficient similarity search capabilities',
+                'chunk_id': 'test3',
+                'metadata': {'file_name': 'test.txt', 'chunk_index': 2}
+            }
+        ]
+        
+        # Build index
+        print("Building vector index...")
+        vector_store.build_index(test_chunks, show_progress=True)
+        print("✅ Built vector index")
+        
+        # Test search
+        query = "How do vector databases work?"
+        results = vector_store.search(query, top_k=2)
+        
+        print(f"Search results for '{query}':")
+        for i, result in enumerate(results):
+            print(f"  {i+1}. Score: {result.score:.3f} - {result.text[:50]}...")
+        
+        # Test serialization
+        serialized = vector_store.serialize()
+        print(f"✅ Serialized data size: {len(serialized['index_base64'])} characters")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ Failed vector store test: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def test_rag_tool():
+    """Test complete RAG tool functionality"""
+    print("\n=== Testing RAG Tool ===")
+    
+    try:
+        # Initialize RAG tool
+        rag_tool = RAGTool()
+        print("✅ Initialized RAG tool")
+        
+        # Process test document
+        test_files = ["test_document.txt"]
+        result = rag_tool.process_uploaded_files(test_files)
+        
+        if result['success']:
+            print(f"✅ {result['message']}")
+            
+            # Show summary
+            summary = result['summary']
+            print(f"Files processed: {summary['total_files']}")
+            print(f"Total chunks: {summary['total_chunks']}")
+            
+            # Test context retrieval
+            query = "What are the benefits of vector databases?"
+            context = rag_tool.get_relevant_context(query, max_chunks=2)
+            
+            if context:
+                print(f"\nContext for '{query}':")
+                print(context[:300] + "..." if len(context) > 300 else context)
+                print("✅ Successfully retrieved context")
+            else:
+                print("⚠️ No context retrieved")
+            
+            # Test serialization for deployment
+            serialized_data = rag_tool.get_serialized_data()
+            if serialized_data:
+                print("✅ Successfully serialized RAG data for deployment")
+                print(f"Serialized keys: {list(serialized_data.keys())}")
+            else:
+                print("❌ Failed to serialize RAG data")
+            
+            return True
+        else:
+            print(f"❌ {result['message']}")
+            return False
+            
+    except Exception as e:
+        print(f"❌ Failed RAG tool test: {e}")
+        import traceback
+        traceback.print_exc()
+        return False
+
+def main():
+    """Run all tests"""
+    print("=== Vector Database Testing ===")
+    print("Testing vector database creation and functionality...")
+    
+    # Check dependencies
+    print("\n=== Checking Dependencies ===")
+    try:
+        import sentence_transformers
+        import faiss
+        import fitz  # PyMuPDF
+        print("✅ All required dependencies available")
+    except ImportError as e:
+        print(f"❌ Missing dependency: {e}")
+        return
+    
+    # Run tests
+    tests = [
+        ("Document Processing", test_document_processing),
+        ("Vector Store", test_vector_store), 
+        ("RAG Tool", test_rag_tool)
+    ]
+    
+    results = []
+    for test_name, test_func in tests:
+        print(f"\n{'='*20}")
+        success = test_func()
+        results.append((test_name, success))
+    
+    # Summary
+    print(f"\n{'='*40}")
+    print("TEST SUMMARY:")
+    for test_name, success in results:
+        status = "✅ PASS" if success else "❌ FAIL"
+        print(f"  {test_name}: {status}")
+    
+    all_passed = all(success for _, success in results)
+    if all_passed:
+        print("\n🎉 All tests passed! Vector database functionality is working.")
+    else:
+        print("\n⚠️ Some tests failed. Check the output above for details.")
+
+if __name__ == "__main__":
+    main()