d90d441019
- Blog writer enhancements and bug fixes - Wix integration improvements - Frontend UI updates - GSC dashboard docs cleanup - Image studio assets - LinkedIn requirements file - Various dependency updates
94 lines
3.8 KiB
Python
94 lines
3.8 KiB
Python
"""
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Competitor Analyzer - AI-powered competitor analysis for research content.
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Extracts competitor insights and market intelligence from research content.
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"""
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from typing import Dict, Any
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from loguru import logger
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import json
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class CompetitorAnalyzer:
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"""Analyzes competitors and market intelligence from research content."""
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def analyze(self, content: str, user_id: str = None) -> Dict[str, Any]:
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"""Parse comprehensive competitor analysis from the research content using AI."""
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competitor_prompt = f"""
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Analyze the following research content and extract competitor insights:
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Research Content:
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{content[:8000]}
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Extract and analyze:
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1. Top competitors mentioned (companies, brands, platforms)
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2. Content gaps (what competitors are missing)
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3. Opportunities (untapped areas)
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4. Competitive advantages (what makes content unique)
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5. Market positioning insights
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6. Industry leaders and their strategies
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Respond with JSON:
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{{
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"top_competitors": ["competitor1", "competitor2"],
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"content_gaps": ["gap1", "gap2"],
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"opportunities": ["opportunity1", "opportunity2"],
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"competitive_advantages": ["advantage1", "advantage2"],
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"market_positioning": "positioning insights",
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"industry_leaders": ["leader1", "leader2"],
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"analysis_notes": "Comprehensive competitor analysis summary"
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}}
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"""
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from services.llm_providers.main_text_generation import llm_text_gen
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competitor_schema = {
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"type": "object",
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"properties": {
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"top_competitors": {"type": "array", "items": {"type": "string"}},
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"content_gaps": {"type": "array", "items": {"type": "string"}},
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"opportunities": {"type": "array", "items": {"type": "string"}},
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"competitive_advantages": {"type": "array", "items": {"type": "string"}},
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"market_positioning": {"type": "string"},
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"industry_leaders": {"type": "array", "items": {"type": "string"}},
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"analysis_notes": {"type": "string"}
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},
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"required": ["top_competitors", "content_gaps", "opportunities", "competitive_advantages", "market_positioning", "industry_leaders", "analysis_notes"]
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}
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raw = llm_text_gen(
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prompt=competitor_prompt,
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user_id=user_id
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)
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# Parse JSON from LLM response (works with both string and dict return types)
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import re
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if isinstance(raw, str):
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cleaned = raw.strip()
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if cleaned.startswith('```json'):
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cleaned = cleaned[7:]
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if cleaned.startswith('```'):
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cleaned = cleaned[3:]
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if cleaned.endswith('```'):
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cleaned = cleaned[:-3]
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cleaned = cleaned.strip()
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try:
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competitor_analysis = json.loads(cleaned)
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except json.JSONDecodeError:
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json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
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if json_match:
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competitor_analysis = json.loads(json_match.group(0))
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else:
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raise ValueError(f"Competitor analysis returned non-JSON string: {cleaned[:200]}")
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elif isinstance(raw, dict):
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competitor_analysis = raw
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else:
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raise ValueError(f"Unexpected LLM response type: {type(raw)}")
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if 'error' in competitor_analysis:
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raise ValueError(f"Competitor analysis failed: {competitor_analysis.get('error', 'Unknown error')}")
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logger.info("✅ AI competitor analysis completed successfully")
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return competitor_analysis
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