Made changes to Getting started with ALwrity and added lot of details on API keys
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ajaysi
203
lib/personalization/style_analyzer.py
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203
lib/personalization/style_analyzer.py
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"""Style analyzer module for analyzing content style using LLM."""
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from typing import Dict, List, Optional
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from loguru import logger
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from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
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import json
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import re
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class StyleAnalyzer:
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"""Analyzer for content style using LLM."""
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def __init__(self):
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"""Initialize the style analyzer."""
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logger.info("[StyleAnalyzer.__init__] Initializing style analyzer")
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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"[StyleAnalyzer._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"[StyleAnalyzer._clean_json_response] Error cleaning response: {str(e)}")
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return ""
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def analyze_content_style(self, content: Dict) -> Dict:
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"""
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Analyze the style of the provided content.
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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
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"""
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try:
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logger.info("[StyleAnalyzer.analyze_content_style] Starting content style analysis")
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# Prepare content for analysis
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main_content = content.get("main_content", "")
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title = content.get("title", "")
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description = content.get("description", "")
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# Construct the analysis prompt
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prompt = f"""Analyze the following content and provide a comprehensive writing style analysis.
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Focus on identifying the writing style, tone, and characteristics that make this content unique.
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Title: {title}
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Description: {description}
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Content: {main_content[:4000]} # Limit content length for API
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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": "formal/casual/technical/etc",
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"voice": "active/passive",
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"complexity": "simple/moderate/complex",
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"engagement_level": "low/medium/high"
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}},
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"content_characteristics": {{
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"sentence_structure": "description",
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"vocabulary_level": "basic/intermediate/advanced",
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"paragraph_organization": "description",
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"content_flow": "description"
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}},
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"target_audience": {{
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"demographics": ["list"],
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"expertise_level": "beginner/intermediate/advanced",
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"industry_focus": "primary industry",
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"geographic_focus": "primary region"
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}},
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"content_type": {{
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"primary_type": "blog/article/product/etc",
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"secondary_types": ["list"],
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"purpose": "inform/entertain/persuade/etc",
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"call_to_action": "type and frequency"
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}},
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"recommended_settings": {{
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"writing_tone": "recommended tone",
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"target_audience": "recommended audience",
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"content_type": "recommended type",
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"creativity_level": "low/medium/high",
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"geographic_location": "recommended location"
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}}
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}}"""
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# Get analysis from LLM
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logger.debug("[StyleAnalyzer.analyze_content_style] Sending prompt to LLM")
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analysis_text = llm_text_gen(prompt)
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try:
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# Clean and parse the JSON response
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cleaned_json = self._clean_json_response(analysis_text)
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if not cleaned_json:
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raise ValueError("No valid JSON found in response")
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# Log the cleaned JSON for debugging
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logger.debug(f"[StyleAnalyzer.analyze_content_style] Cleaned JSON: {cleaned_json}")
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# Try to parse the cleaned JSON
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try:
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analysis = json.loads(cleaned_json)
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except json.JSONDecodeError as e:
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# If parsing fails, try to fix common JSON issues
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logger.warning(f"[StyleAnalyzer.analyze_content_style] Initial JSON parsing failed: {e}")
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# Fix any remaining issues
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cleaned_json = re.sub(r'([^"\\])\n', r'\1 ', cleaned_json)
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cleaned_json = re.sub(r'\\n', ' ', cleaned_json)
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# Try parsing again
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analysis = json.loads(cleaned_json)
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logger.info("[StyleAnalyzer.analyze_content_style] Successfully parsed analysis results")
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return analysis
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except json.JSONDecodeError as e:
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logger.error(f"[StyleAnalyzer.analyze_content_style] Failed to parse JSON response: {e}")
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logger.debug(f"[StyleAnalyzer.analyze_content_style] Raw response: {analysis_text}")
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return {
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"error": "Failed to parse analysis results",
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"raw_response": analysis_text
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}
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except Exception as e:
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logger.error(f"[StyleAnalyzer.analyze_content_style] Error during analysis: {str(e)}")
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return {
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"error": str(e),
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"success": False
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}
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def analyze_style_patterns(self, content: Dict) -> Dict:
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"""
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Analyze specific writing style patterns in the content.
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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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main_content = content.get("main_content", "")
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prompt = f"""Analyze the following content for specific writing style patterns.
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Focus on identifying recurring patterns in sentence structure, word choice, and rhetorical devices.
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Content: {main_content[:4000]}
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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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"sentence_patterns": {{
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"structure": ["list of patterns"],
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"length": "short/medium/long",
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"complexity": "simple/moderate/complex"
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}},
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"word_patterns": {{
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"vocabulary": ["list of patterns"],
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"frequency": "low/medium/high",
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"diversity": "low/medium/high"
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}},
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"rhetorical_devices": {{
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"types": ["list of devices"],
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"frequency": "low/medium/high",
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"effectiveness": "low/medium/high"
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}}
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}}"""
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analysis_text = llm_text_gen(prompt)
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try:
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cleaned_json = self._clean_json_response(analysis_text)
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if not cleaned_json:
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raise ValueError("No valid JSON found in response")
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analysis = json.loads(cleaned_json)
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return analysis
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except json.JSONDecodeError as e:
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logger.error(f"[StyleAnalyzer.analyze_style_patterns] Failed to parse JSON response: {e}")
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return {
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"error": "Failed to parse pattern analysis results",
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"raw_response": analysis_text
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}
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except Exception as e:
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logger.error(f"[StyleAnalyzer.analyze_style_patterns] Error during analysis: {str(e)}")
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return {
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"error": str(e),
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"success": False
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}
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