How to Get GPT to Write 30 Articles a Week—Without Even Opening WordPress

# topic_discovery.py
import requests
import json

# Note: Google Trends API often requires unofficial wrappers or specific libraries.
# For simplicity, let's use a hypothetical "trend_api" here or directly query SerpAPI.
# In a real scenario, you might use a library like 'pytrends' or a service like Semrush API.

SERPAPI_KEY = "YOUR_SERPAPI_KEY" # Replace with your actual SerpAPI key

def get_trending_topics(niche="content automation", country="US", limit=5):
    """
    Simulates getting trending topics. In a real app, this would hit a Trends API.
    For demonstration, we'll use SerpAPI to find popular searches.
    """
    params = {
        "api_key": SERPAPI_KEY,
        "engine": "google_trends",
        "q": niche,
        "data_type": "feeder", # To get trending searches
        "hl": "en",
        "geo": country
    }
    response = requests.get("https://serpapi.com/search", params=params)
    response.raise_for_status()
    data = response.json()

    trending_topics = []
    if "trends" in data:
        for trend in data["trends"][:limit]:
            trending_topics.append(trend["query"])
    return trending_topics

def get_serp_data(query, num_results=5):
    """Fetches top organic search results for a given query."""
    params = {
        "api_key": SERPAPI_KEY,
        "engine": "google",
        "q": query,
        "hl": "en",
        "gl": "us",
        "num": num_results # Number of organic results to fetch
    }
    response = requests.get("https://serpapi.com/search", params=params)
    response.raise_for_status()
    data = response.json()

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

if __name__ == "__main__":
    print("Discovering trending topics for 'AI blogging'...")
    topics = get_trending_topics(niche="AI blogging", limit=3)
    print(f"Discovered topics: {topics}\n")

    if topics:
        first_topic = topics[0]
        print(f"Fetching SERP data for: '{first_topic}'")
        serp_data = get_serp_data(first_topic)
        for i, result in enumerate(serp_data):
            print(f"  Result {i+1}: {result['title']} - {result['snippet'][:70]}...")

# article_generation.py
from openai import OpenAI # Assuming OpenAI's API client
import json

# Replace with your actual OpenAI API Key
OPENAI_API_KEY = "YOUR_OPENAI_API_KEY"

client = OpenAI(api_key=OPENAI_API_KEY)

def generate_article(topic, serp_data):
    """
    Generates a full article using GPT-5 based on a topic and SERP data.
    """
    serp_context = "\n".join([f"- Title: {r['title']}\n  Snippet: {r['snippet']}" for r in serp_data])

    prompt = f"""
    You are an expert technical blogger and content automation specialist.
    Your task is to write a comprehensive, engaging, and SEO-optimized blog post for a solo content creator or developer.

    **Topic**: "{topic}"

    **Context (based on top SERP results)**:
    {serp_context}

    **Instructions**:
    1.  Write a compelling introduction that hooks the reader.
    2.  Develop 3-4 main sections, each with a clear heading (H2).
    3.  Within each section, provide practical advice, code snippets (if applicable for Python/Bash, even if placeholder), and actionable insights.
    4.  Ensure the tone is clear, energetic, and smart, but avoid hype.
    5.  Conclude with a strong summary and a call to action.
    6.  The article should be at least 800 words.
    7.  Suggest 5 relevant SEO tags and a concise meta description at the very end, in a structured JSON format.

    Article Structure:
    # {topic}
    (Introduction)
    ## Section 1 Heading
    (Content)
    ## Section 2 Heading
    (Content)
    ...
    (Conclusion)

    ---JSON_META_DATA---
    {{
        "meta_description": "Concise summary for search engines.",
        "tags": ["tag1", "tag2", "tag3", "tag4", "tag5"]
    }}
    """

    try:
        response = client.chat.completions.create(
            model="gpt-4-turbo-2024-04-09", # Assuming GPT-5 or similar model by 2025, using current best for example
            messages=[
                {"role": "system", "content": "You are a helpful and expert AI assistant."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.7, # Adjust for creativity vs. focus
            max_tokens=2500 # Ensure enough tokens for a long article
        )
        full_content = response.choices[0].message.content
        
        # Extract meta data (simple parsing, robust regex/JSON parser for production)
        parts = full_content.split("---JSON_META_DATA---")
        article_body = parts[0].strip()
        
        meta_data = {}
        if len(parts) > 1:
            try:
                meta_data_str = parts[1].strip()
                meta_data = json.loads(meta_data_str)
            except json.JSONDecodeError:
                print("Warning: Could not parse meta data JSON.")

        return article_body, meta_data

    except Exception as e:
        print(f"Error generating article: {e}")
        return None, {}

if __name__ == "__main__":
    # Example SERP data (from previous step)
    sample_serp_data = [
        {"title": "The Future of Content: GPT-5 and Your Strategy", "snippet": "Explore how GPT-5 will revolutionize content creation..."},
        {"title": "Mastering GPT-5 for SEO: A 2025 Guide", "snippet": "Learn to leverage GPT-5 for high-ranking content..."},
    ]
    
    topic_to_write = "GPT-5 Content Strategy: Building Your Autonomous Blog in 2025"
    print(f"Generating article for: '{topic_to_write}'...")
    
    article_content, article_meta = generate_article(topic_to_write, sample_serp_data)
    
    if article_content:
        print("\n--- Generated Article Excerpt ---")
        print(article_content[:500] + "...\n") # Print first 500 chars
        print(f"Meta Description: {article_meta.get('meta_description')}")
        print(f"Tags: {', '.join(article_meta.get('tags', []))}")
    else:
        print("Article generation failed.")

# wordpress_publisher.py
import xmlrpc.client
import ssl

# Your WordPress details
WP_URL = "https://your-domain.com/xmlrpc.php" # Adjust for your site
WP_USERNAME = "your_wordpress_username"
WP_PASSWORD = "your_wordpress_app_password" # Use an Application Password for better security

def publish_article_to_wordpress(title, content, categories=None, tags=None, status="publish"):
    """
    Publishes an article to WordPress using the XML-RPC API.
    """
    if categories is None:
        categories = ["Uncategorized"]
    if tags is None:
        tags = []

    # Disable SSL certificate verification for local development or if issues occur.
    # NOT RECOMMENDED FOR PRODUCTION. Always use proper SSL verification.
    # context = ssl._create_unverified_context()
    # server = xmlrpc.client.ServerProxy(WP_URL, context=context)

    # Recommended for production with proper SSL
    server = xmlrpc.client.ServerProxy(WP_URL)

    post = {
        'post_type': 'post',
        'post_status': status,
        'post_title': title,
        'post_content': content,
        'post_date_gmt': xmlrpc.client.DateTime(), # Current time in GMT
        'post_format': 'standard',
        'mt_keywords': tags, # For tags
        'mt_allow_comments': 1,
        'mt_allow_pings': 1,
    }

    # Add categories (needs to be an array of category names)
    # Note: 'wp_term_taxonomy' is the modern way but 'mt_categories' might also work
    post['mt_categories'] = categories
    # You might need to convert Markdown to HTML if your WordPress doesn't do it automatically,
    # or instruct GPT to output HTML directly. WordPress often handles basic Markdown.

    try:
        # The 'newPost' method requires blog ID, username, password, and the content dictionary
        post_id = server.wp.newPost(
            0, # Blog ID (0 for single site, or specific ID for multisite)
            WP_USERNAME,
            WP_PASSWORD,
            post,
            True # Publish immediately
        )
        print(f"Successfully published article! Post ID: {post_id}")
        return post_id
    except xmlrpc.client.Fault as err:
        print(f"A fault occurred: {err.faultCode}, {err.faultString}")
        return None
    except Exception as e:
        print(f"An error occurred: {e}")
        return None

if __name__ == "__main__":
    # Example article content and metadata (from article_generation.py)
    example_title = "GPT-5 Content Strategy: Building Your Autonomous Blog in 2025"
    example_content = """
    # GPT-5 Content Strategy: Building Your Autonomous Blog in 2025

    The digital landscape is constantly evolving, but few shifts have been as seismic as the advent of advanced AI models like GPT-5. For solo content creators, ambitious bloggers, and savvy developers, this isn't just a technological marvel; it's a paradigm shift in how we approach content production.

    ## The Dawn of Autonomous Content Creation

    (Full article content here...)

    This is just a snippet for demonstration. The actual generated article would be much longer.
    """
    example_categories = ["AI Blogging", "Automation"]
    example_tags = ["GPT-5", "Content Creation", "2025", "WordPress", "Automation"]

    print(f"Attempting to publish article: '{example_title}'...")
    published_id = publish_article_to_wordpress(
        example_title,
        example_content,
        categories=example_categories,
        tags=example_tags
    )
    if published_id:
        print(f"Check your WordPress site for post ID: {published_id}")

# main_automation_script.py (Conceptual Outline)
import pandas as pd
from datetime import datetime
import time

# Import functions from previous files
from topic_discovery import get_trending_topics, get_serp_data
from article_generation import generate_article
from wordpress_publisher import publish_article_to_wordpress

def main_automation_loop():
    print(f"Starting content automation run at {datetime.now()}")
    
    # 1. Discover Topics
    topics_to_process = get_trending_topics(niche="content automation", limit=10)
    print(f"Discovered {len(topics_to_process)} new topics.")

    for topic in topics_to_process:
        print(f"\nProcessing topic: '{topic}'")
        
        # 2. Get SERP Data for Context
        serp_results = get_serp_data(topic, num_results=7) # Get more context for better articles
        if not serp_results:
            print(f"Skipping '{topic}' due to no SERP data.")
            continue

        # 3. Generate Article
        article_body, article_meta = generate_article(topic, serp_results)
        
        if article_body and article_meta:
            title = topic # Or refine title based on meta
            meta_description = article_meta.get('meta_description', 'Default meta description.')
            tags = article_meta.get('tags', [])
            categories = ["AI Blogging", "Automation"] # Default categories

            print(f"Generated article for '{title}'. Length: {len(article_body)} chars.")
            # Note: Here you might save to a database or file for review
            # For direct publish, proceed:

            # 4. Publish to WordPress
            published_id = publish_article_to_wordpress(
                title,
                article_body,
                categories=categories,
                tags=tags
            )
            if published_id:
                print(f"Successfully published '{title}' (ID: {published_id}).")
            else:
                print(f"Failed to publish '{title}'.")
        else:
            print(f"Could not generate full article for '{topic}'.")
        
        time.sleep(5) # Be kind to APIs, avoid hitting rate limits too fast

    print(f"Content automation run finished at {datetime.now()}")

if __name__ == "__main__":
    main_automation_loop()
    # To run daily: set up a cron job:
    # 0 6 * * * /usr/bin/python3 /path/to/your/main_automation_script.py >> /path/to/automation.log 2>&1