feat(audit): Add AI-powered audit analysis and action generation
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Add Gemini AI integration to SEO, GBP, and Social Media audits that
generates contextual analysis summaries and prioritized action items
with ready-to-use content (Schema.org, meta descriptions, social posts,
GBP descriptions, review responses, content calendars).

New files:
- audit_ai_service.py: Central AI service with caching (7-day TTL)
- blueprints/api/routes_audit_actions.py: 4 API endpoints
- database/migrations/056_audit_actions.sql: 3 new tables
- templates/partials/audit_ai_actions.html: Reusable UI component

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Maciej Pienczyn 2026-02-07 12:41:26 +01:00
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"""
Audit AI Service
=================
Centralny serwis AI do analizy wyników audytów i generowania
priorytetowanych akcji z treścią gotową do wdrożenia.
Obsługiwane typy audytów:
- SEO (PageSpeed, on-page, technical, local SEO)
- GBP (Google Business Profile completeness)
- Social Media (presence across platforms)
Używa Gemini API (via gemini_service.py) do generowania analiz.
Author: Norda Biznes Development Team
Created: 2026-02-07
"""
import hashlib
import json
import logging
from datetime import datetime, timedelta
from database import (
SessionLocal, Company, CompanyWebsiteAnalysis, CompanySocialMedia,
CompanyCitation, AuditAction, AuditAICache
)
logger = logging.getLogger(__name__)
# Cache expiry: 7 days
CACHE_EXPIRY_DAYS = 7
def _get_gemini_service():
"""Get the initialized Gemini service instance."""
from gemini_service import get_gemini_service
service = get_gemini_service()
if not service:
raise RuntimeError("Gemini service not initialized")
return service
def _hash_data(data: dict) -> str:
"""Generate SHA256 hash of audit data for cache invalidation."""
serialized = json.dumps(data, sort_keys=True, default=str)
return hashlib.sha256(serialized.encode()).hexdigest()
# ============================================================
# SEO AUDIT DATA COLLECTION
# ============================================================
def _collect_seo_data(db, company) -> dict:
"""Collect SEO audit data for AI analysis."""
analysis = db.query(CompanyWebsiteAnalysis).filter(
CompanyWebsiteAnalysis.company_id == company.id
).order_by(CompanyWebsiteAnalysis.seo_audited_at.desc()).first()
if not analysis or not analysis.seo_audited_at:
return {}
citations = db.query(CompanyCitation).filter(
CompanyCitation.company_id == company.id
).all()
return {
'company_name': company.name,
'company_category': company.category,
'website': company.website,
'city': company.address_city,
# PageSpeed scores
'seo_score': analysis.pagespeed_seo_score,
'performance_score': analysis.pagespeed_performance_score,
'accessibility_score': analysis.pagespeed_accessibility_score,
'best_practices_score': analysis.pagespeed_best_practices_score,
# On-page
'meta_title': analysis.meta_title,
'meta_description': analysis.meta_description,
'h1_count': analysis.h1_count,
'h1_text': analysis.h1_text,
'h2_count': analysis.h2_count,
'h3_count': analysis.h3_count,
'total_images': analysis.total_images,
'images_without_alt': analysis.images_without_alt,
# Technical
'has_ssl': analysis.has_ssl,
'has_sitemap': analysis.has_sitemap,
'has_robots_txt': analysis.has_robots_txt,
'has_canonical': analysis.has_canonical,
'is_indexable': analysis.is_indexable,
'is_mobile_friendly': getattr(analysis, 'is_mobile_friendly', None),
'load_time_ms': analysis.load_time_ms,
# Structured data
'has_structured_data': analysis.has_structured_data,
'structured_data_types': analysis.structured_data_types,
'has_local_business_schema': analysis.has_local_business_schema,
# Social/analytics
'has_og_tags': analysis.has_og_tags,
'has_twitter_cards': analysis.has_twitter_cards,
'has_google_analytics': analysis.has_google_analytics,
'has_google_tag_manager': analysis.has_google_tag_manager,
# Local SEO
'local_seo_score': analysis.local_seo_score,
'has_google_maps_embed': analysis.has_google_maps_embed,
'has_local_keywords': analysis.has_local_keywords,
'nap_on_website': analysis.nap_on_website,
# Core Web Vitals
'lcp_ms': analysis.largest_contentful_paint_ms,
'fid_ms': analysis.first_input_delay_ms,
'cls': float(analysis.cumulative_layout_shift) if analysis.cumulative_layout_shift else None,
# Content
'content_freshness_score': analysis.content_freshness_score,
'word_count_homepage': analysis.word_count_homepage,
# Links
'internal_links_count': analysis.internal_links_count,
'external_links_count': analysis.external_links_count,
'broken_links_count': analysis.broken_links_count,
# Citations
'citations_count': len(citations),
'citations_found': len([c for c in citations if c.status == 'found']),
}
def _collect_gbp_data(db, company) -> dict:
"""Collect GBP audit data for AI analysis."""
try:
from gbp_audit_service import get_company_audit
audit = get_company_audit(db, company.id)
except ImportError:
audit = None
if not audit:
return {}
return {
'company_name': company.name,
'company_category': company.category,
'city': company.address_city,
'completeness_score': audit.completeness_score,
# Field presence
'has_name': audit.has_name,
'has_address': audit.has_address,
'has_phone': audit.has_phone,
'has_website': audit.has_website,
'has_hours': audit.has_hours,
'has_categories': audit.has_categories,
'has_photos': audit.has_photos,
'has_description': audit.has_description,
'has_services': audit.has_services,
'has_reviews': audit.has_reviews,
# Reviews
'review_count': audit.review_count,
'average_rating': float(audit.average_rating) if audit.average_rating else None,
'reviews_with_response': audit.reviews_with_response,
'reviews_without_response': audit.reviews_without_response,
'review_response_rate': float(audit.review_response_rate) if audit.review_response_rate else None,
# Activity
'has_posts': audit.has_posts,
'posts_count_30d': audit.posts_count_30d,
'has_products': audit.has_products,
'has_qa': audit.has_qa,
# Photos
'photo_count': audit.photo_count,
'logo_present': audit.logo_present,
'cover_photo_present': audit.cover_photo_present,
# NAP
'nap_consistent': audit.nap_consistent,
'nap_issues': audit.nap_issues,
}
def _collect_social_data(db, company) -> dict:
"""Collect social media audit data for AI analysis."""
profiles = db.query(CompanySocialMedia).filter(
CompanySocialMedia.company_id == company.id
).all()
all_platforms = ['facebook', 'instagram', 'linkedin', 'youtube', 'twitter', 'tiktok']
profiles_dict = {}
for p in profiles:
profiles_dict[p.platform] = {
'url': p.url,
'is_valid': p.is_valid,
'followers_count': p.followers_count,
'has_bio': p.has_bio,
'has_profile_photo': p.has_profile_photo,
'has_cover_photo': p.has_cover_photo,
'posts_count_30d': p.posts_count_30d,
'last_post_date': str(p.last_post_date) if p.last_post_date else None,
'posting_frequency_score': p.posting_frequency_score,
'engagement_rate': float(p.engagement_rate) if p.engagement_rate else None,
'profile_completeness_score': p.profile_completeness_score,
}
present = [p for p in all_platforms if p in profiles_dict]
missing = [p for p in all_platforms if p not in profiles_dict]
return {
'company_name': company.name,
'company_category': company.category,
'city': company.address_city,
'platforms_present': present,
'platforms_missing': missing,
'profiles': profiles_dict,
'total_platforms': len(all_platforms),
'platforms_found': len(present),
'score': int((len(present) / len(all_platforms)) * 100) if all_platforms else 0,
}
# ============================================================
# GEMINI PROMPTS
# ============================================================
def _build_seo_prompt(data: dict) -> str:
"""Build Gemini prompt for SEO audit analysis."""
return f"""Jesteś ekspertem SEO analizującym stronę internetową lokalnej firmy w Polsce.
DANE FIRMY:
- Nazwa: {data.get('company_name', 'N/A')}
- Branża: {data.get('company_category', 'N/A')}
- Miasto: {data.get('city', 'N/A')}
- Strona: {data.get('website', 'N/A')}
WYNIKI AUDYTU SEO:
- Wynik SEO (PageSpeed): {data.get('seo_score', 'brak')}/100
- Wydajność: {data.get('performance_score', 'brak')}/100
- Dostępność: {data.get('accessibility_score', 'brak')}/100
- Best Practices: {data.get('best_practices_score', 'brak')}/100
Core Web Vitals:
- LCP: {data.get('lcp_ms', 'brak')} ms
- FID: {data.get('fid_ms', 'brak')} ms
- CLS: {data.get('cls', 'brak')}
On-Page SEO:
- Meta title: {data.get('meta_title', 'brak')}
- Meta description: {'tak' if data.get('meta_description') else 'BRAK'}
- H1: {data.get('h1_count', 0)} (treść: {data.get('h1_text', 'brak')})
- H2: {data.get('h2_count', 0)}, H3: {data.get('h3_count', 0)}
- Obrazy: {data.get('total_images', 0)} (bez alt: {data.get('images_without_alt', 0)})
- Linki wewnętrzne: {data.get('internal_links_count', 0)}, zewnętrzne: {data.get('external_links_count', 0)}, uszkodzone: {data.get('broken_links_count', 0)}
Technical SEO:
- SSL: {'tak' if data.get('has_ssl') else 'NIE'}
- Sitemap: {'tak' if data.get('has_sitemap') else 'NIE'}
- Robots.txt: {'tak' if data.get('has_robots_txt') else 'NIE'}
- Canonical: {'tak' if data.get('has_canonical') else 'NIE'}
- Indeksowalna: {'tak' if data.get('is_indexable') else 'NIE'}
- Mobile-friendly: {'tak' if data.get('is_mobile_friendly') else 'NIE/brak danych'}
Dane strukturalne:
- Schema.org: {'tak' if data.get('has_structured_data') else 'NIE'} (typy: {data.get('structured_data_types', [])})
- LocalBusiness Schema: {'tak' if data.get('has_local_business_schema') else 'NIE'}
Social & Analytics:
- Open Graph: {'tak' if data.get('has_og_tags') else 'NIE'}
- Twitter Cards: {'tak' if data.get('has_twitter_cards') else 'NIE'}
- Google Analytics: {'tak' if data.get('has_google_analytics') else 'NIE'}
- GTM: {'tak' if data.get('has_google_tag_manager') else 'NIE'}
Local SEO (wynik: {data.get('local_seo_score', 'brak')}/100):
- Mapa Google: {'tak' if data.get('has_google_maps_embed') else 'NIE'}
- Lokalne słowa kluczowe: {'tak' if data.get('has_local_keywords') else 'NIE'}
- NAP na stronie: {'tak' if data.get('nap_on_website') else 'NIE'}
- Cytacje: {data.get('citations_found', 0)}/{data.get('citations_count', 0)} znalezionych
Treść:
- Świeżość: {data.get('content_freshness_score', 'brak')}/100
- Słów na stronie głównej: {data.get('word_count_homepage', 'brak')}
ZADANIE:
Przygotuj analizę w formacie JSON z dwoma kluczami:
1. "summary" - krótki akapit (2-4 zdania) podsumowujący stan SEO strony, co jest dobrze, a co wymaga poprawy. Pisz bezpośrednio do właściciela firmy, po polsku.
2. "actions" - lista od 3 do 8 priorytetowanych akcji do podjęcia. Każda akcja to obiekt:
{{
"action_type": "typ akcji z listy: generate_schema_org, generate_meta_description, suggest_heading_fix, generate_alt_texts, seo_roadmap, add_analytics, add_sitemap, fix_ssl, add_og_tags, improve_performance, add_local_keywords, add_nap, fix_broken_links",
"title": "krótki tytuł po polsku",
"description": "opis co trzeba zrobić i dlaczego, 1-2 zdania",
"priority": "critical/high/medium/low",
"impact_score": 1-10,
"effort_score": 1-10,
"platform": "website"
}}
Priorytetyzuj wg: impact_score / effort_score (wyższy stosunek = wyższy priorytet).
NIE sugeruj akcji dla rzeczy, które firma już ma poprawnie.
Odpowiedz WYŁĄCZNIE poprawnym JSON-em, bez markdown, bez komentarzy."""
def _build_gbp_prompt(data: dict) -> str:
"""Build Gemini prompt for GBP audit analysis."""
return f"""Jesteś ekspertem Google Business Profile analizującym wizytówkę lokalnej firmy w Polsce.
DANE FIRMY:
- Nazwa: {data.get('company_name', 'N/A')}
- Branża: {data.get('company_category', 'N/A')}
- Miasto: {data.get('city', 'N/A')}
WYNIKI AUDYTU GBP (kompletność: {data.get('completeness_score', 'brak')}/100):
- Nazwa: {'' if data.get('has_name') else ''}
- Adres: {'' if data.get('has_address') else ''}
- Telefon: {'' if data.get('has_phone') else ''}
- Strona WWW: {'' if data.get('has_website') else ''}
- Godziny otwarcia: {'' if data.get('has_hours') else ''}
- Kategorie: {'' if data.get('has_categories') else ''}
- Zdjęcia: {'' if data.get('has_photos') else ''} ({data.get('photo_count', 0)} zdjęć)
- Opis: {'' if data.get('has_description') else ''}
- Usługi: {'' if data.get('has_services') else ''}
- Logo: {'' if data.get('logo_present') else ''}
- Zdjęcie w tle: {'' if data.get('cover_photo_present') else ''}
Opinie:
- Liczba opinii: {data.get('review_count', 0)}
- Średnia ocena: {data.get('average_rating', 'brak')}
- Z odpowiedzią: {data.get('reviews_with_response', 0)}
- Bez odpowiedzi: {data.get('reviews_without_response', 0)}
- Wskaźnik odpowiedzi: {data.get('review_response_rate', 'brak')}%
Aktywność:
- Posty: {'' if data.get('has_posts') else ''} ({data.get('posts_count_30d', 0)} w ostatnich 30 dniach)
- Produkty: {'' if data.get('has_products') else ''}
- Pytania i odpowiedzi: {'' if data.get('has_qa') else ''}
NAP:
- Spójność NAP: {'' if data.get('nap_consistent') else ''}
- Problemy NAP: {data.get('nap_issues', 'brak')}
ZADANIE:
Przygotuj analizę w formacie JSON z dwoma kluczami:
1. "summary" - krótki akapit (2-4 zdania) podsumowujący stan wizytówki Google, co jest dobrze, a co wymaga poprawy. Pisz bezpośrednio do właściciela firmy, po polsku.
2. "actions" - lista od 3 do 8 priorytetowanych akcji. Każda akcja:
{{
"action_type": "typ z listy: generate_gbp_description, generate_gbp_post, respond_to_review, suggest_categories, gbp_improvement_plan, add_photos, add_hours, add_services, add_products",
"title": "krótki tytuł po polsku",
"description": "opis co trzeba zrobić i dlaczego",
"priority": "critical/high/medium/low",
"impact_score": 1-10,
"effort_score": 1-10,
"platform": "google"
}}
NIE sugeruj akcji dla pól, które firma już ma poprawnie uzupełnione.
Odpowiedz WYŁĄCZNIE poprawnym JSON-em, bez markdown, bez komentarzy."""
def _build_social_prompt(data: dict) -> str:
"""Build Gemini prompt for social media audit analysis."""
profiles_info = ""
for platform, info in data.get('profiles', {}).items():
profiles_info += f"\n {platform}: followers={info.get('followers_count', '?')}, "
profiles_info += f"bio={'' if info.get('has_bio') else ''}, "
profiles_info += f"photo={'' if info.get('has_profile_photo') else ''}, "
profiles_info += f"posty_30d={info.get('posts_count_30d', '?')}, "
profiles_info += f"kompletność={info.get('profile_completeness_score', '?')}%"
return f"""Jesteś ekspertem social media analizującym obecność lokalnej firmy w Polsce w mediach społecznościowych.
DANE FIRMY:
- Nazwa: {data.get('company_name', 'N/A')}
- Branża: {data.get('company_category', 'N/A')}
- Miasto: {data.get('city', 'N/A')}
OBECNOŚĆ W SOCIAL MEDIA (wynik: {data.get('score', 0)}/100):
- Platformy znalezione ({data.get('platforms_found', 0)}/{data.get('total_platforms', 6)}): {', '.join(data.get('platforms_present', []))}
- Platformy brakujące: {', '.join(data.get('platforms_missing', []))}
Szczegóły profili:{profiles_info or ' brak profili'}
ZADANIE:
Przygotuj analizę w formacie JSON z dwoma kluczami:
1. "summary" - krótki akapit (2-4 zdania) podsumowujący obecność firmy w social media. Pisz po polsku, do właściciela firmy.
2. "actions" - lista od 3 do 8 priorytetowanych akcji. Każda akcja:
{{
"action_type": "typ z listy: generate_social_post, generate_bio, content_calendar, content_strategy, create_profile, improve_profile, increase_engagement",
"title": "krótki tytuł po polsku",
"description": "opis co trzeba zrobić i dlaczego",
"priority": "critical/high/medium/low",
"impact_score": 1-10,
"effort_score": 1-10,
"platform": "facebook/instagram/linkedin/youtube/twitter/tiktok/all"
}}
Dla firm lokalnych priorytetyzuj: Facebook > Instagram > LinkedIn > reszta.
NIE sugeruj tworzenia profili na platformach nieistotnych dla branży.
Odpowiedz WYŁĄCZNIE poprawnym JSON-em, bez markdown, bez komentarzy."""
# ============================================================
# CONTENT GENERATION PROMPTS
# ============================================================
CONTENT_PROMPTS = {
'generate_schema_org': """Wygeneruj kompletny JSON-LD Schema.org LocalBusiness dla firmy:
- Nazwa: {company_name}
- Branża: {category}
- Adres: {address}
- Miasto: {city}
- Telefon: {phone}
- Strona: {website}
- Email: {email}
Wygeneruj WYŁĄCZNIE poprawny tag <script type="application/ld+json"> z JSON-LD. Bez komentarzy.""",
'generate_meta_description': """Napisz meta description (150-160 znaków) dla strony firmy:
- Nazwa: {company_name}
- Branża: {category}
- Miasto: {city}
- Usługi: {services}
Opis powinien zawierać lokalne słowa kluczowe, CTA i zachęcać do kliknięcia.
Odpowiedz WYŁĄCZNIE tekstem meta description, bez cudzysłowów, bez komentarzy.""",
'suggest_heading_fix': """Zaproponuj poprawioną strukturę nagłówków (H1, H2, H3) dla strony firmy:
- Nazwa: {company_name}
- Branża: {category}
- Obecny H1: {h1_text}
- Liczba H1: {h1_count}, H2: {h2_count}, H3: {h3_count}
Zaproponuj strukturę z jednym H1 i logiczną hierarchią H2/H3. Po polsku.
Format odpowiedzi - lista nagłówków z poziomami, np.:
H1: ...
H2: ...
H3: ...
H2: ...""",
'generate_alt_texts': """Zaproponuj teksty alternatywne (alt) dla obrazów na stronie firmy:
- Nazwa: {company_name}
- Branża: {category}
- Liczba obrazów bez alt: {images_without_alt}
Napisz {images_without_alt} propozycji tekstów alt (max 125 znaków każdy).
Uwzględnij lokalne słowa kluczowe i opis kontekstowy. Po polsku.
Format: po jednym alt tekście na linię.""",
'generate_gbp_description': """Napisz opis firmy na Google Business Profile (max 750 znaków):
- Nazwa: {company_name}
- Branża: {category}
- Miasto: {city}
- Usługi: {services}
Opis powinien:
- Zawierać lokalne słowa kluczowe (miasto, region)
- Wymieniać główne usługi/produkty
- Zawierać CTA (zachętę do kontaktu)
- Być profesjonalny ale przyjazny
Odpowiedz WYŁĄCZNIE tekstem opisu, po polsku.""",
'generate_gbp_post': """Napisz post na Google Business Profile dla firmy:
- Nazwa: {company_name}
- Branża: {category}
- Miasto: {city}
Post powinien:
- Mieć 150-300 słów
- Zawierać CTA (np. "Zadzwoń", "Odwiedź stronę")
- Być angażujący i profesjonalny
- Dotyczyć aktualnej oferty/promocji/wydarzenia
Odpowiedz WYŁĄCZNIE tekstem postu, po polsku.""",
'respond_to_review': """Napisz profesjonalną odpowiedź na opinię Google:
- Firma: {company_name}
- Ocena: {review_rating}/5
- Treść opinii: {review_text}
Odpowiedź powinna:
- Podziękować za opinię
- Odnieść się do konkretnych punktów
- {'Przeprosić i zaproponować rozwiązanie' if int(str({review_rating}).replace('None','3')) <= 3 else 'Zachęcić do ponownych odwiedzin'}
- Być profesjonalna i osobista
- Max 200 słów
Odpowiedz WYŁĄCZNIE tekstem odpowiedzi, po polsku.""",
'generate_social_post': """Napisz post na {platform} dla firmy:
- Nazwa: {company_name}
- Branża: {category}
- Miasto: {city}
Wymagania dla {platform}:
- Facebook: 100-250 słów, może zawierać emoji, CTA
- Instagram: 100-150 słów, 5-10 hashtagów, emoji
- LinkedIn: 150-300 słów, profesjonalny ton, 3-5 hashtagów
- Twitter: max 280 znaków, 2-3 hashtagi
Odpowiedz WYŁĄCZNIE tekstem postu z hashtagami, po polsku.""",
'generate_bio': """Napisz bio/opis profilu na {platform} dla firmy:
- Nazwa: {company_name}
- Branża: {category}
- Miasto: {city}
- Strona: {website}
Limity znaków:
- Facebook: 255 znaków
- Instagram: 150 znaków
- LinkedIn: 2000 znaków (opis firmy)
- Twitter: 160 znaków
Odpowiedz WYŁĄCZNIE tekstem bio, po polsku.""",
'content_calendar': """Przygotuj kalendarz treści na tydzień (pon-pt) dla firmy w social media:
- Nazwa: {company_name}
- Branża: {category}
- Miasto: {city}
- Platformy: {platforms}
Dla każdego dnia podaj:
- Dzień tygodnia
- Platforma
- Temat postu
- Krótki zarys treści (1-2 zdania)
- Sugerowany typ: tekst/zdjęcie/wideo/karuzela
Format JSON array:
[{{"day": "Poniedziałek", "platform": "...", "topic": "...", "outline": "...", "content_type": "..."}}]
Odpowiedz WYŁĄCZNIE JSON-em, po polsku.""",
'content_strategy': """Przygotuj krótką strategię obecności w social media dla firmy:
- Nazwa: {company_name}
- Branża: {category}
- Miasto: {city}
- Obecne platformy: {platforms_present}
- Brakujące platformy: {platforms_missing}
Strategia powinna zawierać:
1. Rekomendowane platformy (z uzasadnieniem)
2. Częstotliwość publikacji per platforma
3. Typy treści per platforma
4. Ton komunikacji
5. Cele na 3 miesiące
Max 500 słów, po polsku. Pisz bezpośrednio do właściciela firmy.""",
}
# ============================================================
# MAIN SERVICE FUNCTIONS
# ============================================================
def generate_analysis(company_id: int, audit_type: str, user_id: int = None, force: bool = False) -> dict:
"""
Generate AI analysis for an audit.
Returns cached version if available and data hasn't changed.
Args:
company_id: Company ID
audit_type: 'seo', 'gbp', or 'social'
user_id: Current user ID for cost tracking
force: Force regeneration even if cache is valid
Returns:
dict with 'summary' and 'actions' keys
"""
db = SessionLocal()
try:
company = db.query(Company).filter_by(id=company_id, status='active').first()
if not company:
return {'error': 'Firma nie znaleziona'}
# Collect audit data
collectors = {
'seo': _collect_seo_data,
'gbp': _collect_gbp_data,
'social': _collect_social_data,
}
collector = collectors.get(audit_type)
if not collector:
return {'error': f'Nieznany typ audytu: {audit_type}'}
data = collector(db, company)
if not data:
return {'error': f'Brak danych audytu {audit_type} dla tej firmy'}
data_hash = _hash_data(data)
# Check cache
if not force:
cache = db.query(AuditAICache).filter_by(
company_id=company_id,
audit_type=audit_type
).first()
if cache and cache.audit_data_hash == data_hash and cache.expires_at and cache.expires_at > datetime.now():
logger.info(f"AI analysis cache hit for company {company_id} audit_type={audit_type}")
return {
'summary': cache.analysis_summary,
'actions': cache.actions_json or [],
'cached': True,
'generated_at': cache.generated_at.isoformat() if cache.generated_at else None,
}
# Build prompt
prompt_builders = {
'seo': _build_seo_prompt,
'gbp': _build_gbp_prompt,
'social': _build_social_prompt,
}
prompt = prompt_builders[audit_type](data)
# Call Gemini
gemini = _get_gemini_service()
response_text = gemini.generate_text(
prompt=prompt,
temperature=0.3,
feature='audit_analysis',
user_id=user_id,
company_id=company_id,
related_entity_type=f'{audit_type}_audit',
)
if not response_text:
return {'error': 'Gemini nie zwrócił odpowiedzi'}
# Parse JSON response
try:
# Clean possible markdown code blocks
cleaned = response_text.strip()
if cleaned.startswith('```'):
cleaned = cleaned.split('\n', 1)[1] if '\n' in cleaned else cleaned[3:]
if cleaned.endswith('```'):
cleaned = cleaned[:-3]
cleaned = cleaned.strip()
result = json.loads(cleaned)
except json.JSONDecodeError as e:
logger.error(f"Failed to parse Gemini response as JSON: {e}\nResponse: {response_text[:500]}")
return {'error': 'Nie udało się przetworzyć odpowiedzi AI', 'raw_response': response_text}
summary = result.get('summary', '')
actions = result.get('actions', [])
# Save to cache (upsert)
cache = db.query(AuditAICache).filter_by(
company_id=company_id,
audit_type=audit_type
).first()
if cache:
cache.analysis_summary = summary
cache.actions_json = actions
cache.audit_data_hash = data_hash
cache.generated_at = datetime.now()
cache.expires_at = datetime.now() + timedelta(days=CACHE_EXPIRY_DAYS)
else:
cache = AuditAICache(
company_id=company_id,
audit_type=audit_type,
analysis_summary=summary,
actions_json=actions,
audit_data_hash=data_hash,
generated_at=datetime.now(),
expires_at=datetime.now() + timedelta(days=CACHE_EXPIRY_DAYS),
)
db.add(cache)
# Save individual actions to audit_actions table
for action_data in actions:
action = AuditAction(
company_id=company_id,
audit_type=audit_type,
action_type=action_data.get('action_type', 'unknown'),
title=action_data.get('title', 'Akcja'),
description=action_data.get('description', ''),
priority=action_data.get('priority', 'medium'),
impact_score=action_data.get('impact_score'),
effort_score=action_data.get('effort_score'),
platform=action_data.get('platform', 'website'),
ai_model=gemini.model_name,
status='suggested',
created_by=user_id,
)
db.add(action)
db.commit()
return {
'summary': summary,
'actions': actions,
'cached': False,
'generated_at': datetime.now().isoformat(),
}
except Exception as e:
db.rollback()
logger.error(f"Error generating AI analysis: {e}", exc_info=True)
return {'error': f'Błąd generowania analizy: {str(e)}'}
finally:
db.close()
def generate_content(company_id: int, action_type: str, context: dict = None, user_id: int = None) -> dict:
"""
Generate specific content for an audit action.
Args:
company_id: Company ID
action_type: Content type (e.g. 'generate_schema_org', 'generate_gbp_post')
context: Additional context (e.g. platform, review_text)
user_id: Current user ID for cost tracking
Returns:
dict with 'content' key
"""
if action_type not in CONTENT_PROMPTS:
return {'error': f'Nieznany typ akcji: {action_type}'}
db = SessionLocal()
try:
company = db.query(Company).filter_by(id=company_id, status='active').first()
if not company:
return {'error': 'Firma nie znaleziona'}
# Build context for prompt
prompt_context = {
'company_name': company.name,
'category': company.category or 'Usługi',
'city': company.address_city or 'Polska',
'website': company.website or '',
'phone': company.phone or '',
'email': company.email or '',
'address': f"{company.address_street or ''}, {company.address_city or ''}".strip(', '),
'services': ', '.join([s.name for s in company.services[:5]]) if hasattr(company, 'services') and company.services else 'brak danych',
}
# Merge in extra context
if context:
prompt_context.update(context)
# Add audit-specific data
if action_type in ('suggest_heading_fix', 'generate_alt_texts'):
analysis = db.query(CompanyWebsiteAnalysis).filter(
CompanyWebsiteAnalysis.company_id == company.id
).order_by(CompanyWebsiteAnalysis.seo_audited_at.desc()).first()
if analysis:
prompt_context.update({
'h1_text': analysis.h1_text or 'brak',
'h1_count': analysis.h1_count or 0,
'h2_count': analysis.h2_count or 0,
'h3_count': analysis.h3_count or 0,
'images_without_alt': analysis.images_without_alt or 0,
})
if action_type in ('content_calendar', 'content_strategy'):
profiles = db.query(CompanySocialMedia).filter(
CompanySocialMedia.company_id == company.id
).all()
all_platforms = ['facebook', 'instagram', 'linkedin', 'youtube', 'twitter', 'tiktok']
present = [p.platform for p in profiles]
missing = [p for p in all_platforms if p not in present]
prompt_context.update({
'platforms': ', '.join(present) if present else 'brak',
'platforms_present': ', '.join(present) if present else 'brak',
'platforms_missing': ', '.join(missing) if missing else 'brak',
})
# Build prompt from template
prompt_template = CONTENT_PROMPTS[action_type]
try:
prompt = prompt_template.format(**prompt_context)
except KeyError as e:
prompt = prompt_template # Fall back to raw template if context is incomplete
logger.warning(f"Missing prompt context key: {e}")
# Call Gemini
gemini = _get_gemini_service()
content = gemini.generate_text(
prompt=prompt,
temperature=0.5,
feature='audit_content_generation',
user_id=user_id,
company_id=company_id,
related_entity_type=f'audit_action_{action_type}',
)
if not content:
return {'error': 'Gemini nie zwrócił odpowiedzi'}
# Update action record if it exists
action = db.query(AuditAction).filter_by(
company_id=company_id,
action_type=action_type,
status='suggested'
).order_by(AuditAction.created_at.desc()).first()
if action:
action.ai_content = content
action.ai_model = gemini.model_name
db.commit()
return {
'content': content,
'action_type': action_type,
'model': gemini.model_name,
}
except Exception as e:
db.rollback()
logger.error(f"Error generating content: {e}", exc_info=True)
return {'error': f'Błąd generowania treści: {str(e)}'}
finally:
db.close()
def get_actions_for_company(company_id: int, audit_type: str = None) -> list:
"""
Get all audit actions for a company, optionally filtered by audit type.
Returns list of action dicts sorted by priority.
"""
db = SessionLocal()
try:
query = db.query(AuditAction).filter_by(company_id=company_id)
if audit_type:
query = query.filter_by(audit_type=audit_type)
actions = query.filter(
AuditAction.status.in_(['suggested', 'approved'])
).order_by(AuditAction.created_at.desc()).all()
# Deduplicate by action_type (keep newest)
seen = set()
unique_actions = []
for a in actions:
if a.action_type not in seen:
seen.add(a.action_type)
unique_actions.append(a)
# Sort by priority
priority_order = {'critical': 0, 'high': 1, 'medium': 2, 'low': 3}
unique_actions.sort(key=lambda a: priority_order.get(a.priority, 2))
return [{
'id': a.id,
'action_type': a.action_type,
'title': a.title,
'description': a.description,
'priority': a.priority,
'impact_score': a.impact_score,
'effort_score': a.effort_score,
'ai_content': a.ai_content,
'status': a.status,
'platform': a.platform,
'created_at': a.created_at.isoformat() if a.created_at else None,
} for a in unique_actions]
finally:
db.close()
def update_action_status(action_id: int, new_status: str) -> dict:
"""Update the status of an audit action."""
valid_statuses = ['suggested', 'approved', 'implemented', 'dismissed']
if new_status not in valid_statuses:
return {'error': f'Niepoprawny status. Dozwolone: {", ".join(valid_statuses)}'}
db = SessionLocal()
try:
action = db.query(AuditAction).filter_by(id=action_id).first()
if not action:
return {'error': 'Akcja nie znaleziona'}
action.status = new_status
if new_status == 'implemented':
action.implemented_at = datetime.now()
db.commit()
return {'success': True, 'id': action.id, 'status': new_status}
except Exception as e:
db.rollback()
logger.error(f"Error updating action status: {e}")
return {'error': str(e)}
finally:
db.close()

View File

@ -17,3 +17,4 @@ from . import routes_gbp_audit # noqa: E402, F401
from . import routes_social_audit # noqa: E402, F401 from . import routes_social_audit # noqa: E402, F401
from . import routes_company # noqa: E402, F401 from . import routes_company # noqa: E402, F401
from . import routes_membership # noqa: E402, F401 from . import routes_membership # noqa: E402, F401
from . import routes_audit_actions # noqa: E402, F401

View File

@ -0,0 +1,201 @@
"""
Audit AI Actions API Routes - API blueprint
Endpoints for AI-powered audit analysis and content generation.
Uses audit_ai_service.py for Gemini integration.
"""
import logging
from flask import jsonify, request
from flask_login import current_user, login_required
from database import SessionLocal, Company
from . import bp
logger = logging.getLogger(__name__)
@bp.route('/audit/analyze', methods=['POST'])
@login_required
def api_audit_analyze():
"""
Generate AI analysis for an audit.
Request JSON:
company_id: int (required)
audit_type: str - 'seo', 'gbp', or 'social' (required)
force: bool - Force regeneration even if cache valid (optional)
Returns:
JSON with summary and actions list
"""
import audit_ai_service
data = request.get_json()
if not data:
return jsonify({'success': False, 'error': 'Brak danych w żądaniu'}), 400
company_id = data.get('company_id')
audit_type = data.get('audit_type')
force = data.get('force', False)
if not company_id or not audit_type:
return jsonify({'success': False, 'error': 'Wymagane: company_id i audit_type'}), 400
if audit_type not in ('seo', 'gbp', 'social'):
return jsonify({'success': False, 'error': 'audit_type musi być: seo, gbp lub social'}), 400
# Access control
if not current_user.can_edit_company(company_id):
return jsonify({'success': False, 'error': 'Brak uprawnień do analizy tej firmy'}), 403
result = audit_ai_service.generate_analysis(
company_id=company_id,
audit_type=audit_type,
user_id=current_user.id,
force=force,
)
if 'error' in result:
return jsonify({'success': False, 'error': result['error']}), 422
return jsonify({
'success': True,
'summary': result.get('summary', ''),
'actions': result.get('actions', []),
'cached': result.get('cached', False),
'generated_at': result.get('generated_at'),
})
@bp.route('/audit/generate-content', methods=['POST'])
@login_required
def api_audit_generate_content():
"""
Generate specific content for an audit action.
Request JSON:
company_id: int (required)
action_type: str (required) - e.g. 'generate_schema_org', 'generate_gbp_post'
context: dict (optional) - Extra context like platform, review_text
Returns:
JSON with generated content
"""
import audit_ai_service
data = request.get_json()
if not data:
return jsonify({'success': False, 'error': 'Brak danych w żądaniu'}), 400
company_id = data.get('company_id')
action_type = data.get('action_type')
context = data.get('context', {})
if not company_id or not action_type:
return jsonify({'success': False, 'error': 'Wymagane: company_id i action_type'}), 400
# Access control
if not current_user.can_edit_company(company_id):
return jsonify({'success': False, 'error': 'Brak uprawnień'}), 403
result = audit_ai_service.generate_content(
company_id=company_id,
action_type=action_type,
context=context,
user_id=current_user.id,
)
if 'error' in result:
return jsonify({'success': False, 'error': result['error']}), 422
return jsonify({
'success': True,
'content': result.get('content', ''),
'action_type': result.get('action_type'),
'model': result.get('model'),
})
@bp.route('/audit/actions/<slug>')
@login_required
def api_audit_actions_by_slug(slug):
"""
Get audit actions for a company.
Query params:
audit_type: str (optional) - Filter by 'seo', 'gbp', or 'social'
Returns:
JSON with list of actions
"""
import audit_ai_service
audit_type = request.args.get('audit_type')
db = SessionLocal()
try:
company = db.query(Company).filter_by(slug=slug, status='active').first()
if not company:
return jsonify({'success': False, 'error': 'Firma nie znaleziona'}), 404
if not current_user.can_edit_company(company.id):
return jsonify({'success': False, 'error': 'Brak uprawnień'}), 403
actions = audit_ai_service.get_actions_for_company(
company_id=company.id,
audit_type=audit_type,
)
return jsonify({
'success': True,
'company_id': company.id,
'actions': actions,
'count': len(actions),
})
finally:
db.close()
@bp.route('/audit/actions/<int:action_id>/status', methods=['POST'])
@login_required
def api_audit_action_update_status(action_id):
"""
Update the status of an audit action.
Request JSON:
status: str - 'implemented' or 'dismissed'
Returns:
JSON with updated status
"""
import audit_ai_service
from database import AuditAction
data = request.get_json()
if not data:
return jsonify({'success': False, 'error': 'Brak danych'}), 400
new_status = data.get('status')
if not new_status:
return jsonify({'success': False, 'error': 'Wymagane: status'}), 400
# Verify access
db = SessionLocal()
try:
action = db.query(AuditAction).filter_by(id=action_id).first()
if not action:
return jsonify({'success': False, 'error': 'Akcja nie znaleziona'}), 404
if not current_user.can_edit_company(action.company_id):
return jsonify({'success': False, 'error': 'Brak uprawnień'}), 403
finally:
db.close()
result = audit_ai_service.update_action_status(action_id, new_status)
if 'error' in result:
return jsonify({'success': False, 'error': result['error']}), 422
return jsonify({'success': True, **result})

View File

@ -5078,6 +5078,107 @@ class BenefitClick(Base):
return f"<BenefitClick {self.id} benefit={self.benefit_id}>" return f"<BenefitClick {self.id} benefit={self.benefit_id}>"
# ============================================================
# AUDIT AI ACTIONS & CACHE
# ============================================================
class AuditAction(Base):
"""
AI-generated action items from audit analysis.
Tracks suggestions, their generated content, and implementation status.
"""
__tablename__ = 'audit_actions'
id = Column(Integer, primary_key=True)
company_id = Column(Integer, ForeignKey('companies.id', ondelete='CASCADE'), nullable=False)
audit_type = Column(String(20), nullable=False) # 'seo', 'gbp', 'social'
action_type = Column(String(50), nullable=False) # 'generate_schema_org', etc.
title = Column(String(255), nullable=False)
description = Column(Text)
priority = Column(String(20), default='medium') # 'critical', 'high', 'medium', 'low'
impact_score = Column(Integer) # 1-10
effort_score = Column(Integer) # 1-10
ai_content = Column(Text) # Generated content
ai_model = Column(String(50))
status = Column(String(20), default='suggested') # 'suggested', 'approved', 'implemented', 'dismissed'
platform = Column(String(30)) # 'google', 'facebook', etc.
created_by = Column(Integer, ForeignKey('users.id'))
created_at = Column(DateTime, default=datetime.now)
implemented_at = Column(DateTime)
# Relationships
company = relationship('Company', backref='audit_actions')
creator = relationship('User', foreign_keys=[created_by])
PRIORITY_ORDER = {'critical': 0, 'high': 1, 'medium': 2, 'low': 3}
@property
def priority_rank(self):
return self.PRIORITY_ORDER.get(self.priority, 2)
def __repr__(self):
return f"<AuditAction {self.id} {self.audit_type}/{self.action_type} [{self.status}]>"
class AuditAICache(Base):
"""
Cache for AI-generated audit analyses.
Avoids regenerating analysis when audit data hasn't changed.
"""
__tablename__ = 'audit_ai_cache'
id = Column(Integer, primary_key=True)
company_id = Column(Integer, ForeignKey('companies.id', ondelete='CASCADE'), nullable=False)
audit_type = Column(String(20), nullable=False)
analysis_summary = Column(Text)
actions_json = Column(JSONB)
audit_data_hash = Column(String(64))
generated_at = Column(DateTime, default=datetime.now)
expires_at = Column(DateTime)
# Relationships
company = relationship('Company', backref='audit_ai_caches')
__table_args__ = (
UniqueConstraint('company_id', 'audit_type', name='uq_audit_ai_cache_company_type'),
)
def __repr__(self):
return f"<AuditAICache {self.id} company={self.company_id} type={self.audit_type}>"
class SocialConnection(Base):
"""
OAuth connections for social media publishing (Phase 2-3).
Stores access/refresh tokens for GBP, Facebook, Instagram, LinkedIn APIs.
"""
__tablename__ = 'social_connections'
id = Column(Integer, primary_key=True)
company_id = Column(Integer, ForeignKey('companies.id', ondelete='CASCADE'), nullable=False)
platform = Column(String(30), nullable=False)
access_token = Column(Text)
refresh_token = Column(Text)
token_expires_at = Column(DateTime)
scope = Column(Text)
external_account_id = Column(String(255))
external_account_name = Column(String(255))
connected_by = Column(Integer, ForeignKey('users.id'))
connected_at = Column(DateTime, default=datetime.now)
is_active = Column(Boolean, default=True)
# Relationships
company = relationship('Company', backref='social_connections')
connector = relationship('User', foreign_keys=[connected_by])
__table_args__ = (
UniqueConstraint('company_id', 'platform', name='uq_social_connection_company_platform'),
)
def __repr__(self):
return f"<SocialConnection {self.id} company={self.company_id} platform={self.platform}>"
# ============================================================ # ============================================================
# DATABASE INITIALIZATION # DATABASE INITIALIZATION
# ============================================================ # ============================================================

View File

@ -0,0 +1,76 @@
-- Migration 056: Audit AI Actions, Cache, and Social Connections
-- Created: 2026-02-07
-- Purpose: Tables for AI-powered audit analysis, action tracking, and social media OAuth connections
BEGIN;
-- ============================================================
-- AUDIT ACTIONS - AI-generated action items per audit type
-- ============================================================
CREATE TABLE IF NOT EXISTS audit_actions (
id SERIAL PRIMARY KEY,
company_id INT NOT NULL REFERENCES companies(id) ON DELETE CASCADE,
audit_type VARCHAR(20) NOT NULL, -- 'seo', 'gbp', 'social'
action_type VARCHAR(50) NOT NULL, -- 'generate_schema_org', 'generate_gbp_post', etc.
title VARCHAR(255) NOT NULL,
description TEXT,
priority VARCHAR(20) DEFAULT 'medium', -- 'critical', 'high', 'medium', 'low'
impact_score INT, -- 1-10
effort_score INT, -- 1-10
ai_content TEXT, -- Generated content (JSON for complex structures)
ai_model VARCHAR(50), -- Model used for generation
status VARCHAR(20) DEFAULT 'suggested', -- 'suggested', 'approved', 'implemented', 'dismissed'
platform VARCHAR(30), -- 'google', 'facebook', 'instagram', 'linkedin', 'website'
created_by INT REFERENCES users(id),
created_at TIMESTAMP DEFAULT NOW(),
implemented_at TIMESTAMP
);
CREATE INDEX idx_audit_actions_company ON audit_actions(company_id, audit_type);
CREATE INDEX idx_audit_actions_status ON audit_actions(status);
-- ============================================================
-- AUDIT AI CACHE - Cached AI analyses to avoid regeneration
-- ============================================================
CREATE TABLE IF NOT EXISTS audit_ai_cache (
id SERIAL PRIMARY KEY,
company_id INT NOT NULL REFERENCES companies(id) ON DELETE CASCADE,
audit_type VARCHAR(20) NOT NULL,
analysis_summary TEXT, -- AI-generated summary paragraph
actions_json JSONB, -- Cached action list
audit_data_hash VARCHAR(64), -- SHA256 of input data (invalidate when data changes)
generated_at TIMESTAMP DEFAULT NOW(),
expires_at TIMESTAMP, -- Auto-expire after 7 days
UNIQUE(company_id, audit_type)
);
-- ============================================================
-- SOCIAL CONNECTIONS - OAuth tokens for publishing (Phase 2-3)
-- ============================================================
CREATE TABLE IF NOT EXISTS social_connections (
id SERIAL PRIMARY KEY,
company_id INT NOT NULL REFERENCES companies(id) ON DELETE CASCADE,
platform VARCHAR(30) NOT NULL, -- 'google_business', 'facebook', 'instagram', 'linkedin'
access_token TEXT,
refresh_token TEXT,
token_expires_at TIMESTAMP,
scope TEXT,
external_account_id VARCHAR(255),
external_account_name VARCHAR(255),
connected_by INT REFERENCES users(id),
connected_at TIMESTAMP DEFAULT NOW(),
is_active BOOLEAN DEFAULT TRUE,
UNIQUE(company_id, platform)
);
-- ============================================================
-- GRANTS
-- ============================================================
GRANT ALL ON TABLE audit_actions TO nordabiz_app;
GRANT ALL ON TABLE audit_ai_cache TO nordabiz_app;
GRANT ALL ON TABLE social_connections TO nordabiz_app;
GRANT USAGE, SELECT ON SEQUENCE audit_actions_id_seq TO nordabiz_app;
GRANT USAGE, SELECT ON SEQUENCE audit_ai_cache_id_seq TO nordabiz_app;
GRANT USAGE, SELECT ON SEQUENCE social_connections_id_seq TO nordabiz_app;
COMMIT;

View File

@ -1517,6 +1517,10 @@
</div> </div>
</div> </div>
{% with audit_type='gbp' %}
{% include 'partials/audit_ai_actions.html' %}
{% endwith %}
{% else %} {% else %}
<!-- No Audit State --> <!-- No Audit State -->
<div class="no-audit-state"> <div class="no-audit-state">
@ -1900,4 +1904,168 @@ async function runAudit() {
if (btn) btn.disabled = false; if (btn) btn.disabled = false;
} }
} }
/* ============================================================
AI AUDIT ACTIONS
============================================================ */
const companyId = {{ company.id }};
const auditType = 'gbp';
async function runAIAnalysis(force) {
const prompt = document.getElementById('aiAnalyzePrompt');
const loading = document.getElementById('aiLoading');
const results = document.getElementById('aiResults');
const btn = document.getElementById('aiAnalyzeBtn');
if (btn) btn.disabled = true;
if (prompt) prompt.style.display = 'none';
if (results) results.style.display = 'none';
if (loading) loading.style.display = 'block';
try {
const response = await fetch('/api/audit/analyze', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-CSRFToken': csrfToken
},
body: JSON.stringify({
company_id: companyId,
audit_type: auditType,
force: !!force
})
});
const data = await response.json();
if (loading) loading.style.display = 'none';
if (data.success) {
renderAIResults(data);
} else {
if (prompt) prompt.style.display = 'block';
if (btn) btn.disabled = false;
showInfoModal('Blad analizy AI', data.error || 'Wystapil blad', false);
}
} catch (error) {
if (loading) loading.style.display = 'none';
if (prompt) prompt.style.display = 'block';
if (btn) btn.disabled = false;
showInfoModal('Blad polaczenia', error.message, false);
}
}
function renderAIResults(data) {
const results = document.getElementById('aiResults');
const summaryEl = document.getElementById('aiSummaryText');
const cacheInfo = document.getElementById('aiCacheInfo');
const actionsList = document.getElementById('aiActionsList');
summaryEl.textContent = data.summary || '';
cacheInfo.style.display = data.cached ? 'block' : 'none';
actionsList.innerHTML = '';
const actions = data.actions || [];
const priorityLabels = {critical: 'KRYTYCZNE', high: 'WYSOKI', medium: 'SREDNI', low: 'NISKI'};
actions.forEach((action, idx) => {
const impact = action.impact_score || 5;
const effort = action.effort_score || 5;
const card = document.createElement('div');
card.className = 'ai-action-card priority-' + (action.priority || 'medium');
card.id = 'ai-action-' + idx;
card.innerHTML = `
<div style="display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: var(--spacing-sm); flex-wrap: wrap; gap: var(--spacing-xs);">
<div style="display: flex; align-items: center; gap: var(--spacing-sm);">
<span class="ai-priority-badge ${action.priority || 'medium'}">${priorityLabels[action.priority] || 'SREDNI'}</span>
<span class="ai-action-title" style="font-weight: 600; color: var(--text-primary);">${escapeHtml(action.title || '')}</span>
</div>
</div>
<p style="color: var(--text-secondary); font-size: var(--font-size-sm); margin-bottom: var(--spacing-sm);">${escapeHtml(action.description || '')}</p>
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: var(--spacing-md); margin-bottom: var(--spacing-sm);">
<div>
<div style="font-size: var(--font-size-xs); color: var(--text-tertiary); margin-bottom: 2px;">Wplyw: ${impact}/10</div>
<div class="ai-score-bar"><div class="ai-score-bar-fill impact" style="width: ${impact * 10}%;"></div></div>
</div>
<div>
<div style="font-size: var(--font-size-xs); color: var(--text-tertiary); margin-bottom: 2px;">Wysilek: ${effort}/10</div>
<div class="ai-score-bar"><div class="ai-score-bar-fill effort" style="width: ${effort * 10}%;"></div></div>
</div>
</div>
<div class="ai-action-buttons">
<button class="btn btn-outline btn-sm" onclick="generateContent('${action.action_type}', ${idx})">
<svg width="14" height="14" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9.663 17h4.673M12 3v1m6.364 1.636l-.707.707M21 12h-1M4 12H3m3.343-5.657l-.707-.707m2.828 9.9a5 5 0 117.072 0l-.548.547A3.374 3.374 0 0014 18.469V19a2 2 0 11-4 0v-.531c0-.895-.356-1.754-.988-2.386l-.548-.547z"/></svg>
Wygeneruj tresc
</button>
<button class="btn btn-outline btn-sm" onclick="markAction(${idx}, 'implemented')" style="color: #10b981; border-color: #10b981;">
<svg width="14" height="14" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"/></svg>
Zrobione
</button>
<button class="btn btn-outline btn-sm" onclick="markAction(${idx}, 'dismissed')" style="color: var(--text-tertiary); border-color: var(--border);">Odrzuc</button>
</div>
<div id="ai-content-${idx}" style="display: none;"></div>
`;
actionsList.appendChild(card);
});
results.style.display = 'block';
window._aiActions = actions;
}
async function generateContent(actionType, idx) {
const container = document.getElementById('ai-content-' + idx);
if (!container) return;
if (container.dataset.loaded === 'true') {
container.style.display = container.style.display === 'none' ? 'block' : 'none';
return;
}
container.innerHTML = '<div style="padding: var(--spacing-md); color: var(--text-secondary); font-size: var(--font-size-sm);">Generowanie tresci...</div>';
container.style.display = 'block';
try {
const response = await fetch('/api/audit/generate-content', {
method: 'POST',
headers: {'Content-Type': 'application/json', 'X-CSRFToken': csrfToken},
body: JSON.stringify({company_id: companyId, action_type: actionType, context: {}})
});
const data = await response.json();
if (data.success && data.content) {
container.innerHTML = `<div class="ai-content-output"><button class="ai-copy-btn" onclick="copyContent(this)">Kopiuj</button><code>${escapeHtml(data.content)}</code></div>`;
container.dataset.loaded = 'true';
} else {
container.innerHTML = `<div style="padding: var(--spacing-sm); color: #ef4444; font-size: var(--font-size-sm);">${escapeHtml(data.error || 'Blad generowania')}</div>`;
}
} catch (error) {
container.innerHTML = `<div style="padding: var(--spacing-sm); color: #ef4444; font-size: var(--font-size-sm);">Blad: ${escapeHtml(error.message)}</div>`;
}
}
function copyContent(btn) {
const code = btn.parentElement.querySelector('code');
if (!code) return;
navigator.clipboard.writeText(code.textContent).then(() => {
const orig = btn.textContent;
btn.textContent = 'Skopiowano!';
setTimeout(() => { btn.textContent = orig; }, 2000);
});
}
function markAction(idx, status) {
const card = document.getElementById('ai-action-' + idx);
if (!card) return;
if (status === 'implemented') card.classList.add('implemented');
else if (status === 'dismissed') card.classList.add('dismissed');
const actions = window._aiActions || [];
if (actions[idx] && actions[idx].id) {
fetch('/api/audit/actions/' + actions[idx].id + '/status', {
method: 'POST',
headers: {'Content-Type': 'application/json', 'X-CSRFToken': csrfToken},
body: JSON.stringify({status: status})
}).catch(() => {});
}
}
function escapeHtml(text) {
const div = document.createElement('div');
div.textContent = text;
return div.innerHTML;
}
{% endblock %} {% endblock %}

View File

@ -0,0 +1,160 @@
{#
Partial: AI Audit Actions Section
Variables required:
company - Company object (with .id, .slug)
audit_type - 'seo', 'gbp', or 'social'
Include this at the bottom of audit templates:
{% include 'partials/audit_ai_actions.html' %}
#}
<!-- AI Analysis & Actions Section -->
<div id="aiActionsSection" style="margin-top: var(--spacing-2xl);">
<h2 class="section-title" style="font-size: var(--font-size-xl); font-weight: 600; color: var(--text-primary); margin-bottom: var(--spacing-md); display: flex; align-items: center; gap: var(--spacing-sm);">
<svg width="24" height="24" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9.663 17h4.673M12 3v1m6.364 1.636l-.707.707M21 12h-1M4 12H3m3.343-5.657l-.707-.707m2.828 9.9a5 5 0 117.072 0l-.548.547A3.374 3.374 0 0014 18.469V19a2 2 0 11-4 0v-.531c0-.895-.356-1.754-.988-2.386l-.548-.547z"/>
</svg>
Analiza AI i Rekomendacje
</h2>
<!-- Generate Analysis Button -->
<div id="aiAnalyzePrompt" style="background: var(--surface); padding: var(--spacing-xl); border-radius: var(--radius-lg); box-shadow: var(--shadow); text-align: center;">
<p style="color: var(--text-secondary); margin-bottom: var(--spacing-md);">
AI przeanalizuje wyniki audytu i zaproponuje priorytetowane akcje do podjecia.
</p>
<button class="btn btn-primary" onclick="runAIAnalysis()" id="aiAnalyzeBtn">
<svg width="18" height="18" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9.663 17h4.673M12 3v1m6.364 1.636l-.707.707M21 12h-1M4 12H3m3.343-5.657l-.707-.707m2.828 9.9a5 5 0 117.072 0l-.548.547A3.374 3.374 0 0014 18.469V19a2 2 0 11-4 0v-.531c0-.895-.356-1.754-.988-2.386l-.548-.547z"/>
</svg>
Wygeneruj analize AI
</button>
</div>
<!-- AI Loading Spinner -->
<div id="aiLoading" style="display: none; background: var(--surface); padding: var(--spacing-xl); border-radius: var(--radius-lg); box-shadow: var(--shadow); text-align: center;">
<div style="width: 40px; height: 40px; border: 3px solid var(--border); border-top-color: var(--primary); border-radius: 50%; animation: spin 1s linear infinite; margin: 0 auto var(--spacing-md);"></div>
<p style="color: var(--text-secondary);">Analiza AI w toku... (moze potrwac 5-10 sekund)</p>
</div>
<!-- AI Results Container -->
<div id="aiResults" style="display: none;">
<!-- Summary -->
<div id="aiSummary" style="background: linear-gradient(135deg, #eff6ff 0%, #f0fdf4 100%); padding: var(--spacing-lg); border-radius: var(--radius-lg); margin-bottom: var(--spacing-lg); border: 1px solid #bfdbfe;">
<div style="display: flex; align-items: flex-start; gap: var(--spacing-sm);">
<svg width="20" height="20" fill="none" stroke="#2563eb" viewBox="0 0 24 24" style="flex-shrink: 0; margin-top: 2px;">
<path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M13 16h-1v-4h-1m1-4h.01M21 12a9 9 0 11-18 0 9 9 0 0118 0z"/>
</svg>
<p id="aiSummaryText" style="color: var(--text-primary); line-height: 1.6; margin: 0;"></p>
</div>
<div id="aiCacheInfo" style="display: none; margin-top: var(--spacing-sm); font-size: var(--font-size-xs); color: var(--text-tertiary);">
Analiza z cache &mdash; <a href="#" onclick="runAIAnalysis(true); return false;" style="color: var(--primary);">Wygeneruj ponownie</a>
</div>
</div>
<!-- Actions List -->
<div style="font-size: var(--font-size-lg); font-weight: 600; color: var(--text-primary); margin-bottom: var(--spacing-md);">
Priorytetowe akcje
</div>
<div id="aiActionsList"></div>
</div>
</div>
<style>
.ai-action-card {
background: var(--surface);
border-radius: var(--radius-lg);
box-shadow: var(--shadow-sm);
padding: var(--spacing-lg);
margin-bottom: var(--spacing-md);
border-left: 4px solid var(--border);
transition: box-shadow 0.2s;
}
.ai-action-card:hover {
box-shadow: var(--shadow);
}
.ai-action-card.priority-critical { border-left-color: #ef4444; }
.ai-action-card.priority-high { border-left-color: #f97316; }
.ai-action-card.priority-medium { border-left-color: #f59e0b; }
.ai-action-card.priority-low { border-left-color: #84cc16; }
.ai-priority-badge {
display: inline-flex;
align-items: center;
gap: 4px;
padding: 2px 8px;
border-radius: var(--radius-sm);
font-size: var(--font-size-xs);
font-weight: 600;
text-transform: uppercase;
letter-spacing: 0.5px;
}
.ai-priority-badge.critical { background: #fee2e2; color: #dc2626; }
.ai-priority-badge.high { background: #ffedd5; color: #ea580c; }
.ai-priority-badge.medium { background: #fef3c7; color: #d97706; }
.ai-priority-badge.low { background: #ecfccb; color: #65a30d; }
.ai-score-bar {
height: 6px;
border-radius: 3px;
background: #e2e8f0;
overflow: hidden;
}
.ai-score-bar-fill {
height: 100%;
border-radius: 3px;
transition: width 0.3s;
}
.ai-score-bar-fill.impact { background: #3b82f6; }
.ai-score-bar-fill.effort { background: #f59e0b; }
.ai-content-output {
background: #1e293b;
color: #e2e8f0;
padding: var(--spacing-md);
border-radius: var(--radius);
margin-top: var(--spacing-md);
font-family: 'Menlo', 'Monaco', 'Consolas', monospace;
font-size: var(--font-size-sm);
white-space: pre-wrap;
word-break: break-all;
position: relative;
max-height: 400px;
overflow-y: auto;
}
.ai-copy-btn {
position: absolute;
top: 8px;
right: 8px;
background: rgba(255,255,255,0.1);
border: 1px solid rgba(255,255,255,0.2);
color: #e2e8f0;
padding: 4px 10px;
border-radius: var(--radius-sm);
font-size: var(--font-size-xs);
cursor: pointer;
transition: background 0.2s;
}
.ai-copy-btn:hover {
background: rgba(255,255,255,0.2);
}
.ai-action-buttons {
display: flex;
gap: var(--spacing-sm);
margin-top: var(--spacing-md);
flex-wrap: wrap;
}
.ai-action-card.implemented {
opacity: 0.6;
border-left-color: #10b981;
}
.ai-action-card.implemented .ai-action-title {
text-decoration: line-through;
}
.ai-action-card.dismissed {
display: none;
}
</style>

View File

@ -847,6 +847,12 @@
</div> </div>
{% endif %} {% endif %}
{% if seo_data %}
{% with audit_type='seo' %}
{% include 'partials/audit_ai_actions.html' %}
{% endwith %}
{% endif %}
<!-- Loading Overlay --> <!-- Loading Overlay -->
<div class="loading-overlay" id="loadingOverlay"> <div class="loading-overlay" id="loadingOverlay">
<div class="loading-content"> <div class="loading-content">
@ -939,4 +945,206 @@ async function runAudit() {
if (btn) btn.disabled = false; if (btn) btn.disabled = false;
} }
} }
/* ============================================================
AI AUDIT ACTIONS
============================================================ */
const companyId = {{ company.id }};
const auditType = 'seo';
async function runAIAnalysis(force) {
const prompt = document.getElementById('aiAnalyzePrompt');
const loading = document.getElementById('aiLoading');
const results = document.getElementById('aiResults');
const btn = document.getElementById('aiAnalyzeBtn');
if (btn) btn.disabled = true;
if (prompt) prompt.style.display = 'none';
if (results) results.style.display = 'none';
if (loading) loading.style.display = 'block';
try {
const response = await fetch('/api/audit/analyze', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-CSRFToken': csrfToken
},
body: JSON.stringify({
company_id: companyId,
audit_type: auditType,
force: !!force
})
});
const data = await response.json();
if (loading) loading.style.display = 'none';
if (data.success) {
renderAIResults(data);
} else {
if (prompt) prompt.style.display = 'block';
if (btn) btn.disabled = false;
showInfoModal('Blad analizy AI', data.error || 'Wystapil blad', false);
}
} catch (error) {
if (loading) loading.style.display = 'none';
if (prompt) prompt.style.display = 'block';
if (btn) btn.disabled = false;
showInfoModal('Blad polaczenia', error.message, false);
}
}
function renderAIResults(data) {
const results = document.getElementById('aiResults');
const summaryEl = document.getElementById('aiSummaryText');
const cacheInfo = document.getElementById('aiCacheInfo');
const actionsList = document.getElementById('aiActionsList');
summaryEl.textContent = data.summary || '';
if (data.cached) {
cacheInfo.style.display = 'block';
} else {
cacheInfo.style.display = 'none';
}
actionsList.innerHTML = '';
const actions = data.actions || [];
const priorityLabels = {critical: 'KRYTYCZNE', high: 'WYSOKI', medium: 'SREDNI', low: 'NISKI'};
actions.forEach((action, idx) => {
const card = document.createElement('div');
card.className = 'ai-action-card priority-' + (action.priority || 'medium');
card.id = 'ai-action-' + idx;
const impact = action.impact_score || 5;
const effort = action.effort_score || 5;
card.innerHTML = `
<div style="display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: var(--spacing-sm); flex-wrap: wrap; gap: var(--spacing-xs);">
<div style="display: flex; align-items: center; gap: var(--spacing-sm);">
<span class="ai-priority-badge ${action.priority || 'medium'}">${priorityLabels[action.priority] || 'SREDNI'}</span>
<span class="ai-action-title" style="font-weight: 600; color: var(--text-primary);">${escapeHtml(action.title || '')}</span>
</div>
</div>
<p style="color: var(--text-secondary); font-size: var(--font-size-sm); margin-bottom: var(--spacing-sm);">${escapeHtml(action.description || '')}</p>
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: var(--spacing-md); margin-bottom: var(--spacing-sm);">
<div>
<div style="font-size: var(--font-size-xs); color: var(--text-tertiary); margin-bottom: 2px;">Wplyw: ${impact}/10</div>
<div class="ai-score-bar"><div class="ai-score-bar-fill impact" style="width: ${impact * 10}%;"></div></div>
</div>
<div>
<div style="font-size: var(--font-size-xs); color: var(--text-tertiary); margin-bottom: 2px;">Wysilek: ${effort}/10</div>
<div class="ai-score-bar"><div class="ai-score-bar-fill effort" style="width: ${effort * 10}%;"></div></div>
</div>
</div>
<div class="ai-action-buttons">
<button class="btn btn-outline btn-sm" onclick="generateContent('${action.action_type}', ${idx})">
<svg width="14" height="14" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9.663 17h4.673M12 3v1m6.364 1.636l-.707.707M21 12h-1M4 12H3m3.343-5.657l-.707-.707m2.828 9.9a5 5 0 117.072 0l-.548.547A3.374 3.374 0 0014 18.469V19a2 2 0 11-4 0v-.531c0-.895-.356-1.754-.988-2.386l-.548-.547z"/></svg>
Wygeneruj tresc
</button>
<button class="btn btn-outline btn-sm" onclick="markAction(${idx}, 'implemented')" style="color: #10b981; border-color: #10b981;">
<svg width="14" height="14" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"/></svg>
Zrobione
</button>
<button class="btn btn-outline btn-sm" onclick="markAction(${idx}, 'dismissed')" style="color: var(--text-tertiary); border-color: var(--border);">
Odrzuc
</button>
</div>
<div id="ai-content-${idx}" style="display: none;"></div>
`;
actionsList.appendChild(card);
});
results.style.display = 'block';
// Store actions data for content generation
window._aiActions = actions;
}
async function generateContent(actionType, idx) {
const container = document.getElementById('ai-content-' + idx);
if (!container) return;
// If already has content, toggle visibility
if (container.dataset.loaded === 'true') {
container.style.display = container.style.display === 'none' ? 'block' : 'none';
return;
}
container.innerHTML = '<div style="padding: var(--spacing-md); color: var(--text-secondary); font-size: var(--font-size-sm);">Generowanie tresci...</div>';
container.style.display = 'block';
try {
const response = await fetch('/api/audit/generate-content', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-CSRFToken': csrfToken
},
body: JSON.stringify({
company_id: companyId,
action_type: actionType,
context: {}
})
});
const data = await response.json();
if (data.success && data.content) {
container.innerHTML = `
<div class="ai-content-output">
<button class="ai-copy-btn" onclick="copyContent(this)">Kopiuj</button>
<code>${escapeHtml(data.content)}</code>
</div>
`;
container.dataset.loaded = 'true';
} else {
container.innerHTML = `<div style="padding: var(--spacing-sm); color: #ef4444; font-size: var(--font-size-sm);">${escapeHtml(data.error || 'Blad generowania')}</div>`;
}
} catch (error) {
container.innerHTML = `<div style="padding: var(--spacing-sm); color: #ef4444; font-size: var(--font-size-sm);">Blad: ${escapeHtml(error.message)}</div>`;
}
}
function copyContent(btn) {
const code = btn.parentElement.querySelector('code');
if (!code) return;
navigator.clipboard.writeText(code.textContent).then(() => {
const orig = btn.textContent;
btn.textContent = 'Skopiowano!';
setTimeout(() => { btn.textContent = orig; }, 2000);
});
}
function markAction(idx, status) {
const card = document.getElementById('ai-action-' + idx);
if (!card) return;
if (status === 'implemented') {
card.classList.add('implemented');
} else if (status === 'dismissed') {
card.classList.add('dismissed');
}
// Fire and forget status update to backend
const actions = window._aiActions || [];
if (actions[idx] && actions[idx].id) {
fetch('/api/audit/actions/' + actions[idx].id + '/status', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-CSRFToken': csrfToken
},
body: JSON.stringify({ status: status })
}).catch(() => {});
}
}
function escapeHtml(text) {
const div = document.createElement('div');
div.textContent = text;
return div.innerHTML;
}
{% endblock %} {% endblock %}

View File

@ -1036,6 +1036,12 @@
</div> </div>
{% endif %} {% endif %}
{% if social_data %}
{% with audit_type='social' %}
{% include 'partials/audit_ai_actions.html' %}
{% endwith %}
{% endif %}
<!-- Loading Overlay --> <!-- Loading Overlay -->
<div class="loading-overlay" id="loadingOverlay"> <div class="loading-overlay" id="loadingOverlay">
<div class="loading-content"> <div class="loading-content">
@ -1348,4 +1354,168 @@ document.getElementById('modalOverlay').addEventListener('click', function(e) {
closeModal(); closeModal();
} }
}); });
/* ============================================================
AI AUDIT ACTIONS
============================================================ */
const companyId = {{ company.id }};
const auditType = 'social';
async function runAIAnalysis(force) {
const prompt = document.getElementById('aiAnalyzePrompt');
const loading = document.getElementById('aiLoading');
const results = document.getElementById('aiResults');
const btn = document.getElementById('aiAnalyzeBtn');
if (btn) btn.disabled = true;
if (prompt) prompt.style.display = 'none';
if (results) results.style.display = 'none';
if (loading) loading.style.display = 'block';
try {
const response = await fetch('/api/audit/analyze', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'X-CSRFToken': csrfToken
},
body: JSON.stringify({
company_id: companyId,
audit_type: auditType,
force: !!force
})
});
const data = await response.json();
if (loading) loading.style.display = 'none';
if (data.success) {
renderAIResults(data);
} else {
if (prompt) prompt.style.display = 'block';
if (btn) btn.disabled = false;
showModal('Blad analizy AI', data.error || 'Wystapil blad', false);
}
} catch (error) {
if (loading) loading.style.display = 'none';
if (prompt) prompt.style.display = 'block';
if (btn) btn.disabled = false;
showModal('Blad polaczenia', error.message, false);
}
}
function renderAIResults(data) {
const results = document.getElementById('aiResults');
const summaryEl = document.getElementById('aiSummaryText');
const cacheInfo = document.getElementById('aiCacheInfo');
const actionsList = document.getElementById('aiActionsList');
summaryEl.textContent = data.summary || '';
cacheInfo.style.display = data.cached ? 'block' : 'none';
actionsList.innerHTML = '';
const actions = data.actions || [];
const priorityLabels = {critical: 'KRYTYCZNE', high: 'WYSOKI', medium: 'SREDNI', low: 'NISKI'};
actions.forEach((action, idx) => {
const impact = action.impact_score || 5;
const effort = action.effort_score || 5;
const card = document.createElement('div');
card.className = 'ai-action-card priority-' + (action.priority || 'medium');
card.id = 'ai-action-' + idx;
card.innerHTML = `
<div style="display: flex; justify-content: space-between; align-items: flex-start; margin-bottom: var(--spacing-sm); flex-wrap: wrap; gap: var(--spacing-xs);">
<div style="display: flex; align-items: center; gap: var(--spacing-sm);">
<span class="ai-priority-badge ${action.priority || 'medium'}">${priorityLabels[action.priority] || 'SREDNI'}</span>
<span class="ai-action-title" style="font-weight: 600; color: var(--text-primary);">${escapeHtml(action.title || '')}</span>
</div>
</div>
<p style="color: var(--text-secondary); font-size: var(--font-size-sm); margin-bottom: var(--spacing-sm);">${escapeHtml(action.description || '')}</p>
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: var(--spacing-md); margin-bottom: var(--spacing-sm);">
<div>
<div style="font-size: var(--font-size-xs); color: var(--text-tertiary); margin-bottom: 2px;">Wplyw: ${impact}/10</div>
<div class="ai-score-bar"><div class="ai-score-bar-fill impact" style="width: ${impact * 10}%;"></div></div>
</div>
<div>
<div style="font-size: var(--font-size-xs); color: var(--text-tertiary); margin-bottom: 2px;">Wysilek: ${effort}/10</div>
<div class="ai-score-bar"><div class="ai-score-bar-fill effort" style="width: ${effort * 10}%;"></div></div>
</div>
</div>
<div class="ai-action-buttons">
<button class="btn btn-outline btn-sm" onclick="generateContent('${action.action_type}', ${idx})">
<svg width="14" height="14" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M9.663 17h4.673M12 3v1m6.364 1.636l-.707.707M21 12h-1M4 12H3m3.343-5.657l-.707-.707m2.828 9.9a5 5 0 117.072 0l-.548.547A3.374 3.374 0 0014 18.469V19a2 2 0 11-4 0v-.531c0-.895-.356-1.754-.988-2.386l-.548-.547z"/></svg>
Wygeneruj tresc
</button>
<button class="btn btn-outline btn-sm" onclick="markAction(${idx}, 'implemented')" style="color: #10b981; border-color: #10b981;">
<svg width="14" height="14" fill="none" stroke="currentColor" viewBox="0 0 24 24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M5 13l4 4L19 7"/></svg>
Zrobione
</button>
<button class="btn btn-outline btn-sm" onclick="markAction(${idx}, 'dismissed')" style="color: var(--text-tertiary); border-color: var(--border);">Odrzuc</button>
</div>
<div id="ai-content-${idx}" style="display: none;"></div>
`;
actionsList.appendChild(card);
});
results.style.display = 'block';
window._aiActions = actions;
}
async function generateContent(actionType, idx) {
const container = document.getElementById('ai-content-' + idx);
if (!container) return;
if (container.dataset.loaded === 'true') {
container.style.display = container.style.display === 'none' ? 'block' : 'none';
return;
}
container.innerHTML = '<div style="padding: var(--spacing-md); color: var(--text-secondary); font-size: var(--font-size-sm);">Generowanie tresci...</div>';
container.style.display = 'block';
try {
const response = await fetch('/api/audit/generate-content', {
method: 'POST',
headers: {'Content-Type': 'application/json', 'X-CSRFToken': csrfToken},
body: JSON.stringify({company_id: companyId, action_type: actionType, context: {}})
});
const data = await response.json();
if (data.success && data.content) {
container.innerHTML = `<div class="ai-content-output"><button class="ai-copy-btn" onclick="copyContent(this)">Kopiuj</button><code>${escapeHtml(data.content)}</code></div>`;
container.dataset.loaded = 'true';
} else {
container.innerHTML = `<div style="padding: var(--spacing-sm); color: #ef4444; font-size: var(--font-size-sm);">${escapeHtml(data.error || 'Blad generowania')}</div>`;
}
} catch (error) {
container.innerHTML = `<div style="padding: var(--spacing-sm); color: #ef4444; font-size: var(--font-size-sm);">Blad: ${escapeHtml(error.message)}</div>`;
}
}
function copyContent(btn) {
const code = btn.parentElement.querySelector('code');
if (!code) return;
navigator.clipboard.writeText(code.textContent).then(() => {
const orig = btn.textContent;
btn.textContent = 'Skopiowano!';
setTimeout(() => { btn.textContent = orig; }, 2000);
});
}
function markAction(idx, status) {
const card = document.getElementById('ai-action-' + idx);
if (!card) return;
if (status === 'implemented') card.classList.add('implemented');
else if (status === 'dismissed') card.classList.add('dismissed');
const actions = window._aiActions || [];
if (actions[idx] && actions[idx].id) {
fetch('/api/audit/actions/' + actions[idx].id + '/status', {
method: 'POST',
headers: {'Content-Type': 'application/json', 'X-CSRFToken': csrfToken},
body: JSON.stringify({status: status})
}).catch(() => {});
}
}
function escapeHtml(text) {
const div = document.createElement('div');
div.textContent = text;
return div.innerHTML;
}
{% endblock %} {% endblock %}