ALwrity Version 0.5.0 (Fastapi + React )
This commit is contained in:
ajaysi
499
backend/services/component_logic/style_detection_logic.py
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499
backend/services/component_logic/style_detection_logic.py
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"""Style Detection Logic Service for ALwrity Backend.
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This service handles business logic for content style detection and analysis,
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migrated from the legacy StyleAnalyzer functionality.
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"""
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from typing import Dict, Any, List, Optional
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from loguru import logger
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from datetime import datetime
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import json
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import re
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import sys
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import os
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# Add the backend directory to Python path for absolute imports
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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# Import the new backend LLM providers from services
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from ..llm_providers.main_text_generation import llm_text_gen
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class StyleDetectionLogic:
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"""Business logic for content style detection and analysis."""
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def __init__(self):
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"""Initialize the Style Detection Logic service."""
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logger.info("[StyleDetectionLogic.__init__] Initializing style detection service")
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def _clean_json_response(self, text: str) -> str:
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"""
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Clean the LLM response to extract valid JSON.
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Args:
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text (str): Raw response from LLM
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Returns:
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str: Cleaned JSON string
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"""
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try:
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# Remove markdown code block markers
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cleaned_string = text.replace("```json", "").replace("```", "").strip()
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# Log the cleaned JSON for debugging
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logger.debug(f"[StyleDetectionLogic._clean_json_response] Cleaned JSON: {cleaned_string}")
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return cleaned_string
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except Exception as e:
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logger.error(f"[StyleDetectionLogic._clean_json_response] Error cleaning response: {str(e)}")
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return ""
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def analyze_content_style(self, content: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Analyze the style of the provided content using AI with enhanced prompts.
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Args:
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content (Dict): Content to analyze, containing main_content, title, etc.
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Returns:
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Dict: Analysis results with writing style, characteristics, and recommendations
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"""
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try:
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logger.info("[StyleDetectionLogic.analyze_content_style] Starting enhanced style analysis")
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# Extract content components
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title = content.get('title', '')
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description = content.get('description', '')
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main_content = content.get('main_content', '')
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headings = content.get('headings', [])
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domain_info = content.get('domain_info', {})
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brand_info = content.get('brand_info', {})
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social_media = content.get('social_media', {})
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content_structure = content.get('content_structure', {})
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# Construct the enhanced analysis prompt
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prompt = f"""Analyze the following website content for comprehensive writing style, tone, and characteristics.
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This is a detailed analysis for content personalization and AI-powered content generation.
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WEBSITE INFORMATION:
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- Domain: {domain_info.get('domain_name', 'Unknown')}
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- Website Type: {self._determine_website_type(domain_info)}
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- Brand Name: {brand_info.get('company_name', 'Not specified')}
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- Tagline: {brand_info.get('tagline', 'Not specified')}
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- Social Media Presence: {', '.join(social_media.keys()) if social_media else 'None detected'}
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CONTENT STRUCTURE:
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- Headings: {len(headings)} total ({content_structure.get('headings', {}).get('h1', 0)} H1, {content_structure.get('headings', {}).get('h2', 0)} H2)
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- Paragraphs: {content_structure.get('paragraphs', 0)}
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- Images: {content_structure.get('images', 0)}
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- Links: {content_structure.get('links', 0)}
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- Has Navigation: {content_structure.get('has_navigation', False)}
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- Has Call-to-Action: {content_structure.get('has_call_to_action', False)}
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CONTENT TO ANALYZE:
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Title: {title}
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Description: {description}
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Main Content: {main_content[:5000]} # Enhanced content length
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Key Headings: {headings[:10]} # First 10 headings for context
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ANALYSIS REQUIREMENTS:
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1. Analyze the writing style, tone, and voice characteristics
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2. Identify target audience demographics and expertise level
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3. Determine content type and purpose
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4. Assess content structure and organization patterns
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5. Evaluate brand voice consistency and personality
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6. Identify unique style elements and patterns
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7. Consider the website type and industry context
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8. Analyze social media presence impact on content style
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IMPORTANT: Respond ONLY with a JSON object in the following format. Do not include any additional text, explanations, or markdown formatting:
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{{
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"writing_style": {{
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"tone": "detailed tone description with context",
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"voice": "active/passive with explanation",
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"complexity": "simple/moderate/complex with reasoning",
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"engagement_level": "low/medium/high with justification",
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"brand_personality": "detailed brand personality analysis",
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"formality_level": "casual/semi-formal/formal/professional",
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"emotional_appeal": "rational/emotional/mixed with examples"
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}},
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"content_characteristics": {{
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"sentence_structure": "detailed analysis of sentence patterns",
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"vocabulary_level": "basic/intermediate/advanced with examples",
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"paragraph_organization": "detailed structure analysis",
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"content_flow": "detailed flow analysis",
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"readability_score": "estimated readability level",
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"content_density": "high/medium/low with reasoning",
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"visual_elements_usage": "analysis of how visual elements complement text"
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}},
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"target_audience": {{
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"demographics": ["detailed demographic analysis"],
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"expertise_level": "beginner/intermediate/advanced with reasoning",
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"industry_focus": "detailed industry analysis",
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"geographic_focus": "detailed geographic analysis",
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"psychographic_profile": "detailed psychographic analysis",
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"pain_points": ["identified audience pain points"],
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"motivations": ["identified audience motivations"]
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}},
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"content_type": {{
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"primary_type": "detailed content type analysis",
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"secondary_types": ["list of secondary content types"],
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"purpose": "detailed content purpose analysis",
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"call_to_action": "detailed CTA analysis",
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"conversion_focus": "high/medium/low with reasoning",
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"educational_value": "high/medium/low with reasoning"
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}},
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"brand_analysis": {{
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"brand_voice": "detailed brand voice analysis",
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"brand_values": ["identified brand values"],
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"brand_positioning": "detailed positioning analysis",
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"competitive_differentiation": "detailed differentiation analysis",
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"trust_signals": ["identified trust elements"],
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"authority_indicators": ["identified authority elements"]
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}},
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"content_strategy_insights": {{
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"strengths": ["content strengths"],
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"weaknesses": ["content weaknesses"],
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"opportunities": ["content opportunities"],
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"threats": ["content threats"],
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"recommended_improvements": ["specific improvement suggestions"],
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"content_gaps": ["identified content gaps"]
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}},
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"recommended_settings": {{
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"writing_tone": "recommended tone for AI generation",
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"target_audience": "recommended audience focus",
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"content_type": "recommended content type",
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"creativity_level": "low/medium/high with reasoning",
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"geographic_location": "recommended geographic focus",
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"industry_context": "recommended industry approach",
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"brand_alignment": "recommended brand alignment strategy"
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}}
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}}
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"""
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# Call the LLM for analysis
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logger.debug("[StyleDetectionLogic.analyze_content_style] Sending enhanced prompt to LLM")
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analysis_text = llm_text_gen(prompt)
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# Clean and parse the response
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cleaned_json = self._clean_json_response(analysis_text)
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try:
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analysis_results = json.loads(cleaned_json)
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logger.info("[StyleDetectionLogic.analyze_content_style] Successfully parsed enhanced analysis results")
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return {
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'success': True,
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'analysis': analysis_results
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}
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except json.JSONDecodeError as e:
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logger.error(f"[StyleDetectionLogic.analyze_content_style] Failed to parse JSON response: {e}")
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logger.debug(f"[StyleDetectionLogic.analyze_content_style] Raw response: {analysis_text}")
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return {
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'success': False,
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'error': 'Failed to parse analysis response'
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}
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except Exception as e:
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logger.error(f"[StyleDetectionLogic.analyze_content_style] Error in enhanced analysis: {str(e)}")
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return {
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'success': False,
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'error': str(e)
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}
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def _determine_website_type(self, domain_info: Dict[str, Any]) -> str:
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"""Determine the type of website based on domain and content analysis."""
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if domain_info.get('is_blog'):
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return 'Blog/Content Platform'
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elif domain_info.get('is_ecommerce'):
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return 'E-commerce/Online Store'
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elif domain_info.get('is_corporate'):
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return 'Corporate/Business Website'
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elif domain_info.get('has_blog_section'):
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return 'Business with Blog'
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elif domain_info.get('has_about_page') and domain_info.get('has_contact_page'):
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return 'Professional Services'
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else:
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return 'General Website'
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def _get_fallback_analysis(self, content: Dict[str, Any]) -> Dict[str, Any]:
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"""Get fallback analysis when LLM analysis fails."""
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main_content = content.get("main_content", "")
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title = content.get("title", "")
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# Simple content analysis based on content characteristics
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content_length = len(main_content)
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word_count = len(main_content.split())
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# Determine tone based on content characteristics
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if any(word in main_content.lower() for word in ['professional', 'business', 'industry', 'company']):
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tone = "professional"
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elif any(word in main_content.lower() for word in ['casual', 'fun', 'enjoy', 'exciting']):
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tone = "casual"
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else:
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tone = "neutral"
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# Determine complexity based on sentence length and vocabulary
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avg_sentence_length = word_count / max(len([s for s in main_content.split('.') if s.strip()]), 1)
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if avg_sentence_length > 20:
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complexity = "complex"
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elif avg_sentence_length > 15:
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complexity = "moderate"
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else:
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complexity = "simple"
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return {
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"writing_style": {
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"tone": tone,
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"voice": "active",
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"complexity": complexity,
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"engagement_level": "medium"
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},
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"content_characteristics": {
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"sentence_structure": "standard",
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"vocabulary_level": "intermediate",
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"paragraph_organization": "logical",
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"content_flow": "smooth"
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},
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"target_audience": {
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"demographics": ["general audience"],
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"expertise_level": "intermediate",
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"industry_focus": "general",
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"geographic_focus": "global"
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},
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"content_type": {
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"primary_type": "article",
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"secondary_types": ["blog", "content"],
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"purpose": "inform",
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"call_to_action": "minimal"
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},
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"recommended_settings": {
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"writing_tone": tone,
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"target_audience": "general audience",
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"content_type": "article",
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"creativity_level": "medium",
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"geographic_location": "global"
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}
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}
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def analyze_style_patterns(self, content: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Analyze recurring patterns in the content style.
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Args:
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content (Dict): Content to analyze
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Returns:
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Dict: Pattern analysis results
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"""
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try:
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logger.info("[StyleDetectionLogic.analyze_style_patterns] Starting pattern analysis")
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main_content = content.get("main_content", "")
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prompt = f"""Analyze the following content for recurring writing patterns and style characteristics.
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Focus on identifying patterns in sentence structure, vocabulary usage, and writing techniques.
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Content: {main_content[:3000]}
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IMPORTANT: Respond ONLY with a JSON object in the following format:
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{{
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"patterns": {{
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"sentence_length": "short/medium/long",
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"vocabulary_patterns": ["list of patterns"],
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"rhetorical_devices": ["list of devices used"],
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"paragraph_structure": "description",
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"transition_phrases": ["list of common transitions"]
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}},
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"style_consistency": "high/medium/low",
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"unique_elements": ["list of unique style elements"]
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}}
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"""
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analysis_text = llm_text_gen(prompt)
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cleaned_json = self._clean_json_response(analysis_text)
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try:
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pattern_results = json.loads(cleaned_json)
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return {
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'success': True,
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'patterns': pattern_results
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}
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except json.JSONDecodeError as e:
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logger.error(f"[StyleDetectionLogic.analyze_style_patterns] Failed to parse JSON response: {e}")
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return {
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'success': False,
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'error': 'Failed to parse pattern analysis response'
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}
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except Exception as e:
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logger.error(f"[StyleDetectionLogic.analyze_style_patterns] Error during analysis: {str(e)}")
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return {
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'success': False,
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'error': str(e)
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}
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def generate_style_guidelines(self, analysis_results: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Generate comprehensive content guidelines based on enhanced style analysis.
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Args:
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analysis_results (Dict): Results from enhanced style analysis
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Returns:
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Dict: Generated comprehensive guidelines
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"""
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try:
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logger.info("[StyleDetectionLogic.generate_style_guidelines] Generating comprehensive style guidelines")
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# Extract key information from analysis
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writing_style = analysis_results.get('writing_style', {})
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content_characteristics = analysis_results.get('content_characteristics', {})
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target_audience = analysis_results.get('target_audience', {})
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brand_analysis = analysis_results.get('brand_analysis', {})
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content_strategy_insights = analysis_results.get('content_strategy_insights', {})
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prompt = f"""Based on the following comprehensive style analysis, generate detailed content creation guidelines for AI-powered content generation.
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ANALYSIS DATA:
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Writing Style: {writing_style}
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Content Characteristics: {content_characteristics}
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Target Audience: {target_audience}
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Brand Analysis: {brand_analysis}
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Content Strategy Insights: {content_strategy_insights}
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REQUIREMENTS:
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1. Create actionable guidelines for AI content generation
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2. Provide specific recommendations for maintaining brand voice
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3. Include strategies for audience engagement
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4. Address content gaps and opportunities
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5. Consider competitive positioning
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6. Provide technical writing recommendations
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7. Include SEO and conversion optimization tips
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8. Address content structure and formatting
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IMPORTANT: Respond ONLY with a JSON object in the following format:
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{{
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"guidelines": {{
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"tone_recommendations": [
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"specific tone guidelines with examples",
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"brand voice consistency tips",
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"emotional appeal strategies"
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],
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"structure_guidelines": [
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"content structure recommendations",
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"formatting best practices",
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"organization strategies"
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],
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"vocabulary_suggestions": [
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"specific vocabulary recommendations",
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"industry terminology guidance",
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"language complexity advice"
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],
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"engagement_tips": [
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"audience engagement strategies",
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"interaction techniques",
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"conversion optimization tips"
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],
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"audience_considerations": [
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"specific audience targeting advice",
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"pain point addressing strategies",
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"motivation-based content tips"
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],
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"brand_alignment": [
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"brand voice consistency guidelines",
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"brand value integration tips",
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"competitive differentiation strategies"
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],
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"seo_optimization": [
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"keyword integration strategies",
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"content optimization tips",
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"search visibility recommendations"
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],
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"conversion_optimization": [
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"call-to-action strategies",
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"conversion funnel optimization",
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"lead generation techniques"
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]
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||||
}},
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"best_practices": [
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"comprehensive best practices list",
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"industry-specific recommendations",
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"quality assurance guidelines"
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],
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"avoid_elements": [
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"elements to avoid with explanations",
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"common pitfalls to prevent",
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"brand-inappropriate content types"
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],
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"content_strategy": "comprehensive content strategy recommendation with specific action items",
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"ai_generation_tips": [
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"specific tips for AI content generation",
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"prompt optimization strategies",
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"quality control measures"
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],
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"competitive_advantages": [
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"identified competitive advantages",
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"differentiation strategies",
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"market positioning recommendations"
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],
|
||||
"content_calendar_suggestions": [
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"content frequency recommendations",
|
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"topic planning strategies",
|
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"seasonal content opportunities"
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||||
]
|
||||
}}
|
||||
"""
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||||
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||||
guidelines_text = llm_text_gen(prompt)
|
||||
cleaned_json = self._clean_json_response(guidelines_text)
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||||
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||||
try:
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||||
guidelines = json.loads(cleaned_json)
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||||
return {
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||||
'success': True,
|
||||
'guidelines': guidelines
|
||||
}
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"[StyleDetectionLogic.generate_style_guidelines] Failed to parse JSON response: {e}")
|
||||
return {
|
||||
'success': False,
|
||||
'error': 'Failed to parse guidelines response'
|
||||
}
|
||||
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||||
except Exception as e:
|
||||
logger.error(f"[StyleDetectionLogic.generate_style_guidelines] Error generating guidelines: {str(e)}")
|
||||
return {
|
||||
'success': False,
|
||||
'error': str(e)
|
||||
}
|
||||
|
||||
def validate_style_analysis_request(self, request_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Validate style analysis request data.
|
||||
|
||||
Args:
|
||||
request_data (Dict): Request data to validate
|
||||
|
||||
Returns:
|
||||
Dict: Validation results
|
||||
"""
|
||||
errors = []
|
||||
|
||||
# Check if content is provided
|
||||
if not request_data.get('content') and not request_data.get('url') and not request_data.get('text_sample'):
|
||||
errors.append("Content is required for style analysis")
|
||||
|
||||
# Check content length
|
||||
content = request_data.get('content', {})
|
||||
main_content = content.get('main_content', '')
|
||||
if len(main_content) < 50:
|
||||
errors.append("Content must be at least 50 characters long for meaningful analysis")
|
||||
|
||||
# Check for required fields
|
||||
if not content.get('title') and not content.get('main_content'):
|
||||
errors.append("Either title or main content must be provided")
|
||||
|
||||
return {
|
||||
'valid': len(errors) == 0,
|
||||
'errors': errors
|
||||
}
|
||||
Reference in New Issue
Block a user