Automating Content Creation with AI

import openai, os, json

client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])

def generate_article(topic: str, target_keywords: list[str]) -> dict:
    # Step 1: Research and outline
    outline_resp = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "system", "content": """You are an SEO content strategist.
Create a detailed article outline with:
- title (under 60 chars, include primary keyword)
- meta_description (150 chars)
- h2_sections (5-7 sections, each with 2-3 key points)
Reply with JSON only."""}, {"role": "user", "content": f"Topic: {topic}\nKeywords: {', '.join(target_keywords)}"}],
        response_format={"type": "json_object"},
        temperature=0.5
    )
    outline = json.loads(outline_resp.choices[0].message.content)

    # Step 2: Write each section
    sections = []
    for section in outline.get("h2_sections", []):
        section_resp = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[
                {"role": "system", "content": "Write a detailed, informative section for a blog article. Use short paragraphs, include examples, and maintain a professional tone. 200-300 words."},
                {"role": "user", "content": f"Section: {section}\nArticle topic: {topic}\nKeywords to include: {', '.join(target_keywords)}"}
            ],
            temperature=0.7
        )
        sections.append({"heading": section if isinstance(section, str) else section.get("heading", ""),
                         "content": section_resp.choices[0].message.content})

    # Step 3: Generate intro and conclusion
    intro_resp = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "system", "content": "Write a compelling article introduction in 2-3 paragraphs. Hook the reader, state the problem, and preview the solution."}, {"role": "user", "content": f"Topic: {topic}\nSections: {json.dumps([s['heading'] for s in sections])}"}],
        temperature=0.7
    )

    return {"outline": outline, "intro": intro_resp.choices[0].message.content, "sections": sections}

article = generate_article("AI automation for small businesses", ["ai automation", "small business automation", "automate workflows"])
print(f"Title: {article['outline'].get('title', 'N/A')}")
print(f"Sections: {len(article['sections'])}")

def generate_social_posts(article_title: str, article_summary: str) -> dict:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "system", "content": """Generate social media posts for the given article.
Return JSON with these keys:
- twitter (under 280 chars, include 2-3 hashtags)
- linkedin (professional tone, 150-300 words, include call to action)
- instagram (engaging caption, 5-10 hashtags at end)"""}, {"role": "user", "content": f"Title: {article_title}\nSummary: {article_summary}"}],
        response_format={"type": "json_object"},
        temperature=0.8
    )
    return json.loads(response.choices[0].message.content)

posts = generate_social_posts(
    "10 Ways AI Automation Saves Small Businesses Time",
    "A guide to implementing AI automation in small businesses, covering email, scheduling, and customer support."
)
for platform, text in posts.items():
    print(f"\n--- {platform.upper()} ---\n{text}")

def build_content_cluster(pillar_topic: str) -> dict:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "system", "content": """You are an SEO content strategist. Given a pillar topic, generate a content cluster:
- pillar_article: {title, keyword, description}
- supporting_articles: array of 8-10 articles, each with {title, keyword, search_intent, internal_link_to_pillar}
- content_calendar: array of {week, article_title, target_publish_date}

Ensure keywords have realistic search volume potential and articles interlink logically.
Reply with JSON."""}, {"role": "user", "content": pillar_topic}],
        response_format={"type": "json_object"},
        temperature=0.6
    )
    return json.loads(response.choices[0].message.content)

cluster = build_content_cluster("AI automation for business")
print(f"Pillar: {cluster.get('pillar_article', {}).get('title', 'N/A')}")
print(f"Supporting articles: {len(cluster.get('supporting_articles', []))}")
for article in cluster.get("supporting_articles", []):
    print(f"  - {article.get('title', 'N/A')} [{article.get('keyword', 'N/A')}]")