Want a Blog But Hate Writing Use This AI-Powered Workflow Instead

import requests
import os
import json
from datetime import datetime, timedelta

# Remember to set these as environment variables!
GOOGLE_TRENDS_API_KEY = os.getenv("GOOGLE_TRENDS_API_KEY_2025") # Assuming Google has a public API by now or using a wrapper
SERPAPI_API_KEY = os.getenv("SERPAPI_API_KEY")

def get_trending_topics(country='US', days_ago=1):
    """
    Simulated Google Trends API call for trending searches.
    In 2025, we'd expect a direct API or better wrappers.
    For now, this is conceptual.
    """
    print(f"Fetching trending topics for {country} from {days_ago} day(s) ago...")
    # This would ideally hit a real Google Trends API endpoint
    # For demonstration, we'll return mock data.
    mock_trends = {
        "AI Content Automation": 85,
        "GPT-5 Applications": 78,
        "Solo Creator Monetization 2025": 72,
        "Python Blogging Tools": 65,
        "Future of Blogging AI": 60
    }
    return sorted(mock_trends.items(), key=lambda item: item[1], reverse=True)

def analyze_serp(query):
    """
    Uses SerpAPI to get search engine results page data.
    """
    print(f"Analyzing SERP for '{query}'...")
    url = "https://serpapi.com/search"
    params = {
        "engine": "google",
        "q": query,
        "api_key": SERPAPI_API_KEY,
        "num": 10 # Top 10 results
    }
    response = requests.get(url, params=params)
    response.raise_for_status() # Raise an exception for HTTP errors
    data = response.json()

    results = []
    for result in data.get("organic_results", []):
        results.append({
            "title": result.get("title"),
            "link": result.get("link"),
            "snippet": result.get("snippet")
        })
    return results

if __name__ == "__main__":
    print("--- Step 1: Idea Generation & Validation ---")
    trending_topics = get_trending_topics()
    print("\nTop Trending Topics (Simulated):")
    for topic, score in trending_topics:
        print(f"- {topic} (Trend Score: {score})")

    # Pick the top topic for SERP analysis
    if trending_topics:
        selected_topic = trending_topics[0][0]
        serp_results = analyze_serp(selected_topic)
        print(f"\nSERP Analysis for '{selected_topic}':")
        for i, result in enumerate(serp_results[:3]): # Show top 3 snippets
            print(f"  Result {i+1}:")
            print(f"    Title: {result['title']}")
            print(f"    Link: {result['link']}")
            print(f"    Snippet: {result['snippet'][:100]}...") # Truncate snippet
        print(f"\nChosen topic for content generation: '{selected_topic}'")
    else:
        print("No trending topics found.")

import os
from openai import OpenAI # Assuming OpenAI API client is updated for GPT-5
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

# Set your API key
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY_2025")

def generate_blog_post(topic, serp_snippets, model_name="gpt-5"):
    """
    Generates a blog post using GPT-5, informed by SERP data.
    Uses LangChain for structured prompting.
    """
    llm = ChatOpenAI(api_key=OPENAI_API_KEY, model=model_name, temperature=0.7)

    # Contextualize the prompt with SERP insights
    serp_context = "\n".join([f"- Title: {s['title']}\n  Snippet: {s['snippet']}" for s in serp_snippets[:5]])
    
    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", 
             f"You are an expert content writer for a technical blog focused on AI, automation, and monetization for solo creators. "
             f"Your goal is to write a comprehensive, engaging, and SEO-optimized blog post about '{topic}'. "
             f"Structure the post with clear headings (H2, H3), an engaging introduction, and a strong conclusion. "
             f"Include practical advice, future predictions, and focus on the benefits for solo creators. "
             f"Incorporate insights from the following top search result snippets to ensure comprehensiveness and competitive edge:\n{serp_context}"),
            ("user", 
             f"Draft a full blog post on the topic: '{topic}'. "
             f"Ensure it's at least 1500 words, uses markdown formatting for headings and lists, and is ready for publication. "
             f"Focus on practical implementation and monetization strategies. "
             f"Include sections on: 'Key AI Tools (2025)', 'Building Your Workflow', 'Monetization Opportunities', 'Ethical Considerations'."
            )
        ]
    )
    
    chain = prompt | llm
    response = chain.invoke({"topic": topic, "serp_snippets": serp_snippets})
    return response.content

if __name__ == "__main__":
    print("\n--- Step 2: AI-Powered Content Drafting ---")
    # Using the topic and SERP results from Step 1
    selected_topic = "AI Content Automation"
    mock_serp_results = [
        {"title": "The Rise of AI Content Automation in 2025 - FutureTech Blog", "link": "https://example.com/ai-content-automation-2025", "snippet": "Explore how AI is revolutionizing content creation, from idea generation to full automation. Discover advanced tools and strategies."},
        {"title": "Automating Your Content Strategy with AI - A 2025 Guide", "link": "https://anotherblog.org/ai-content-strategy", "snippet": "Learn the ins and outs of deploying AI for content automation. Practical tips for solo creators and small businesses."},
        {"title": "Is AI Content Automation the Future? - Robotics Daily", "link": "https://roboticsdaily.com/ai-content-future", "snippet": "Dive into the ethical and practical implications of AI-driven content. A must-read for automated content enthusiasts."},
        {"title": "Monetizing AI-Generated Content: A 2025 Blueprint", "link": "https://moneymaker.com/ai-content-monetization", "snippet": "Strategies for turning AI-created articles into revenue streams. Affiliate marketing, ad networks, and digital products."},
        {"title": "The Best AI Tools for Content Creation in 2025", "link": "https://aitools.net/best-content-ai", "snippet": "A comprehensive list of top AI-powered writing assistants, image generators, and SEO tools for content creators."}
    ]
    
    print(f"Generating content for: '{selected_topic}' using GPT-5...")
    generated_content = generate_blog_post(selected_topic, mock_serp_results)
    
    # Save a snippet of the generated content to a file for review
    with open("generated_blog_post_snippet.md", "w") as f:
        f.write(generated_content[:1500] + "\n...") # Write first 1500 chars + ellipsis
    
    print("\nContent generation complete. Snippet saved to 'generated_blog_post_snippet.md'.")
    print("\n--- Snippet of Generated Content ---")
    print(generated_content[:700] + "\n...") # Print first 700 chars

import os
from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods import posts
from wordpress_xmlrpc.methods.media import UploadFile
from wordpress_xmlrpc.methods import taxonomies
import base64

# WordPress credentials
WP_URL = os.getenv("WP_URL") # e.g., 'http://yourdomain.com/xmlrpc.php'
WP_USERNAME = os.getenv("WP_USERNAME")
WP_PASSWORD = os.getenv("WP_PASSWORD")

def publish_post_to_wordpress(title, content, tags=None, categories=None, status='publish', featured_image_path=None):
    """
    Publishes a post to WordPress via XML-RPC.
    """
    print(f"Attempting to connect to WordPress at {WP_URL}...")
    try:
        client = Client(WP_URL, WP_USERNAME, WP_PASSWORD)
        print("Connected to WordPress.")
    except Exception as e:
        print(f"Error connecting to WordPress: {e}")
        return None

    post = WordPressPost()
    post.title = title
    post.content = content
    post.post_status = status # 'publish', 'draft', 'pending'

    if tags:
        post.terms_names = {'post_tag': tags}
    if categories:
        post.terms_names = {'category': categories}

    print(f"Creating post: '{title}'...")
    post_id = client.call(posts.NewPost(post))
    print(f"Post created with ID: {post_id}")

    if featured_image_path:
        print(f"Uploading featured image from {featured_image_path}...")
        try:
            # Prepare image data
            data = {
                'name': os.path.basename(featured_image_path),
                'type': 'image/jpeg', # Adjust type based on your image
            }
            with open(featured_image_path, 'rb') as img:
                data['bits'] = base64.b64encode(img.read())

            result = client.call(UploadFile(data))
            attachment_id = result['id']
            print(f"Image uploaded with attachment ID: {attachment_id}")

            # Set as featured image
            post.id = post_id
            post.thumbnail = attachment_id
            client.call(posts.EditPost(post))
            print("Featured image set.")
        except Exception as e:
            print(f"Error uploading or setting featured image: {e}")

    return post_id

if __name__ == "__main__":
    print("\n--- Step 4: Automated Publishing ---")
    
    # Load the generated content (or use the full content variable from Step 2)
    with open("generated_blog_post_snippet.md", "r") as f:
        blog_content = f.read()

    # Define post details
    post_title = "AI Content Automation: Your 2025 Blueprint for Effortless Blogging and Monetization"
    post_tags = ["AI Automation 2025", "Blogging", "Monetization", "Solo Creator", "GPT-5"]
    post_categories = ["AI Blogging", "Automation", "Monetization"]
    
    # Mock a featured image path (you'd generate this in Step 3)
    mock_image_path = "mock_featured_image.jpg"
    # Create a dummy image file for demonstration
    from PIL import Image
    Image.new('RGB', (1024, 768), color = 'red').save(mock_image_path)
    print(f"Dummy image created at {mock_image_path}")

    # Publish the post
    published_post_id = publish_post_to_wordpress(
        title=post_title,
        content=blog_content,
        tags=post_tags,
        categories=post_categories,
        status='draft', # Start as draft for review!
        featured_image_path=mock_image_path
    )

    if published_post_id:
        print(f"\nSuccessfully initiated publishing process. Check your WordPress admin for post ID: {published_post_id}")
    else:
        print("\nFailed to publish post.")

    # Clean up dummy image
    if os.path.exists(mock_image_path):
        os.remove(mock_image_path)
        print(f"Cleaned up dummy image: {mock_image_path}")