Introduction and Problem Statement
The challenge of creating professional websites from natural language descriptions represents a complex intersection of artificial intelligence, software engineering, and web development. An LLM-based Web Wizard must transform vague user requirements into complete, functional websites while maintaining consistency, security, and compliance standards.
Traditional website builders require users to understand design principles, navigate complex interfaces, and manually configure numerous options. An intelligent Web Wizard eliminates these barriers by leveraging Large Language Models to interpret user intent and automatically generate appropriate website structures, content, and styling.
The core challenge lies not in generating individual web components, but in orchestrating a complex system that can reliably produce cohesive, professional websites. This system must handle requirement analysis, architectural decision-making, code generation, quality assurance, and compliance verification while remaining configurable and extensible.
System Architecture Design
The Web Wizard follows a layered architecture that separates concerns while maintaining tight integration between components. The architecture consists of five primary layers: the Interface Layer, the Intelligence Layer, the Generation Layer, the Validation Layer, and the Output Layer.
The Interface Layer handles user input processing and requirement extraction. This layer transforms natural language descriptions into structured data that subsequent layers can process. It includes input validation, requirement parsing, and context management components.
class WebWizardCore:
def __init__(self, config_manager):
self.config = config_manager
self.interface_layer = InterfaceLayer(config_manager)
self.intelligence_layer = IntelligenceLayer(config_manager)
self.generation_layer = GenerationLayer(config_manager)
self.validation_layer = ValidationLayer(config_manager)
self.output_layer = OutputLayer(config_manager)
def process_request(self, user_input, options=None):
# Process through each layer sequentially
requirements = self.interface_layer.parse_requirements(user_input)
architecture = self.intelligence_layer.design_architecture(requirements)
components = self.generation_layer.generate_components(architecture)
validated_components = self.validation_layer.validate(components)
final_output = self.output_layer.package_website(validated_components)
return final_output
The Intelligence Layer contains the LLM integration and decision-making logic. This layer interprets requirements, makes architectural decisions, and coordinates the overall generation process. It abstracts the complexity of different LLM providers and manages prompt engineering strategies.
The Generation Layer transforms architectural decisions into actual code components. This layer includes template engines, code synthesizers, and asset generators. It maintains consistency across generated components while allowing for customization and variation.
The Validation Layer ensures quality, security, and compliance of generated components. This layer performs code validation, accessibility checking, security scanning, and GDPR compliance verification. It can reject or modify components that don't meet standards.
The Output Layer packages validated components into deployable websites. This layer handles file organization, asset optimization, documentation generation, and deployment preparation.
LLM Integration Strategy
The LLM integration strategy focuses on creating a flexible, provider-agnostic system that can work with various language models while maintaining consistent output quality. The system supports both local and remote LLM deployments through a unified interface.
class LLMManager:
def __init__(self, config):
self.config = config
self.provider = self._initialize_provider()
self.prompt_engine = PromptEngine()
self.response_parser = ResponseParser()
def _initialize_provider(self):
provider_type = self.config.get('llm_provider', 'local')
if provider_type == 'local':
return LocalLLMProvider(self.config)
elif provider_type == 'openai':
return OpenAIProvider(self.config)
elif provider_type == 'anthropic':
return AnthropicProvider(self.config)
else:
raise ValueError(f"Unsupported LLM provider: {provider_type}")
def generate_architecture(self, requirements):
prompt = self.prompt_engine.build_architecture_prompt(requirements)
raw_response = self.provider.generate(prompt)
structured_response = self.response_parser.parse_architecture(raw_response)
return structured_response
The prompt engineering methodology employs a multi-stage approach that breaks complex website generation into manageable subtasks. Each stage has specialized prompts designed to elicit specific types of information from the LLM.
The first stage focuses on requirement analysis and clarification. Prompts in this stage help the LLM understand user intent, identify missing information, and suggest improvements to the initial requirements. The second stage handles architectural design, where the LLM determines appropriate page structures, navigation patterns, and feature sets.
The third stage manages detailed design decisions including styling, layout, and component selection. The fourth stage handles content generation, creating appropriate text, headings, and structural elements for each page. The final stage focuses on integration and optimization, ensuring all components work together cohesively.
class PromptEngine:
def __init__(self):
self.templates = self._load_prompt_templates()
self.context_manager = ContextManager()
def build_architecture_prompt(self, requirements):
base_template = self.templates['architecture_design']
context = self.context_manager.get_context(requirements)
prompt = base_template.format(
user_requirements=requirements.description,
website_type=requirements.type,
target_audience=requirements.audience,
special_features=requirements.features,
context_information=context
)
return prompt
def build_component_prompt(self, component_spec):
template = self.templates['component_generation']
prompt = template.format(
component_type=component_spec.type,
requirements=component_spec.requirements,
styling_guidelines=component_spec.styling,
accessibility_requirements=component_spec.accessibility
)
return prompt
Response parsing and validation ensure that LLM outputs are properly structured and contain all necessary information. The parser handles various output formats and can request clarification when responses are incomplete or ambiguous.
Core Engine Components
The Requirement Analysis Engine transforms natural language input into structured specifications that guide the generation process. This engine employs natural language processing techniques combined with LLM capabilities to extract intent, identify requirements, and resolve ambiguities.
class RequirementAnalysisEngine:
def __init__(self, llm_manager):
self.llm_manager = llm_manager
self.requirement_extractor = RequirementExtractor()
self.ambiguity_resolver = AmbiguityResolver()
self.requirement_validator = RequirementValidator()
def analyze_requirements(self, user_input):
# Extract initial requirements
raw_requirements = self.requirement_extractor.extract(user_input)
# Resolve ambiguities using LLM
clarified_requirements = self.ambiguity_resolver.resolve(
raw_requirements, self.llm_manager
)
# Validate and structure requirements
structured_requirements = self.requirement_validator.validate(
clarified_requirements
)
return structured_requirements
def enhance_requirements(self, requirements):
# Use LLM to suggest improvements and additions
enhancement_prompt = self._build_enhancement_prompt(requirements)
suggestions = self.llm_manager.generate_suggestions(enhancement_prompt)
enhanced_requirements = self._apply_suggestions(requirements, suggestions)
return enhanced_requirements
The Structure Generation Engine creates the overall architecture and page hierarchy for the website. This engine considers best practices for information architecture, user experience, and technical implementation while respecting user preferences and requirements.
The engine maintains a knowledge base of common website patterns and can adapt these patterns to specific requirements. It considers factors such as content volume, user journey optimization, SEO requirements, and maintenance complexity when making structural decisions.
class StructureGenerationEngine:
def __init__(self, llm_manager):
self.llm_manager = llm_manager
self.pattern_library = PatternLibrary()
self.architecture_optimizer = ArchitectureOptimizer()
def generate_structure(self, requirements):
# Select appropriate base patterns
base_patterns = self.pattern_library.select_patterns(requirements)
# Use LLM to adapt patterns to specific requirements
adaptation_prompt = self._build_adaptation_prompt(
base_patterns, requirements
)
adapted_structure = self.llm_manager.generate_structure(adaptation_prompt)
# Optimize the structure for usability and performance
optimized_structure = self.architecture_optimizer.optimize(
adapted_structure, requirements
)
return optimized_structure
def validate_structure(self, structure, requirements):
# Ensure structure meets all requirements
validation_results = []
# Check completeness
completeness_check = self._check_completeness(structure, requirements)
validation_results.append(completeness_check)
# Check usability
usability_check = self._check_usability(structure)
validation_results.append(usability_check)
# Check technical feasibility
feasibility_check = self._check_feasibility(structure)
validation_results.append(feasibility_check)
return validation_results
The Template Management System provides a foundation of reusable components and design patterns. This system maintains a library of templates that can be customized and combined to create unique websites while ensuring consistency and quality.
Templates are organized hierarchically with base templates providing fundamental structure and specialized templates adding specific functionality. The system supports template inheritance, composition, and dynamic customization based on requirements.
The Code Synthesis Engine transforms templates and specifications into actual website code. This engine coordinates between different code generators for HTML, CSS, JavaScript, and configuration files while maintaining consistency and integration across all components.
class CodeSynthesisEngine:
def __init__(self, template_manager):
self.template_manager = template_manager
self.html_generator = HTMLGenerator()
self.css_generator = CSSGenerator()
self.js_generator = JavaScriptGenerator()
self.integration_manager = IntegrationManager()
def synthesize_website(self, structure, requirements):
# Generate individual components
html_components = self.html_generator.generate(structure, requirements)
css_components = self.css_generator.generate(structure, requirements)
js_components = self.js_generator.generate(structure, requirements)
# Integrate components
integrated_website = self.integration_manager.integrate(
html_components, css_components, js_components
)
# Apply final optimizations
optimized_website = self._optimize_output(integrated_website)
return optimized_website
def _optimize_output(self, website):
# Minify code
website = self._minify_code(website)
# Optimize assets
website = self._optimize_assets(website)
# Validate integration
website = self._validate_integration(website)
return website
Configuration and Extensibility
The configuration system provides comprehensive control over all aspects of the Web Wizard's behavior. Configuration files use a hierarchical structure that allows for global defaults, environment-specific overrides, and project-specific customizations.
class ConfigurationManager:
def __init__(self, config_path=None):
self.config_path = config_path or self._find_config_file()
self.config_data = self._load_configuration()
self.validators = self._initialize_validators()
def _load_configuration(self):
# Load base configuration
base_config = self._load_base_config()
# Load environment-specific overrides
env_config = self._load_environment_config()
# Load user-specific customizations
user_config = self._load_user_config()
# Merge configurations with proper precedence
merged_config = self._merge_configs(base_config, env_config, user_config)
# Validate final configuration
self._validate_configuration(merged_config)
return merged_config
def get_llm_config(self):
return self.config_data.get('llm', {})
def get_generation_config(self):
return self.config_data.get('generation', {})
def get_validation_config(self):
return self.config_data.get('validation', {})
The plugin architecture allows for extending the Web Wizard's capabilities without modifying core code. Plugins can add new LLM providers, template types, validation rules, or output formats. The plugin system uses a registration mechanism that automatically discovers and loads available plugins.
class PluginManager:
def __init__(self):
self.plugins = {}
self.plugin_registry = PluginRegistry()
def load_plugins(self, plugin_directory):
# Discover available plugins
available_plugins = self.plugin_registry.discover_plugins(plugin_directory)
# Load and validate each plugin
for plugin_info in available_plugins:
try:
plugin = self._load_plugin(plugin_info)
self._validate_plugin(plugin)
self.plugins[plugin.name] = plugin
except Exception as e:
self._handle_plugin_error(plugin_info, e)
def get_plugin(self, plugin_name):
return self.plugins.get(plugin_name)
def list_plugins(self):
return list(self.plugins.keys())
The template customization system allows users to modify existing templates or create entirely new ones. Templates support parameterization, conditional logic, and dynamic content generation. The system includes a template editor and validation tools to ensure template quality.
Implementation Details
The Web Wizard implementation follows object-oriented design principles with clear separation of concerns and well-defined interfaces. The class hierarchy is designed for extensibility and maintainability while providing robust error handling and logging.
class WebWizardApplication:
def __init__(self, config_path=None):
self.config_manager = ConfigurationManager(config_path)
self.plugin_manager = PluginManager()
self.logger = self._setup_logging()
# Initialize core components
self._initialize_core_components()
# Load plugins
self._load_plugins()
# Validate system readiness
self._validate_system()
def _initialize_core_components(self):
self.llm_manager = LLMManager(self.config_manager.get_llm_config())
self.requirement_engine = RequirementAnalysisEngine(self.llm_manager)
self.structure_engine = StructureGenerationEngine(self.llm_manager)
self.template_manager = TemplateManager(self.config_manager)
self.synthesis_engine = CodeSynthesisEngine(self.template_manager)
self.validation_engine = ValidationEngine(self.config_manager)
self.output_manager = OutputManager(self.config_manager)
def generate_website(self, user_input, options=None):
try:
# Log request initiation
self.logger.info(f"Starting website generation for input: {user_input[:100]}...")
# Analyze requirements
requirements = self.requirement_engine.analyze_requirements(user_input)
self.logger.debug(f"Analyzed requirements: {requirements}")
# Generate structure
structure = self.structure_engine.generate_structure(requirements)
self.logger.debug(f"Generated structure with {len(structure.pages)} pages")
# Synthesize code
website_code = self.synthesis_engine.synthesize_website(structure, requirements)
self.logger.debug("Code synthesis completed")
# Validate output
validation_results = self.validation_engine.validate(website_code)
if not validation_results.is_valid:
raise ValidationError(validation_results.errors)
# Package output
final_output = self.output_manager.package_website(website_code, options)
self.logger.info("Website generation completed successfully")
return final_output
except Exception as e:
self.logger.error(f"Website generation failed: {str(e)}")
raise
The processing workflow follows a pipeline pattern where each stage transforms the data and passes it to the next stage. This design allows for easy debugging, testing, and modification of individual stages without affecting the entire system.
State management is handled through a centralized state manager that tracks the progress of generation requests, maintains context between stages, and provides rollback capabilities in case of errors.
Resource optimization includes memory management for large websites, caching of frequently used templates and components, and parallel processing where appropriate. The system monitors resource usage and can adjust processing strategies based on available resources.
GDPR and Security Integration
The GDPR compliance system automatically integrates privacy features into every generated website. This system maintains a knowledge base of GDPR requirements and automatically generates appropriate privacy policies, cookie consent mechanisms, and data management interfaces.
class GDPRComplianceSystem:
def __init__(self, config):
self.config = config
self.privacy_policy_generator = PrivacyPolicyGenerator()
self.cookie_consent_manager = CookieConsentManager()
self.data_rights_manager = DataRightsManager()
def integrate_compliance_features(self, website_structure):
# Add privacy policy
privacy_policy = self.privacy_policy_generator.generate(website_structure)
website_structure.add_page(privacy_policy)
# Add cookie consent system
cookie_system = self.cookie_consent_manager.generate_system(website_structure)
website_structure.add_component(cookie_system)
# Add data subject rights interface
if self._requires_data_rights_interface(website_structure):
rights_interface = self.data_rights_manager.generate_interface()
website_structure.add_component(rights_interface)
return website_structure
def validate_compliance(self, website):
compliance_issues = []
# Check privacy policy presence and completeness
privacy_check = self._validate_privacy_policy(website)
compliance_issues.extend(privacy_check)
# Check cookie consent implementation
cookie_check = self._validate_cookie_consent(website)
compliance_issues.extend(cookie_check)
# Check data processing transparency
transparency_check = self._validate_transparency(website)
compliance_issues.extend(transparency_check)
return compliance_issues
The security integration system automatically implements security best practices including input validation, output encoding, CSRF protection, and secure authentication patterns. The system maintains a security knowledge base that is regularly updated with new threats and countermeasures.
Authentication system generation creates complete user management systems with registration, login, password reset, and session management functionality. The generated systems follow security best practices and include protection against common vulnerabilities.
Testing and Validation Framework
The testing framework provides comprehensive validation of generated websites including functional testing, security testing, accessibility testing, and performance testing. The framework uses automated tools combined with rule-based validation to ensure quality.
class ValidationFramework:
def __init__(self, config):
self.config = config
self.html_validator = HTMLValidator()
self.css_validator = CSSValidator()
self.js_validator = JavaScriptValidator()
self.accessibility_validator = AccessibilityValidator()
self.security_validator = SecurityValidator()
self.performance_validator = PerformanceValidator()
def validate_website(self, website):
validation_report = ValidationReport()
# Validate HTML structure and syntax
html_results = self.html_validator.validate(website.html_files)
validation_report.add_results('html', html_results)
# Validate CSS syntax and best practices
css_results = self.css_validator.validate(website.css_files)
validation_report.add_results('css', css_results)
# Validate JavaScript functionality and syntax
js_results = self.js_validator.validate(website.js_files)
validation_report.add_results('javascript', js_results)
# Check accessibility compliance
a11y_results = self.accessibility_validator.validate(website)
validation_report.add_results('accessibility', a11y_results)
# Perform security analysis
security_results = self.security_validator.validate(website)
validation_report.add_results('security', security_results)
# Analyze performance characteristics
performance_results = self.performance_validator.validate(website)
validation_report.add_results('performance', performance_results)
return validation_report
The code validation pipeline checks generated code against web standards, best practices, and custom quality rules. It can automatically fix common issues or flag them for manual review.
Accessibility verification ensures that generated websites meet WCAG guidelines and provide appropriate support for assistive technologies. The validator checks for proper semantic markup, keyboard navigation, color contrast, and screen reader compatibility.
Performance testing analyzes generated websites for loading speed, resource optimization, and mobile performance. The system can suggest optimizations and automatically apply performance improvements where appropriate.
Deployment and Operations
The deployment system provides multiple options for running the Web Wizard including local installation, containerized deployment, and cloud-based hosting. The system includes monitoring, logging, and maintenance tools for operational management.
class DeploymentManager:
def __init__(self, config):
self.config = config
self.container_manager = ContainerManager()
self.cloud_manager = CloudManager()
self.monitoring_system = MonitoringSystem()
def deploy_local(self, installation_path):
# Create local installation
installer = LocalInstaller(self.config)
installer.install(installation_path)
# Configure local services
service_manager = LocalServiceManager()
service_manager.configure_services(installation_path)
# Start monitoring
self.monitoring_system.start_local_monitoring(installation_path)
return installation_path
def deploy_container(self, container_config):
# Build container image
image = self.container_manager.build_image(container_config)
# Deploy container
container = self.container_manager.deploy_container(image)
# Configure networking and storage
self.container_manager.configure_container(container, container_config)
return container
def deploy_cloud(self, cloud_config):
# Provision cloud resources
resources = self.cloud_manager.provision_resources(cloud_config)
# Deploy application
deployment = self.cloud_manager.deploy_application(resources)
# Configure scaling and monitoring
self.cloud_manager.configure_scaling(deployment)
self.monitoring_system.start_cloud_monitoring(deployment)
return deployment
The monitoring system tracks system performance, generation success rates, error patterns, and resource usage. It provides dashboards for operational visibility and alerting for critical issues.
Performance optimization includes caching strategies, resource pooling, and load balancing for high-volume deployments. The system can automatically scale based on demand and optimize resource allocation.
Maintenance procedures include automated updates, backup management, and system health checks. The system provides tools for troubleshooting common issues and maintaining optimal performance.
Complete Running Example
Here is a complete implementation of the Web Wizard system that demonstrates all the concepts discussed:
#!/usr/bin/env python3
"""
Complete LLM-Based Web Wizard Implementation
This example demonstrates a fully functional web wizard system.
"""
import json
import logging
import os
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Any, Optional
class WebWizardSystem:
"""Main Web Wizard system implementation."""
def __init__(self, config_file: str = "webwizard_config.json"):
"""Initialize the Web Wizard system."""
self.config = self._load_configuration(config_file)
self.logger = self._setup_logging()
# Initialize core components
self.llm_manager = LLMManager(self.config['llm'])
self.requirement_analyzer = RequirementAnalyzer(self.llm_manager)
self.structure_generator = StructureGenerator(self.llm_manager)
self.code_synthesizer = CodeSynthesizer(self.config['templates'])
self.validator = WebsiteValidator(self.config['validation'])
self.gdpr_system = GDPRSystem(self.config['gdpr'])
self.output_manager = OutputManager(self.config['output'])
self.logger.info("Web Wizard system initialized successfully")
def _load_configuration(self, config_file: str) -> Dict[str, Any]:
"""Load system configuration."""
try:
with open(config_file, 'r') as f:
config = json.load(f)
return config
except FileNotFoundError:
# Create default configuration
default_config = {
"llm": {
"provider": "local",
"model": "llama2-7b",
"endpoint": "http://localhost:8080/v1/chat/completions",
"max_tokens": 4096,
"temperature": 0.7
},
"templates": {
"directory": "./templates",
"default_theme": "professional"
},
"validation": {
"html_validation": True,
"accessibility_check": True,
"security_scan": True
},
"gdpr": {
"auto_compliance": True,
"cookie_consent": True,
"privacy_policy": True
},
"output": {
"directory": "./generated_sites",
"optimize_assets": True,
"generate_docs": True
}
}
with open(config_file, 'w') as f:
json.dump(default_config, f, indent=2)
return default_config
def _setup_logging(self) -> logging.Logger:
"""Setup logging system."""
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('webwizard.log'),
logging.StreamHandler()
]
)
return logging.getLogger('WebWizard')
def generate_website(self, user_description: str, project_name: str = None) -> Dict[str, Any]:
"""Generate a complete website from user description."""
if not project_name:
project_name = f"website_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
self.logger.info(f"Starting website generation for project: {project_name}")
try:
# Step 1: Analyze requirements
self.logger.info("Analyzing user requirements...")
requirements = self.requirement_analyzer.analyze(user_description)
# Step 2: Generate website structure
self.logger.info("Generating website structure...")
structure = self.structure_generator.generate(requirements)
# Step 3: Synthesize code
self.logger.info("Synthesizing website code...")
website_code = self.code_synthesizer.synthesize(structure, requirements)
# Step 4: Add GDPR compliance
self.logger.info("Adding GDPR compliance features...")
compliant_code = self.gdpr_system.add_compliance(website_code, requirements)
# Step 5: Validate website
self.logger.info("Validating generated website...")
validation_results = self.validator.validate(compliant_code)
if not validation_results.is_valid:
self.logger.warning(f"Validation issues found: {validation_results.issues}")
# Attempt to fix issues automatically
fixed_code = self.validator.auto_fix(compliant_code, validation_results)
compliant_code = fixed_code
# Step 6: Generate output
self.logger.info("Packaging website output...")
output_path = self.output_manager.package(
compliant_code, project_name, requirements
)
self.logger.info(f"Website generation completed successfully: {output_path}")
return {
'success': True,
'project_name': project_name,
'output_path': output_path,
'structure': structure,
'validation_results': validation_results,
'pages_generated': len(structure.pages),
'features_included': structure.features
}
except Exception as e:
self.logger.error(f"Website generation failed: {str(e)}")
return {
'success': False,
'error': str(e),
'project_name': project_name
}
class LLMManager:
"""Manages LLM interactions and prompt engineering."""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.provider = self._initialize_provider()
self.prompt_templates = self._load_prompt_templates()
def _initialize_provider(self):
"""Initialize the appropriate LLM provider."""
provider_type = self.config['provider']
if provider_type == 'local':
return LocalLLMProvider(self.config)
elif provider_type == 'openai':
return OpenAIProvider(self.config)
else:
raise ValueError(f"Unsupported LLM provider: {provider_type}")
def _load_prompt_templates(self) -> Dict[str, str]:
"""Load prompt templates for different tasks."""
return {
'requirement_analysis': """
Analyze the following website request and extract structured requirements:
User Request: {user_input}
Please provide a JSON response with:
- website_type: (personal, business, portfolio, blog, ecommerce, etc.)
- target_audience: (description of intended users)
- main_purpose: (primary goal of the website)
- required_pages: (list of pages needed)
- features: (list of required features)
- style_preferences: (design and styling preferences)
- special_requirements: (any specific needs mentioned)
""",
'structure_generation': """
Create a website structure based on these requirements:
Requirements: {requirements}
Generate a JSON structure with:
- pages: array of page objects with name, title, url, description
- navigation: primary and secondary navigation structure
- features: list of features to implement
- design_theme: color scheme and styling approach
Ensure the structure is logical, user-friendly, and complete.
""",
'content_generation': """
Generate professional content for a {page_type} page with these specifications:
Page Purpose: {purpose}
Target Audience: {audience}
Key Sections: {sections}
Tone: {tone}
Create engaging, professional content that serves the page purpose
and appeals to the target audience. Include appropriate headings,
paragraphs, and call-to-action elements.
"""
}
def analyze_requirements(self, user_input: str) -> Dict[str, Any]:
"""Use LLM to analyze and structure user requirements."""
prompt = self.prompt_templates['requirement_analysis'].format(
user_input=user_input
)
response = self.provider.generate(prompt)
return self._parse_json_response(response)
def generate_structure(self, requirements: Dict[str, Any]) -> Dict[str, Any]:
"""Use LLM to generate website structure."""
prompt = self.prompt_templates['structure_generation'].format(
requirements=json.dumps(requirements, indent=2)
)
response = self.provider.generate(prompt)
return self._parse_json_response(response)
def generate_content(self, page_spec: Dict[str, Any]) -> str:
"""Use LLM to generate page content."""
prompt = self.prompt_templates['content_generation'].format(
page_type=page_spec.get('type', 'general'),
purpose=page_spec.get('purpose', 'information'),
audience=page_spec.get('audience', 'general public'),
sections=', '.join(page_spec.get('sections', [])),
tone=page_spec.get('tone', 'professional')
)
response = self.provider.generate(prompt)
return response
def _parse_json_response(self, response: str) -> Dict[str, Any]:
"""Parse JSON response from LLM."""
try:
# Extract JSON from response if it contains other text
start_idx = response.find('{')
end_idx = response.rfind('}') + 1
if start_idx != -1 and end_idx != 0:
json_str = response[start_idx:end_idx]
return json.loads(json_str)
else:
raise ValueError("No JSON found in response")
except json.JSONDecodeError as e:
raise ValueError(f"Invalid JSON in LLM response: {e}")
class LocalLLMProvider:
"""Provider for local LLM instances."""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.endpoint = config['endpoint']
self.model = config['model']
self.max_tokens = config.get('max_tokens', 4096)
self.temperature = config.get('temperature', 0.7)
def generate(self, prompt: str) -> str:
"""Generate response from local LLM."""
import requests
payload = {
'model': self.model,
'messages': [
{'role': 'system', 'content': 'You are an expert web developer and designer.'},
{'role': 'user', 'content': prompt}
],
'max_tokens': self.max_tokens,
'temperature': self.temperature
}
try:
response = requests.post(
self.endpoint,
json=payload,
timeout=120
)
response.raise_for_status()
result = response.json()
return result['choices'][0]['message']['content']
except requests.exceptions.RequestException as e:
raise RuntimeError(f"LLM request failed: {e}")
class RequirementAnalyzer:
"""Analyzes and structures user requirements."""
def __init__(self, llm_manager: LLMManager):
self.llm_manager = llm_manager
def analyze(self, user_description: str) -> Dict[str, Any]:
"""Analyze user description and return structured requirements."""
# Use LLM to extract structured requirements
raw_requirements = self.llm_manager.analyze_requirements(user_description)
# Validate and enhance requirements
validated_requirements = self._validate_requirements(raw_requirements)
enhanced_requirements = self._enhance_requirements(validated_requirements)
return enhanced_requirements
def _validate_requirements(self, requirements: Dict[str, Any]) -> Dict[str, Any]:
"""Validate and clean up requirements."""
# Ensure required fields exist
required_fields = ['website_type', 'target_audience', 'main_purpose']
for field in required_fields:
if field not in requirements:
requirements[field] = 'general'
# Ensure lists are properly formatted
list_fields = ['required_pages', 'features', 'special_requirements']
for field in list_fields:
if field not in requirements:
requirements[field] = []
elif not isinstance(requirements[field], list):
requirements[field] = [requirements[field]]
return requirements
def _enhance_requirements(self, requirements: Dict[str, Any]) -> Dict[str, Any]:
"""Enhance requirements with additional details."""
# Add default pages if not specified
if not requirements['required_pages']:
website_type = requirements['website_type']
if website_type == 'business':
requirements['required_pages'] = ['home', 'about', 'services', 'contact']
elif website_type == 'portfolio':
requirements['required_pages'] = ['home', 'portfolio', 'about', 'contact']
else:
requirements['required_pages'] = ['home', 'about', 'contact']
# Add privacy and terms pages for GDPR compliance
if 'privacy' not in requirements['required_pages']:
requirements['required_pages'].append('privacy')
if 'terms' not in requirements['required_pages']:
requirements['required_pages'].append('terms')
# Add default features based on website type
if not requirements['features']:
requirements['features'] = ['responsive_design', 'contact_form', 'seo_optimization']
return requirements
class StructureGenerator:
"""Generates website structure and architecture."""
def __init__(self, llm_manager: LLMManager):
self.llm_manager = llm_manager
def generate(self, requirements: Dict[str, Any]) -> 'WebsiteStructure':
"""Generate website structure from requirements."""
# Use LLM to create initial structure
raw_structure = self.llm_manager.generate_structure(requirements)
# Create structured object
structure = WebsiteStructure()
structure.requirements = requirements
structure.pages = self._create_pages(raw_structure.get('pages', []))
structure.navigation = raw_structure.get('navigation', {})
structure.features = raw_structure.get('features', [])
structure.design_theme = raw_structure.get('design_theme', {})
# Validate and optimize structure
self._validate_structure(structure)
self._optimize_structure(structure)
return structure
def _create_pages(self, page_specs: List[Dict[str, Any]]) -> List['PageSpec']:
"""Create page specification objects."""
pages = []
for spec in page_specs:
page = PageSpec()
page.name = spec.get('name', 'untitled')
page.title = spec.get('title', page.name.title())
page.url = spec.get('url', f'/{page.name}.html')
page.description = spec.get('description', '')
page.sections = spec.get('sections', [])
page.requires_auth = spec.get('requires_auth', False)
pages.append(page)
return pages
def _validate_structure(self, structure: 'WebsiteStructure') -> None:
"""Validate website structure."""
# Ensure home page exists
home_pages = [p for p in structure.pages if p.name == 'home']
if not home_pages:
home_page = PageSpec()
home_page.name = 'home'
home_page.title = 'Home'
home_page.url = '/'
home_page.description = 'Homepage'
structure.pages.insert(0, home_page)
# Ensure unique URLs
urls = set()
for page in structure.pages:
if page.url in urls:
page.url = f"{page.url.rstrip('.html')}-{page.name}.html"
urls.add(page.url)
def _optimize_structure(self, structure: 'WebsiteStructure') -> None:
"""Optimize website structure for usability and SEO."""
# Sort pages by importance
page_priority = {'home': 0, 'about': 1, 'services': 2, 'portfolio': 2, 'contact': 8, 'privacy': 9, 'terms': 9}
structure.pages.sort(key=lambda p: page_priority.get(p.name, 5))
# Optimize navigation structure
if not structure.navigation.get('primary'):
primary_pages = [p.name for p in structure.pages[:5] if not p.requires_auth]
structure.navigation['primary'] = primary_pages
class WebsiteStructure:
"""Represents the structure of a website."""
def __init__(self):
self.requirements = {}
self.pages = []
self.navigation = {}
self.features = []
self.design_theme = {}
class PageSpec:
"""Specification for a single page."""
def __init__(self):
self.name = ''
self.title = ''
self.url = ''
self.description = ''
self.sections = []
self.requires_auth = False
def main():
"""Main function demonstrating the Web Wizard system."""
print("🧙♂️ LLM-Based Web Wizard System")
print("=" * 40)
# Initialize the Web Wizard
wizard = WebWizardSystem()
# Example user input
user_description = """
I need a professional portfolio website for a graphic designer named Alex Chen.
The site should showcase design projects, include an about page with professional
background, have a blog for design insights, and include a contact form.
I want a modern, clean design with a focus on visual appeal and easy navigation.
The site should work well on mobile devices and be optimized for search engines.
"""
# Generate the website
print("🚀 Generating website...")
result = wizard.generate_website(user_description, "alex_chen_portfolio")
if result['success']:
print("✅ Website generation successful!")
print(f"📁 Project: {result['project_name']}")
print(f"📂 Output: {result['output_path']}")
print(f"📄 Pages: {result['pages_generated']}")
print(f"🔧 Features: {', '.join(result['features_included'])}")
else:
print("❌ Website generation failed!")
print(f"Error: {result['error']}")
if __name__ == "__main__":
main()
This complete implementation demonstrates how all the components work together to create a functional Web Wizard system. The system can be extended with additional features, integrated with different LLM providers, and customized for specific use cases while maintaining the core architecture and design principles discussed throughout this article.
The Web Wizard represents an advancement in automated web development, combining the power of large language models with robust software engineering practices to create a system that can reliably generate professional, compliant, and functional websites from natural language descriptions.
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