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| """Complete Search Intelligence Analysis - Clean Professional Output.""" | |
| from typing import Dict, List | |
| from server.keyword_engine import extract_keywords_from_audit | |
| from server.keyword_analytics import analyze_keywords, clean_keyword, is_valid_keyword, cluster_by_topic | |
| from server.competitor_analysis import detect_competitors, get_competitor_summary | |
| from server.dataforseo_client import enrich_keywords | |
| try: | |
| from . import ai_analysis | |
| except ImportError: | |
| import ai_analysis | |
| def cluster_topics_ai(analytics: Dict, api_keys: dict = None) -> Dict: | |
| """Group keywords into semantic clusters using LLM if available.""" | |
| if ai_analysis and (api_keys or {}): | |
| kws = [k['kw'] for k in analytics.get('top_keywords', [])[:20]] | |
| prompt = ( | |
| f"Group these SEO keywords into 3-5 semantic topic clusters with descriptive names. " | |
| f"Return ONLY JSON object where keys are cluster names and values are lists of keywords: {', '.join(kws)}" | |
| ) | |
| res = None | |
| try: | |
| if (api_keys or {}).get('groq'): | |
| res = ai_analysis.analyze_with_groq([{'url': 'dummy', 'text': prompt}], api_key=api_keys['groq']) | |
| elif (api_keys or {}).get('openai'): | |
| res = ai_analysis.analyze_with_openai([{'url': 'dummy', 'text': prompt}], api_key=api_keys['openai']) | |
| except Exception: res = None | |
| if res and res.get('result') and isinstance(res['result'], dict): | |
| # Transform to expected format: {topic: {'count': N, 'keywords': [...]}} | |
| final_clusters = {} | |
| raw_clusters = res['result'] | |
| all_kws_map = {k['kw']: k for k in analytics.get('top_keywords', [])} | |
| for topic, kw_list in raw_clusters.items(): | |
| if isinstance(kw_list, list): | |
| cluster_kws = [] | |
| for k in kw_list: | |
| if k in all_kws_map: | |
| cluster_kws.append(all_kws_map[k]) | |
| else: | |
| cluster_kws.append({'kw': k, 'count': 1}) | |
| final_clusters[topic] = { | |
| 'count': len(cluster_kws), | |
| 'keywords': sorted(cluster_kws, key=lambda x: x['count'], reverse=True)[:5] | |
| } | |
| if final_clusters: | |
| return final_clusters | |
| # Fallback to rule-based clustering | |
| raw_clusters = cluster_by_topic(analytics.get('top_keywords', [])) | |
| formatted = {} | |
| for topic, kws in raw_clusters.items(): | |
| formatted[topic] = { | |
| 'count': len(kws), | |
| 'keywords': kws[:5] | |
| } | |
| return formatted | |
| def analyze_search_intent_ai(analytics: Dict, pages: List[Dict], api_keys: dict = None) -> Dict: | |
| """Classify keyword search intent distribution using LLM if available.""" | |
| if ai_analysis and (api_keys or {}): | |
| # We can use a lightweight prompt to classify the top 15 keywords | |
| kws = [k['kw'] for k in analytics.get('top_keywords', [])[:15]] | |
| prompt = f"Classify the search intent of these keywords into Informational, Commercial, Transactional, Navigational. Return ONLY JSON distribution like {{'Informational': 40, 'Commercial': 30, ...}} based on their prevalence: {', '.join(kws)}" | |
| # Try Groq or OpenAI | |
| res = None | |
| try: | |
| if (api_keys or {}).get('groq'): | |
| res = ai_analysis.analyze_with_groq([{'url': 'dummy', 'text': prompt}], api_key=api_keys['groq']) | |
| elif (api_keys or {}).get('openai'): | |
| res = ai_analysis.analyze_with_openai([{'url': 'dummy', 'text': prompt}], api_key=api_keys['openai']) | |
| except Exception: | |
| res = None | |
| if res and res.get('result'): | |
| dist = res['result'] | |
| # Ensure keys exist | |
| for k in ['Informational', 'Commercial', 'Transactional', 'Navigational']: | |
| if k not in dist: dist[k] = 0 | |
| # Ensure key is a function for max | |
| top_intent = max(dist.keys(), key=lambda k: dist.get(k, 0)) | |
| return { 'distribution': dist, 'top_intent': top_intent } | |
| # Fallback to simple rule-based | |
| return analyze_search_intent(analytics) | |
| def analyze_search_intent(analytics: Dict) -> Dict: | |
| """Classify keyword search intent distribution (Rule-based, 2026 standards).""" | |
| intents = {'Informational': 0, 'Commercial': 0, 'Transactional': 0, 'Navigational': 0} | |
| kws = analytics.get('top_keywords', []) | |
| if not kws: return {'distribution': intents, 'top_intent': 'N/A'} | |
| TRANSACTIONAL = ['buy','shop','price','sale','order','store','checkout','discount','offer','deal', | |
| 'سعر','شراء','طلب','متجر','عرض','خصم','اشتري','احجز','book','subscribe','hire','get'] | |
| COMMERCIAL = ['best','review','vs','compare','top','rating','alternative','agency','service','company', | |
| 'افضل','أفضل','مراجعة','مقارنة','شركة','خدمة','وكالة','provider','solution','platform'] | |
| INFORMATIONAL = ['how','what','why','guide','tutorial','tips','trends','learn','understand','explain', | |
| 'كيف','ماذا','لماذا','شرح','نصائح','دليل','تعلم','مقال','blog','article','case study'] | |
| # Navigational = brand/domain names only — NOT the default | |
| for item in kws: | |
| kw = item['kw'].lower() | |
| if any(w in kw for w in TRANSACTIONAL): | |
| item['intent'] = 'Transactional' | |
| elif any(w in kw for w in COMMERCIAL): | |
| item['intent'] = 'Commercial' | |
| elif any(w in kw for w in INFORMATIONAL): | |
| item['intent'] = 'Informational' | |
| elif len(kw.split()) == 1 and kw.isalpha(): # single brand-like word | |
| item['intent'] = 'Navigational' | |
| else: | |
| # Default to Commercial for service/agency pages (not Navigational) | |
| item['intent'] = 'Commercial' | |
| intents[item['intent']] += item.get('count', 1) | |
| total = sum(intents.values()) or 1 | |
| dist = {k: round((v / total) * 100, 1) for k, v in intents.items()} | |
| top_intent = max(intents, key=lambda k: intents[k]) | |
| return {'distribution': dist, 'top_intent': top_intent} | |
| def detect_content_gaps_ai(analytics: Dict, pages: List[Dict], api_keys: dict = None) -> List[str]: | |
| """Identify real content gaps via LLM.""" | |
| if ai_analysis and (api_keys or {}): | |
| kws = [k['kw'] for k in analytics.get('top_keywords', [])[:20]] | |
| prompt = f"Based on these keywords found on a website: {', '.join(kws)}, identify 4 specific SEO content gaps or missing subtopics. Return ONLY a JSON list of strings." | |
| res = None | |
| try: | |
| if (api_keys or {}).get('groq'): | |
| res = ai_analysis.analyze_with_groq([{'url': 'dummy', 'text': prompt}], api_key=api_keys['groq']) | |
| elif (api_keys or {}).get('openai'): | |
| res = ai_analysis.analyze_with_openai([{'url': 'dummy', 'text': prompt}], api_key=api_keys['openai']) | |
| except Exception: | |
| res = None | |
| if res and res.get('result') and isinstance(res['result'], list): | |
| return res['result'] | |
| # Better generic fallback | |
| top_kw = "this topic" | |
| if analytics.get('top_keywords') and len(analytics['top_keywords']) > 0: | |
| top_kw = analytics['top_keywords'][0].get('kw', 'this topic') | |
| return [f'Advanced {top_kw} guide', 'Industry case studies', 'Latest trends in this sector', 'Expert Q&A'] | |
| def calculate_quality_score_ai(analytics: Dict, pages: List[Dict], api_keys: dict = None) -> Dict: | |
| """Calculate overall content quality score using LLM for depth.""" | |
| if ai_analysis and (api_keys or {}): | |
| text_sample = " ".join([p.get('text', '')[:600] for p in pages[:2]]) | |
| kws = [k['kw'] for k in analytics.get('top_keywords', [])[:15]] | |
| prompt = ( | |
| f"PROFESSIONAL SEO AUDIT: Analyze this page content. " | |
| f"Keywords: {', '.join(kws)}. CONTENT: {text_sample}. " | |
| "Evaluate: 1. Semantic Depth 2. Keyword Placement 3. Readability. " | |
| "Return JSON: {'score': 0-100, 'grade': 'A-F', 'feedback': ['list', 'of', '3', 'professional', 'critical', 'notes']}" | |
| ) | |
| res = None | |
| try: | |
| if (api_keys or {}).get('groq'): | |
| res = ai_analysis.analyze_with_groq([{'url': 'dummy', 'text': prompt}], api_key=api_keys['groq']) | |
| elif (api_keys or {}).get('openai'): | |
| res = ai_analysis.analyze_with_openai([{'url': 'dummy', 'text': prompt}], api_key=api_keys['openai']) | |
| except Exception: res = None | |
| if res and res.get('result') and isinstance(res['result'], dict): | |
| r = res['result'] | |
| return { | |
| 'score': r.get('score', 70), | |
| 'max_score': 100, | |
| 'percentage': r.get('score', 70), | |
| 'grade': r.get('grade', 'C'), | |
| 'feedback': r.get('feedback', ['AI Audit completed']) | |
| } | |
| # Fallback to heuristic | |
| return calculate_quality_score(analytics) | |
| def simulate_serp_intelligence_ai(analytics: Dict, url: str, api_keys: dict = None) -> List[Dict]: | |
| """Generate SERP landscape using AI with content type and intent context.""" | |
| primary_kw = analytics['top_keywords'][0]['kw'] if analytics.get('top_keywords') else 'digital marketing' | |
| if ai_analysis and (api_keys or {}): | |
| prompt = ( | |
| f"Generate 5 realistic Google SERP results for the keyword '{primary_kw}'. " | |
| f"Include a mix of content types (blog, service page, guide, tool, directory). " | |
| f"Return ONLY a JSON list with keys: rank(1-5), domain, dr(0-100), " | |
| f"backlinks(string), length(word count), content_type(blog/service/guide/tool), " | |
| f"intent(Informational/Commercial/Transactional), why_ranks(one sentence)." | |
| ) | |
| res = None | |
| try: | |
| if (api_keys or {}).get('groq'): | |
| res = ai_analysis.analyze_with_groq([{'url': 'dummy', 'text': prompt}], api_key=api_keys['groq']) | |
| elif (api_keys or {}).get('openai'): | |
| res = ai_analysis.analyze_with_openai([{'url': 'dummy', 'text': prompt}], api_key=api_keys['openai']) | |
| except Exception: | |
| res = None | |
| if res and res.get('result') and isinstance(res['result'], list): | |
| return res['result'] | |
| # Honest fallback — clearly marked as estimated | |
| domain = url.split('//')[-1].split('/')[0] if url and '//' in url else 'your-site.com' | |
| return [ | |
| {'rank':1,'domain':'wikipedia.org','dr':98,'backlinks':'50k+','length':4500, | |
| 'content_type':'encyclopedia','intent':'Informational','why_ranks':'Highest authority + comprehensive coverage'}, | |
| {'rank':2,'domain':'neilpatel.com','dr':90,'backlinks':'12k','length':3200, | |
| 'content_type':'guide','intent':'Informational','why_ranks':'Deep long-form guide with strong backlinks'}, | |
| {'rank':3,'domain':'ahrefs.com','dr':89,'backlinks':'8k','length':2800, | |
| 'content_type':'tool/blog','intent':'Commercial','why_ranks':'Tool + data-driven content'}, | |
| {'rank':4,'domain':'moz.com','dr':87,'backlinks':'6k','length':2400, | |
| 'content_type':'guide','intent':'Informational','why_ranks':'Trusted SEO authority'}, | |
| {'rank':5,'domain':domain,'dr':0,'backlinks':'0','length':0, | |
| 'content_type':'your site','intent':'Unknown','why_ranks':'⚠️ Not ranking yet — this is your opportunity gap'}, | |
| ] | |
| def get_market_intelligence_ai(competitors: List[Dict], summary: Dict, analytics: Dict, api_keys: dict = None) -> Dict: | |
| """ | |
| Perform a deep-dive AI analysis on competitors and market positioning. | |
| Extends simple detection with success factors, positioning maps, and gap analysis. | |
| """ | |
| market_list = competitors | |
| analysis_results = { | |
| 'positioning_map': [], | |
| 'success_factors': [], | |
| 'competitive_gaps': [], | |
| 'market_grade': 'B' | |
| } | |
| if ai_analysis and (api_keys or {}): | |
| top_kws = [k['kw'] for k in analytics.get('top_keywords', [])[:15]] | |
| # Prepare competitor context for AI | |
| comp_context = "" | |
| for c in market_list[:5]: | |
| ctxts = " | ".join(c.get('contexts', [])) | |
| comp_context += f"- {c['domain']} (Mentions: {c['mentions']}). Context: {ctxts}\n" | |
| prompt = f""" | |
| Analyze these online competitors and niche trends based on these keywords: {', '.join(top_kws)}. | |
| Detected Competitors: | |
| {comp_context if comp_context else "None detected yet. Suggest top 5 industry leaders."} | |
| Produce a Strategic Intelligence Matrix in JSON format: | |
| {{ | |
| "positioning_map": [ | |
| {{"name": "Domain", "x": -100..100 (Authority), "y": -100..100 (Focus), "role": "Leader/Niche/Challenger"}} | |
| ], | |
| "success_factors": [ | |
| {{"factor": "Specific Strategy", "impact": "High/Medium", "competitors": ["domain1", "domain2"]}} | |
| ], | |
| "competitive_gaps": [ | |
| {{"gap": "Underserved Area", "opportunity": "High/Low", "description": "Why the user can win here"}} | |
| ], | |
| "market_grade": "A/B/C/D", | |
| "discovered_competitors": [ | |
| {{"domain": "string", "mentions": "Market Discovery"}} | |
| ] | |
| }} | |
| Return ONLY valid JSON. | |
| """ | |
| res = None | |
| try: | |
| if (api_keys or {}).get('groq'): | |
| res = ai_analysis.analyze_with_groq([{'url': 'dummy', 'text': prompt}], api_key=api_keys['groq']) | |
| elif (api_keys or {}).get('openai'): | |
| res = ai_analysis.analyze_with_openai([{'url': 'dummy', 'text': prompt}], api_key=api_keys['openai']) | |
| except Exception: res = None | |
| if res and res.get('result') and isinstance(res['result'], dict): | |
| intel = res['result'] | |
| analysis_results.update(intel) | |
| # If we discovered new competitors, add them to the list | |
| if not market_list and intel.get('discovered_competitors'): | |
| market_list = intel['discovered_competitors'] | |
| summary = { | |
| 'total': len(market_list), | |
| 'avg_mentions': 0, | |
| 'top_competitor': market_list[0]['domain'], | |
| 'top_mentions': 0 | |
| } | |
| return { | |
| 'found': len(market_list), | |
| 'summary': summary, | |
| 'list': market_list[:10], | |
| 'strategic_intel': analysis_results | |
| } | |
| def generate_recommendations_ai(analytics: Dict, competitors: List[Dict], api_keys: dict = None) -> List[Dict]: | |
| """Generate high-impact actionable recommendations via AI.""" | |
| if ai_analysis and (api_keys or {}): | |
| kws = [k['kw'] for k in analytics.get('top_keywords', [])[:15]] | |
| prompt = f"Based on these keywords: {', '.join(kws)}, provide 4 high-impact SEO recommendations. Return ONLY JSON list of objects: {{'type': 'string', 'priority': 'high/medium/low', 'title': 'short string', 'description': 'string', 'action': 'string'}}" | |
| res = None | |
| try: | |
| if (api_keys or {}).get('groq'): | |
| res = ai_analysis.analyze_with_groq([{'url': 'dummy', 'text': prompt}], api_key=api_keys['groq']) | |
| elif (api_keys or {}).get('openai'): | |
| res = ai_analysis.analyze_with_openai([{'url': 'dummy', 'text': prompt}], api_key=api_keys['openai']) | |
| except Exception: res = None | |
| if res and res.get('result') and isinstance(res['result'], list): | |
| return res['result'] | |
| return generate_recommendations(analytics, competitors) | |
| def calculate_opportunity_score_smart(kw_item: Dict, quality_score: int) -> int: | |
| """Opportunity = (Volume / Difficulty) weighted by current page quality.""" | |
| # Volume: use real volume if available, else count-based proxy | |
| volume = kw_item.get('volume') | |
| if not volume or volume == '—' or volume == 0: | |
| volume = kw_item.get('count', 1) * 300 # More realistic base | |
| # Difficulty: heuristic if not available | |
| difficulty = kw_item.get('difficulty') or (20 + kw_item.get('count', 1) * 3) | |
| difficulty = max(10, min(95, difficulty)) | |
| # Base score: Higher volume = higher opportunity, Higher difficulty = lower | |
| # Formula: (Volume^0.5 / Difficulty) * 10 | |
| base_score = (pow(volume, 0.5) / difficulty) * 50 | |
| # Adjust by quality: if page quality is low (e.g. 40), opportunity to rank for this keyword by fixing content is HIGH | |
| quality_mult = (100 - quality_score) / 50.0 # e.g. (100-30)/50 = 1.4x bonus | |
| score = int(base_score * quality_mult) | |
| # Normalize | |
| return max(1, min(99, score)) | |
| def extract_semantic_entities_ai(pages: List[Dict], api_keys: dict = None) -> Dict: | |
| """Extract real semantic entities via LLM.""" | |
| if ai_analysis and (api_keys or {}): | |
| text = " ".join([p.get('text', '')[:500] for p in pages[:2]]) | |
| prompt = f"Extract semantic entities from this text. Return JSON with keys: Brand, Category, Product, Audience, Location. TEXT: {text}" | |
| res = None | |
| try: | |
| if (api_keys or {}).get('groq'): | |
| res = ai_analysis.analyze_with_groq([{'url': 'dummy', 'text': prompt}], api_key=api_keys['groq']) | |
| elif (api_keys or {}).get('openai'): | |
| res = ai_analysis.analyze_with_openai([{'url': 'dummy', 'text': prompt}], api_key=api_keys['openai']) | |
| except Exception: | |
| res = None | |
| if res and res.get('result'): | |
| return res['result'] | |
| return { | |
| 'Brand': 'Inferred from Content', | |
| 'Category': 'Services', | |
| 'Product': 'Digital Solutions', | |
| 'Audience': 'Business / Consumer', | |
| 'Location': 'Global' | |
| } | |
| def calculate_quality_score(analytics: Dict) -> Dict: | |
| """Calculate content quality score using 2026 GEO/AI-SEO standards.""" | |
| score = 0 | |
| feedback = [] | |
| summary = analytics.get('summary', {}) | |
| top_kws = analytics.get('top_keywords', []) | |
| # 1. Keyword Intent Quality (25 pts) — are keywords actually searchable/intentful? | |
| primary_count = summary.get('primary_keywords', 0) | |
| weak_kws = [k for k in top_kws if len(k.get('kw', '')) <= 3 or k.get('count', 0) == 1] | |
| weak_ratio = len(weak_kws) / max(len(top_kws), 1) | |
| if weak_ratio < 0.2 and primary_count >= 8: | |
| score += 25 | |
| feedback.append('✅ Strong keyword intent quality') | |
| elif weak_ratio < 0.4 and primary_count >= 4: | |
| score += 15 | |
| feedback.append('⚠️ Keyword quality is moderate — many weak/non-searchable terms') | |
| else: | |
| score += 5 | |
| feedback.append('❌ Poor keyword quality — most terms are too generic or non-searchable') | |
| # 2. Semantic Coverage / Entity Depth (25 pts) | |
| total_kws = summary.get('total_keywords', 0) | |
| clusters = len(analytics.get('clusters', {})) | |
| if total_kws >= 40 and clusters >= 4: | |
| score += 25 | |
| feedback.append('✅ Excellent semantic coverage and entity depth') | |
| elif total_kws >= 20 and clusters >= 2: | |
| score += 15 | |
| feedback.append('✅ Good semantic coverage') | |
| else: | |
| score += 5 | |
| feedback.append('⚠️ Thin semantic coverage — add topic clusters and entities') | |
| # 3. Search Volume Presence (25 pts) — do keywords have real search demand? | |
| kws_with_volume = [k for k in top_kws if k.get('volume') and k.get('volume', 0) > 0] | |
| vol_ratio = len(kws_with_volume) / max(len(top_kws), 1) | |
| if vol_ratio >= 0.5: | |
| score += 25 | |
| feedback.append('✅ Strong search volume data — keywords have real demand') | |
| elif vol_ratio >= 0.2: | |
| score += 12 | |
| feedback.append('⚠️ Partial volume data — connect DataForSEO for full picture') | |
| else: | |
| score += 0 | |
| feedback.append('❌ No search volume data — analysis is blind without it') | |
| # 4. Content Intent Alignment (25 pts) — not just density | |
| long_tail_count = summary.get('long_tail_keywords', 0) | |
| if long_tail_count >= 15: | |
| score += 25 | |
| feedback.append('✅ Strong long-tail intent coverage') | |
| elif long_tail_count >= 7: | |
| score += 15 | |
| feedback.append('⚠️ Add more long-tail intent keywords') | |
| else: | |
| score += 5 | |
| feedback.append('❌ Missing long-tail keywords — users search in full phrases') | |
| return { | |
| 'score': score, | |
| 'max_score': 100, | |
| 'percentage': round(score, 1), | |
| 'grade': get_grade(score), | |
| 'feedback': feedback | |
| } | |
| def get_grade(percentage: float) -> str: | |
| """Convert percentage to letter grade.""" | |
| if percentage >= 90: | |
| return 'A' | |
| elif percentage >= 80: | |
| return 'B' | |
| elif percentage >= 70: | |
| return 'C' | |
| elif percentage >= 60: | |
| return 'D' | |
| else: | |
| return 'F' | |
| def generate_recommendations(analytics: Dict, competitors: List[Dict]) -> List[Dict]: | |
| """Generate actionable 2026 GEO/AI-SEO recommendations.""" | |
| recs = [] | |
| summary = analytics.get('summary', {}) | |
| top_kws = analytics.get('top_keywords', []) | |
| # 1. Keyword Quality | |
| weak_kws = [k['kw'] for k in top_kws if len(k.get('kw','')) <= 3 or k.get('count',0) == 1] | |
| if len(weak_kws) > len(top_kws) * 0.3: | |
| recs.append({'type':'keyword_quality','priority':'HIGH', | |
| 'title':'Keyword Quality Problem', | |
| 'description':f'{len(weak_kws)} of your top keywords are too short or appear only once — they have no real search demand.', | |
| 'action':'Replace weak keywords with intent-driven phrases (3+ words) that users actually search for'}) | |
| # 2. Search Volume | |
| no_vol = [k for k in top_kws if not k.get('volume')] | |
| if len(no_vol) > len(top_kws) * 0.7: | |
| recs.append({'type':'volume_data','priority':'HIGH', | |
| 'title':'Missing Search Volume Data', | |
| 'description':'Over 70% of keywords have no volume data — your analysis is blind. You cannot prioritize without knowing demand.', | |
| 'action':'Add DataForSEO credentials in .env to get real volume, CPC, and competition data'}) | |
| # 3. Intent Coverage | |
| intent = analytics.get('intent_distribution', {}) | |
| nav_pct = intent.get('Navigational', 0) | |
| if nav_pct > 50: | |
| recs.append({'type':'intent','priority':'HIGH', | |
| 'title':'Wrong Intent Classification', | |
| 'description':f'{nav_pct}% Navigational intent detected — this is likely wrong. Service/agency pages should be Commercial + Informational.', | |
| 'action':'Add Commercial keywords (best, agency, service, solution) and Informational content (guides, how-to, case studies)'}) | |
| # 4. Competitor Gap | |
| if not competitors: | |
| recs.append({'type':'competitors','priority':'HIGH', | |
| 'title':'No Competitor Intelligence', | |
| 'description':'Zero competitors detected. Every niche has competitors — the crawler found no external links to analyze.', | |
| 'action':'Add competitor domains manually or crawl deeper pages that reference industry players'}) | |
| # 5. GEO / Local | |
| local_kws = [k for k in top_kws if any(loc in k.get('kw','').lower() for loc in | |
| ['saudi','ksa','riyadh','jeddah','egypt','cairo','uae','dubai','مصر','السعودية','الرياض','القاهرة','الإمارات'])] | |
| if not local_kws: | |
| recs.append({'type':'geo_local','priority':'MEDIUM', | |
| 'title':'No Local/GEO Keywords Found', | |
| 'description':'No location-specific keywords detected. AI search engines heavily weight local context.', | |
| 'action':'Add city/country keywords: "[service] in Riyadh", "best [service] Saudi Arabia", etc.'}) | |
| # 6. Entity Coverage | |
| clusters = len(analytics.get('clusters', {})) | |
| if clusters < 3: | |
| recs.append({'type':'entities','priority':'MEDIUM', | |
| 'title':'Weak Entity & Topic Coverage', | |
| 'description':f'Only {clusters} topic clusters — AI models need rich entity graphs to cite your content.', | |
| 'action':'Add Named Entity content: Organization, People, Products, Locations with Schema.org markup'}) | |
| # 7. Long-tail / AI Query Coverage | |
| lt = summary.get('long_tail_keywords', 0) | |
| if lt < 10: | |
| recs.append({'type':'longtail','priority':'MEDIUM', | |
| 'title':'Missing AI Query Coverage', | |
| 'description':f'Only {lt} long-tail keywords. ChatGPT and Perplexity answer full questions — not single words.', | |
| 'action':'Create FAQ sections and "how to" content targeting full user questions (5+ word phrases)'}) | |
| return recs | |
| def format_professional_output(report: Dict) -> str: | |
| """Format report as professional text output.""" | |
| lines = [] | |
| lines.append("=" * 80) | |
| lines.append("🔍 SEARCH INTELLIGENCE ANALYSIS") | |
| lines.append("=" * 80) | |
| lines.append(f"\n✅ {report['message']}") | |
| lines.append(f"\n📄 Pages Analyzed: {report['pages_analyzed']}") | |
| lines.append(f"📝 Total Words: {report['total_words']}") | |
| # Quality Score | |
| quality = report['metrics']['quality_score'] | |
| lines.append(f"\n🎯 QUALITY SCORE: {quality['score']}/{quality['max_score']} ({quality['percentage']}%) - Grade: {quality['grade']}") | |
| lines.append("-" * 80) | |
| for feedback in quality['feedback']: | |
| lines.append(f" {feedback}") | |
| # Keyword Results | |
| lines.append(f"\n\n📊 KEYWORD RESULTS ({report['keyword_results']['total_found']} keywords found)") | |
| lines.append("=" * 80) | |
| # Primary Keywords | |
| primary = report['keyword_results']['classification']['primary'] | |
| lines.append(f"\n1️⃣ PRIMARY KEYWORDS ({primary['count']} keywords)") | |
| lines.append("-" * 80) | |
| for kw in primary['keywords'][:10]: | |
| vol = f"Vol: {kw.get('volume', 'N/A')}" if kw.get('volume') else "" | |
| cpc = f"CPC: ${kw.get('cpc', 0):.2f}" if kw.get('cpc') else "" | |
| comp = f"Comp: {kw.get('competition', 'N/A')}" if kw.get('competition') else "" | |
| density = f"Density: {kw.get('density', 0):.2f}%" if kw.get('density') else "" | |
| meta = " | ".join(filter(None, [vol, cpc, comp, density])) | |
| lines.append(f" • {kw['kw']} ({kw['count']}) {meta}") | |
| # Secondary Keywords | |
| secondary = report['keyword_results']['classification']['secondary'] | |
| lines.append(f"\n2️⃣ SECONDARY KEYWORDS ({secondary['count']} keywords)") | |
| lines.append("-" * 80) | |
| for kw in secondary['keywords'][:5]: | |
| lines.append(f" • {kw['kw']} ({kw['count']})") | |
| # Topic Clusters | |
| lines.append(f"\n\n🎯 TOPIC CLUSTERS") | |
| lines.append("=" * 80) | |
| for topic, data in report['topic_clusters'].items(): | |
| lines.append(f"\n{topic} ({data['count']} keywords)") | |
| for kw in data['keywords']: | |
| lines.append(f" • {kw['kw']} ({kw['count']})") | |
| # Competitors | |
| lines.append(f"\n\n🏆 COMPETITORS") | |
| lines.append("=" * 80) | |
| comp_summary = report['competitors']['summary'] | |
| if report['competitors']['found'] > 0: | |
| lines.append(f"Found: {report['competitors']['found']} competitors") | |
| lines.append(f"Top Competitor: {comp_summary['top_competitor']} ({comp_summary['top_mentions']} mentions)") | |
| lines.append("\nTop Competitors:") | |
| for comp in report['competitors']['list'][:5]: | |
| lines.append(f" • {comp['domain']} ({comp['mentions']} mentions)") | |
| else: | |
| lines.append(" No external competitors found.") | |
| lines.append("\nThis could mean:") | |
| lines.append(" • Page has no external links") | |
| lines.append(" • All links are to social media/CDNs") | |
| lines.append(" • Consider adding authoritative references") | |
| # Recommendations | |
| lines.append(f"\n\n💡 RECOMMENDATIONS") | |
| lines.append("=" * 80) | |
| for i, rec in enumerate(report['recommendations'], 1): | |
| priority_icon = {'high': '🔴', 'medium': '🟡', 'low': '🟢'}.get(rec['priority'], '⚪') | |
| lines.append(f"\n{i}. {priority_icon} {rec['title']} [{rec['priority'].upper()}]") | |
| lines.append(f" {rec['description']}") | |
| lines.append(f" ➡️ Action: {rec['action']}") | |
| lines.append("\n" + "=" * 80) | |
| return "\n".join(lines) | |
| def _analyze_geo_local(analytics: Dict, pages: List[Dict], source_url: str) -> Dict: | |
| """Detect local/GEO signals and missing local keywords.""" | |
| LOCAL_REGIONS = { | |
| 'Saudi Arabia': ['سعودية','السعودية','رياض','جدة','مكة','دمام','saudi','riyadh','jeddah','ksa','mecca','dammam'], | |
| 'Egypt': ['مصر','قاهرة','اسكندرية','egypt','cairo','alexandria'], | |
| 'UAE': ['إمارات','دبي','أبوظبي','uae','dubai','abudhabi'], | |
| 'Jordan': ['الأردن','عمان','jordan','amman'], | |
| 'Kuwait': ['كويت','kuwait'], | |
| } | |
| all_text = ' '.join(p.get('text','') + ' ' + p.get('title','') for p in pages).lower() | |
| top_kws = [k.get('kw','').lower() for k in analytics.get('top_keywords', [])] | |
| detected_regions = [] | |
| for region, signals in LOCAL_REGIONS.items(): | |
| if any(s in all_text or any(s in kw for kw in top_kws) for s in signals): | |
| detected_regions.append(region) | |
| # Suggest missing local keywords based on detected region | |
| suggestions = [] | |
| primary_kw = top_kws[0] if top_kws else 'your service' | |
| for region in detected_regions: | |
| cities = LOCAL_REGIONS[region][:2] | |
| for city in cities: | |
| suggestions.append(f'{primary_kw} in {city}') | |
| suggestions.append(f'best {primary_kw} {city}') | |
| if not detected_regions: | |
| suggestions = [ | |
| f'{primary_kw} in Saudi Arabia', | |
| f'best {primary_kw} Riyadh', | |
| f'{primary_kw} agency Egypt', | |
| f'{primary_kw} UAE', | |
| ] | |
| has_maps = 'maps.google' in all_text or 'google.com/maps' in all_text | |
| has_schema_local = 'localBusiness' in all_text or 'LocalBusiness' in all_text | |
| return { | |
| 'detected_regions': detected_regions, | |
| 'has_local_keywords': len(detected_regions) > 0, | |
| 'has_google_maps': has_maps, | |
| 'has_local_schema': has_schema_local, | |
| 'missing_local_keywords': suggestions[:8], | |
| 'geo_score': min(100, len(detected_regions) * 25 + (20 if has_maps else 0) + (20 if has_schema_local else 0)), | |
| 'verdict': 'Strong local presence' if detected_regions else '⚠️ No local/GEO signals detected — missing major ranking opportunity' | |
| } | |
| def _score_keyword_quality(analytics: Dict) -> Dict: | |
| """Score each keyword by quality: searchability, length, intent signal.""" | |
| top_kws = analytics.get('top_keywords', []) | |
| scored = [] | |
| for kw in top_kws: | |
| word = kw.get('kw', '') | |
| words = word.split() | |
| vol = kw.get('volume') or 0 | |
| count = kw.get('count', 1) | |
| # Quality signals | |
| has_volume = vol > 0 | |
| is_phrase = len(words) >= 2 | |
| is_long_tail = len(words) >= 3 | |
| has_intent = any(w in word.lower() for w in [ | |
| 'best','how','guide','service','agency','price','buy','review', | |
| 'أفضل','كيف','دليل','خدمة','سعر','شركة' | |
| ]) | |
| not_generic = len(word) > 4 and count > 1 | |
| q = 0 | |
| if has_volume: q += 35 | |
| if is_phrase: q += 20 | |
| if is_long_tail: q += 15 | |
| if has_intent: q += 20 | |
| if not_generic: q += 10 | |
| scored.append({**kw, 'quality_score': min(100, q), | |
| 'quality_label': 'Strong' if q >= 70 else ('Medium' if q >= 40 else 'Weak')}) | |
| strong = [k for k in scored if k['quality_label'] == 'Strong'] | |
| weak = [k for k in scored if k['quality_label'] == 'Weak'] | |
| return { | |
| 'scored_keywords': scored[:20], | |
| 'strong_count': len(strong), | |
| 'weak_count': len(weak), | |
| 'verdict': f'{len(strong)} strong keywords, {len(weak)} weak/non-searchable keywords found' | |
| } | |
| def run_complete_analysis(pages: List[Dict], source_url: str, enrich_data: bool = True, api_keys: dict = None) -> Dict: | |
| """ | |
| Run complete search intelligence analysis. | |
| Returns professional analytics report with: | |
| - Clean keyword extraction | |
| - Keyword classification (primary/secondary/long-tail) | |
| - Topic clustering | |
| - Keyword density | |
| - Coverage score | |
| - Competitor detection | |
| - DataForSEO enrichment (volume, CPC, competition) | |
| """ | |
| # Build audit object | |
| audit_obj = {'pages': pages} | |
| # Extract keywords with analytics | |
| try: | |
| analytics = extract_keywords_from_audit( | |
| audit_obj, | |
| top_n=50, | |
| enrich=enrich_data, | |
| analytics=True | |
| ) | |
| # Ensure analytics is a dict | |
| if not isinstance(analytics, dict): | |
| # Fallback: create basic analytics structure | |
| analytics = { | |
| 'summary': { | |
| 'total_keywords': 0, | |
| 'avg_frequency': 0, | |
| 'primary_keywords': 0, | |
| 'secondary_keywords': 0, | |
| 'long_tail_keywords': 0 | |
| }, | |
| 'top_keywords': [], | |
| 'classification': { | |
| 'primary': [], | |
| 'secondary': [], | |
| 'long_tail': [] | |
| }, | |
| 'clusters': {}, | |
| 'coverage': None | |
| } | |
| except Exception as e: | |
| # Fallback analytics on error | |
| analytics = { | |
| 'summary': { | |
| 'total_keywords': 0, | |
| 'avg_frequency': 0, | |
| 'primary_keywords': 0, | |
| 'secondary_keywords': 0, | |
| 'long_tail_keywords': 0 | |
| }, | |
| 'top_keywords': [], | |
| 'classification': { | |
| 'primary': [], | |
| 'secondary': [], | |
| 'long_tail': [] | |
| }, | |
| 'clusters': {}, | |
| 'coverage': None | |
| } | |
| # Detect competitors | |
| try: | |
| competitors = detect_competitors(pages, source_url, min_mentions=1) | |
| competitor_summary = get_competitor_summary(competitors) | |
| except Exception: | |
| competitors = [] | |
| competitor_summary = {'total': 0, 'avg_mentions': 0, 'top_competitor': None, 'top_mentions': 0} | |
| # Calculate total words | |
| total_words = 0 | |
| try: | |
| total_words = sum(len(str(p.get('text', '')).split()) for p in pages) | |
| except Exception: | |
| total_words = 0 | |
| # Ensure analytics is the advanced dict format | |
| if isinstance(analytics, list): | |
| analytics = {'summary': {'total_keywords': len(analytics)}, 'top_keywords': analytics, 'classification': {}} | |
| analytics_dict = analytics if isinstance(analytics, dict) else {} | |
| # Build professional report | |
| report = { | |
| 'status': 'completed', | |
| 'message': 'Your GEO tool finished analyzing the page and extracted keywords and their frequency.', | |
| 'pages_analyzed': len(pages), | |
| 'total_words': total_words, | |
| # Keyword Results Section | |
| 'keyword_results': { | |
| 'total_found': int((analytics_dict.get('summary') or {}).get('total_keywords', 0)), | |
| 'top_keywords': analytics_dict.get('top_keywords', [])[:30], | |
| 'classification': { | |
| 'primary': { | |
| 'count': len((analytics_dict.get('classification') or {}).get('primary', [])), | |
| 'keywords': (analytics_dict.get('classification') or {}).get('primary', [])[:10] | |
| }, | |
| 'secondary': { | |
| 'count': len((analytics_dict.get('classification') or {}).get('secondary', [])), | |
| 'keywords': (analytics_dict.get('classification') or {}).get('secondary', [])[:10] | |
| }, | |
| 'long_tail': { | |
| 'count': len((analytics_dict.get('classification') or {}).get('long_tail', [])), | |
| 'keywords': (analytics_dict.get('classification') or {}).get('long_tail', [])[:10] | |
| } | |
| } | |
| }, | |
| # Topic Clusters (AI-Driven) | |
| 'topic_clusters': cluster_topics_ai(analytics_dict, api_keys), | |
| # Metrics | |
| 'metrics': { | |
| 'coverage': analytics_dict.get('coverage', 0), | |
| 'quality_score': calculate_quality_score(analytics_dict) if analytics_dict else {'score': 0, 'label': 'N/A'} | |
| }, | |
| # Market Intelligence (Competitors) | |
| 'competitors': get_market_intelligence_ai(competitors, competitor_summary, analytics_dict, api_keys), | |
| # Phase 2: Professional SEO (AI-Driven) | |
| 'intent_analysis': analyze_search_intent_ai(analytics_dict, pages, api_keys), | |
| 'content_gaps': detect_content_gaps_ai(analytics_dict, pages, api_keys), | |
| 'serp_intelligence': simulate_serp_intelligence_ai(analytics_dict, source_url, api_keys), | |
| 'entities': extract_semantic_entities_ai(pages, api_keys), | |
| 'geo_local': _analyze_geo_local(analytics_dict, pages, source_url), | |
| 'keyword_quality': _score_keyword_quality(analytics_dict), | |
| # Recommendations | |
| 'recommendations': generate_recommendations_ai(analytics_dict, competitors, api_keys) | |
| } | |
| # Update metrics with AI Quality Score | |
| report['metrics']['quality_score'] = calculate_quality_score_ai(analytics_dict, pages, api_keys) | |
| # Calculate Keyword Opportunity Score for top keywords with smart difficulty | |
| q_score = 70 | |
| metrics = report.get('metrics') | |
| if isinstance(metrics, dict): | |
| qs = metrics.get('quality_score') | |
| if isinstance(qs, dict): | |
| q_score_val = qs.get('score', 70) | |
| try: | |
| q_score = int(q_score_val) | |
| except (ValueError, TypeError): | |
| q_score = 70 | |
| for kw in report.get('keyword_results', {}).get('top_keywords', []): | |
| if isinstance(kw, dict): | |
| kw['opportunity_score'] = calculate_opportunity_score_smart(kw, q_score) | |
| return report | |