Content Calendar, Content Gap Analysis, and Content Optimization
This commit is contained in:
ajaysi
80
lib/ai_seo_tools/content_calendar/examples/calendar_usage.py
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80
lib/ai_seo_tools/content_calendar/examples/calendar_usage.py
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from datetime import datetime
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from typing import List, Dict, Any
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from ..core.calendar_manager import CalendarManager
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from ..models.calendar import ContentType, Platform
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def create_content_calendar(
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website_url: str,
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start_date: datetime,
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duration: str,
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platforms: List[str]
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) -> Dict[str, Any]:
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"""
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Example of creating a content calendar.
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Args:
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website_url: URL of the website to analyze
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start_date: When to start the calendar
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duration: How long the calendar should span
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platforms: List of platforms to create content for
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Returns:
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Dictionary containing the calendar data
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"""
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# Initialize calendar manager
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calendar_manager = CalendarManager()
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# Create calendar
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calendar = calendar_manager.create_calendar(
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start_date=start_date,
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duration=duration,
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platforms=platforms,
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website_url=website_url
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)
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# Export calendar
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calendar_data = calendar_manager.export_calendar()
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return calendar_data
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def main():
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"""Example usage of the content calendar system."""
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# Example parameters
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website_url = "https://example.com"
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start_date = datetime.now()
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duration = "monthly"
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platforms = [
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Platform.WEBSITE.value,
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Platform.FACEBOOK.value,
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Platform.TWITTER.value,
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Platform.LINKEDIN.value
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]
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try:
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# Create calendar
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calendar_data = create_content_calendar(
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website_url=website_url,
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start_date=start_date,
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duration=duration,
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platforms=platforms
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)
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# Print calendar summary
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print("\nContent Calendar Summary:")
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print(f"Duration: {calendar_data['duration']}")
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print(f"Platforms: {', '.join(calendar_data['platforms'])}")
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print("\nScheduled Content:")
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for date, items in calendar_data['schedule'].items():
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print(f"\n{date}:")
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for item in items:
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print(f"- {item['title']} ({item['content_type']})")
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print(f" Platforms: {', '.join(item['platforms'])}")
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print(f" Status: {item['status']}")
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except Exception as e:
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print(f"Error creating calendar: {str(e)}")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,138 @@
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from datetime import datetime
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from typing import Dict, Any
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from ..models.calendar import ContentItem, ContentType, Platform, SEOData
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from ..core.content_brief import ContentBriefGenerator
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def create_content_brief(
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title: str,
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content_type: ContentType,
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platforms: list[Platform],
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website_url: str,
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target_audience: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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Create a content brief for the given content.
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Args:
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title: Content title
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content_type: Type of content
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platforms: List of platforms to publish on
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website_url: Website URL for context
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target_audience: Target audience information
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Returns:
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Dictionary containing the content brief
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"""
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# Create content item
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content_item = ContentItem(
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id=f"content-{datetime.now().strftime('%Y%m%d%H%M%S')}",
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title=title,
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description=f"Content brief for {title}",
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content_type=content_type,
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platforms=platforms,
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publish_date=datetime.now(),
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seo_data=SEOData(
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keywords=[], # Will be generated by SEO tools
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meta_description="", # Will be generated by SEO tools
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structured_data={}
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),
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platform_specs={}, # Will be generated based on platforms
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context={
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"website_url": website_url,
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"target_audience": target_audience.get("demographics", {}).get("profession", ""),
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"content_goals": ["educate", "generate leads"]
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}
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)
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# Initialize content brief generator
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generator = ContentBriefGenerator()
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# Generate brief
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brief = generator.generate_brief(
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content_item=content_item,
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target_audience=target_audience
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)
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return brief
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def main():
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"""Example usage of content brief generation."""
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# Example content details
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title = "10 Ways to Improve Your SEO Strategy"
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content_type = ContentType.BLOG_POST
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platforms = [Platform.WEBSITE, Platform.LINKEDIN]
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website_url = "https://example.com"
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# Example target audience
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target_audience = {
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"demographics": {
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"age_range": "25-45",
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"profession": "digital marketers",
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"experience_level": "intermediate"
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},
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"interests": [
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"SEO",
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"content marketing",
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"digital strategy",
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"search engine optimization"
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],
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"pain_points": [
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"low search rankings",
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"poor content performance",
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"lack of organic traffic",
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"difficulty in keyword research"
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],
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"goals": [
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"improve search rankings",
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"increase organic traffic",
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"generate more leads",
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"build brand authority"
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]
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}
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try:
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# Generate content brief
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brief = create_content_brief(
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title=title,
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content_type=content_type,
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platforms=platforms,
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website_url=website_url,
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target_audience=target_audience
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)
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# Print brief summary
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print("\nContent Brief Summary:")
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print(f"Title: {brief['title']}")
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print(f"Content Type: {brief['content_type']}")
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print("\nOutline:")
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for heading in brief['outline']['main_headings']:
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print(f"\n- {heading['title']}")
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print(f" Keywords: {', '.join(heading['keywords'])}")
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print(f" Summary: {heading['summary']}")
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# Print subheadings
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subheadings = brief['outline']['subheadings'].get(heading['title'], [])
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for subheading in subheadings:
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print(f" - {subheading['title']}")
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print(f" Keywords: {', '.join(subheading['keywords'])}")
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print("\nKey Points:")
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for point in brief['key_points']:
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print(f"\n- {point['point']}")
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print(f" Importance: {point['importance']}")
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print(f" Evidence: {', '.join(point['supporting_evidence'])}")
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print("\nContent Flow:")
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flow = brief['content_flow']
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print(f"Introduction: {flow['introduction'].get('summary', '')}")
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print(f"Main Sections: {len(flow['main_sections'])} sections")
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print(f"Conclusion: {flow['conclusion'].get('summary', '')}")
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print(f"Transitions: {len(flow['transitions'])} transition points")
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except Exception as e:
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print(f"Error generating content brief: {str(e)}")
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if __name__ == '__main__':
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main()
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@@ -0,0 +1,196 @@
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, Any, List
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from ..integrations.integration_manager import IntegrationManager
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# Set up logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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def create_cross_platform_content(
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title: str,
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content: str,
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platforms: List[str],
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content_type: str,
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target_audience: Dict[str, Any],
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industry: str,
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keywords: List[str]
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) -> Dict[str, Any]:
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"""Create and optimize content for multiple platforms."""
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try:
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# Initialize integration manager
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integration_manager = IntegrationManager()
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# Prepare content item
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content_item = {
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'title': title,
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'content': content,
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'content_type': content_type,
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'keywords': keywords,
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'target_audience': target_audience,
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'industry': industry
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}
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# Get platform suggestions
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suggestions = integration_manager.get_platform_suggestions(
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content=content_item,
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platforms=platforms
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)
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# Validate content for each platform
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validation_results = {}
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for platform in platforms:
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validation = integration_manager.validate_platform_content(
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content=content_item,
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platform=platform
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)
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validation_results[platform] = validation
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# Optimize content for each platform
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optimized_content = integration_manager.optimize_cross_platform_content(
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content=content_item,
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platforms=platforms
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)
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return {
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'original_content': content_item,
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'platform_suggestions': suggestions,
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'validation_results': validation_results,
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'optimized_content': optimized_content
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}
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except Exception as e:
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logger.error(f"Error creating cross-platform content: {str(e)}")
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return {
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'error': str(e)
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}
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def create_content_calendar(
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start_date: datetime,
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end_date: datetime,
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platforms: List[str],
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content_types: List[str],
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target_audience: Dict[str, Any],
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industry: str,
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keywords: List[str]
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) -> Dict[str, Any]:
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"""Create a cross-platform content calendar."""
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try:
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# Initialize integration manager
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integration_manager = IntegrationManager()
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# Create calendar
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calendar = integration_manager.create_cross_platform_calendar(
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start_date=start_date,
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end_date=end_date,
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platforms=platforms,
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content_types=content_types,
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target_audience=target_audience,
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industry=industry,
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keywords=keywords
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)
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return calendar
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except Exception as e:
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logger.error(f"Error creating content calendar: {str(e)}")
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return {
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'error': str(e)
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}
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def main():
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"""Main function to demonstrate integration manager usage."""
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# Example content details
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title = "The Future of AI in Content Marketing"
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content = """
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Artificial Intelligence is revolutionizing the way we approach content marketing.
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From automated content generation to personalized recommendations, AI tools are
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helping marketers create more engaging and effective content strategies.
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Key points:
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1. AI-powered content generation
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2. Personalized content recommendations
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3. Automated content optimization
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4. Data-driven content strategy
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5. Future trends in AI marketing
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"""
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# Platform and content settings
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platforms = ['instagram', 'twitter', 'linkedin', 'blog', 'facebook']
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content_type = 'article'
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target_audience = {
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'age_range': '25-34',
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'interests': ['technology', 'marketing', 'AI'],
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'location': 'global',
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'profession': 'marketing professionals'
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}
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industry = 'technology'
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keywords = ['AI', 'content marketing', 'automation', 'personalization']
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# Create cross-platform content
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logger.info("Creating cross-platform content...")
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content_result = create_cross_platform_content(
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title=title,
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content=content,
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platforms=platforms,
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content_type=content_type,
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target_audience=target_audience,
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industry=industry,
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keywords=keywords
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)
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# Print content results
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logger.info("\nCross-Platform Content Results:")
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logger.info("===============================")
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# Print platform suggestions
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logger.info("\nPlatform Suggestions:")
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for platform, suggestions in content_result['platform_suggestions'].items():
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logger.info(f"\n{platform.upper()}:")
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for key, value in suggestions.items():
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logger.info(f" {key}: {value}")
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# Print validation results
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logger.info("\nValidation Results:")
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for platform, validation in content_result['validation_results'].items():
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logger.info(f"\n{platform.upper()}:")
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logger.info(f" Valid: {validation['is_valid']}")
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if not validation['is_valid']:
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logger.info(f" Error: {validation.get('error', 'N/A')}")
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# Print optimized content
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logger.info("\nOptimized Content:")
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for platform, optimized in content_result['optimized_content'].items():
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logger.info(f"\n{platform.upper()}:")
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for key, value in optimized.items():
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logger.info(f" {key}: {value}")
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# Create content calendar
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logger.info("\nCreating content calendar...")
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start_date = datetime.now()
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end_date = start_date + timedelta(days=30)
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calendar_result = create_content_calendar(
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start_date=start_date,
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end_date=end_date,
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platforms=platforms,
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content_types=[content_type],
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target_audience=target_audience,
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industry=industry,
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keywords=keywords
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)
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# Print calendar results
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logger.info("\nContent Calendar Results:")
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logger.info("========================")
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# Print platform calendars
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logger.info("\nPlatform Calendars:")
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for platform, calendar in calendar_result['platform_calendars'].items():
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logger.info(f"\n{platform.upper()}:")
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logger.info(f" Content Items: {len(calendar['content_items'])}")
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for item in calendar['content_items']:
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logger.info(f" - {item['original_item']['title']}")
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if __name__ == '__main__':
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main()
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@@ -0,0 +1,142 @@
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from typing import Dict, Any
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from datetime import datetime
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from ..integrations.platform_adapters import UnifiedPlatformAdapter
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def create_platform_content(
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title: str,
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content: str,
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platforms: list,
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context: Dict[str, Any] = None
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) -> Dict[str, Any]:
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"""
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Create platform-specific content using the UnifiedPlatformAdapter.
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Args:
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title: The title of the content
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content: The main content to be adapted
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platforms: List of platforms to adapt content for
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context: Additional context for content adaptation
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Returns:
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Dict containing adapted content for each platform
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"""
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# Initialize the platform adapter
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adapter = UnifiedPlatformAdapter()
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# Prepare base content
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base_content = {
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'title': title,
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'content': content,
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'keywords': ['content', 'marketing', 'social media'],
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'tone': 'professional',
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'cta': 'Learn More',
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'audience': 'For All',
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'language': 'English',
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'industry': 'technology',
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'word_count': 1000
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}
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# Adapt content for each platform
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adapted_content = {}
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for platform in platforms:
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try:
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platform_content = adapter.adapt_content(
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content=base_content,
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platform=platform,
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context=context
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)
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adapted_content[platform] = platform_content
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except Exception as e:
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print(f"Error adapting content for {platform}: {str(e)}")
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adapted_content[platform] = {'error': str(e)}
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return adapted_content
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def main():
|
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"""Example usage of platform content adaptation."""
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# Example content
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title = "The Future of AI in Content Marketing"
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content = """
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Artificial Intelligence is revolutionizing content marketing in unprecedented ways.
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From automated content generation to personalized user experiences, AI is becoming
|
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an indispensable tool for marketers. This article explores the latest trends and
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innovations in AI-powered content marketing.
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"""
|
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# Example context
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context = {
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'target_audience': 'marketing professionals',
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'campaign_goals': ['awareness', 'engagement', 'lead generation'],
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'brand_voice': 'authoritative yet approachable',
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'content_theme': 'technology and innovation'
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}
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# Platforms to adapt content for
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platforms = ['instagram', 'twitter', 'linkedin', 'blog', 'facebook']
|
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# Generate platform-specific content
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adapted_content = create_platform_content(
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title=title,
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content=content,
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platforms=platforms,
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context=context
|
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)
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# Print results
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print("\nPlatform-Specific Content Adaptation Results:")
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print("=" * 50)
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for platform, content in adapted_content.items():
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print(f"\n{platform.upper()} Content:")
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print("-" * 30)
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if 'error' in content:
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print(f"Error: {content['error']}")
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continue
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# Print platform-specific content
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if platform == 'instagram':
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print("\nCaptions:")
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for caption in content['captions']:
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print(f"- {caption}")
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print("\nHashtags:")
|
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print(content['hashtags'])
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||||
|
||||
elif platform == 'twitter':
|
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print("\nTweets:")
|
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for tweet in content['tweets']:
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print(f"- {tweet}")
|
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print("\nThread Structure:")
|
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print(content['thread_structure'])
|
||||
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elif platform == 'linkedin':
|
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print("\nPost:")
|
||||
print(content['post'])
|
||||
print("\nEngagement Optimization:")
|
||||
print(content['engagement_optimization'])
|
||||
|
||||
elif platform == 'blog':
|
||||
print("\nPost:")
|
||||
print(content['post'])
|
||||
print("\nSEO Optimization:")
|
||||
print(content['seo_optimization'])
|
||||
|
||||
elif platform == 'facebook':
|
||||
print("\nPost:")
|
||||
print(content['post'])
|
||||
print("\nEngagement Optimization:")
|
||||
print(content['engagement_optimization'])
|
||||
|
||||
# Print media suggestions
|
||||
print("\nMedia Suggestions:")
|
||||
for media in content['media_suggestions']:
|
||||
print(f"- {media['type']}: {media['description']}")
|
||||
|
||||
# Print platform-specific recommendations
|
||||
print("\nPlatform-Specific Recommendations:")
|
||||
for key, value in content['platform_specific'].items():
|
||||
print(f"- {key}: {value}")
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user