Build an AI Blogging Machine in 3 Steps Scrape, Prompt, Publish

import requests
import pandas as pd
import json # For handling API responses

# --- Configuration (replace with your actual API keys) ---
SERPAPI_KEY = "YOUR_SERPAPI_KEY"
# Google Trends doesn't have a direct public API for specific trends,
# but you can use libraries like 'pytrends' or integrate with other trend data providers.
# For simplicity, we'll simulate a 'trending topic' for the SERPAPI example.

def get_trending_topic_data(topic_query):
    """
    Simulates getting trending topic insights. In a real scenario, this would
    integrate with Google Trends API, Twitter Trends, or similar.
    """
    print(f"Simulating trend data for: '{topic_query}'...")
    # Real integration would involve pytrends or a dedicated API.
    # Example: pytrends.request.TrendReq().build_payload(kw_list=[topic_query])
    return {
        "topic": topic_query,
        "search_volume_trend": "rising",
        "competition_level": "medium"
    }

def get_serp_data(query):
    """
    Fetches SERP data using SerpAPI for a given query.
    """
    print(f"Fetching SERP data for: '{query}'...")
    url = "https://serpapi.com/search"
    params = {
        "api_key": SERPAPI_KEY,
        "engine": "google",
        "q": query,
        "gl": "us", # Target country
        "hl": "en"  # Target language
    }
    try:
        response = requests.get(url, params=params)
        response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)
        data = response.json()
        
        results = []
        if "organic_results" in data:
            for item in data["organic_results"][:5]: # Get top 5
                results.append({
                    "title": item.get("title"),
                    "link": item.get("link"),
                    "snippet": item.get("snippet")
                })
        
        related_searches = [s.get("query") for s in data.get("related_searches", [])[:3]]
        
        return {
            "query": query,
            "top_results": results,
            "related_searches": related_searches
        }
    except requests.exceptions.RequestException as e:
        print(f"Error fetching SERP data: {e}")
        return None

if __name__ == "__main__":
    target_topic = "AI content automation 2025" # Example topic
    
    # Step 1.1: Get trending topic insights (simulated)
    trend_info = get_trending_topic_data(target_topic)
    print("\n--- Trending Topic Insight ---")
    print(json.dumps(trend_info, indent=2))

    # Step 1.2: Get SERP data for the topic
    serp_info = get_serp_data(target_topic)
    if serp_info:
        print("\n--- SERP Data for Query ---")
        print(json.dumps(serp_info, indent=2))

        # You can now process this data with pandas for deeper analysis
        # For example, to extract common keywords from snippets
        df_results = pd.DataFrame(serp_info["top_results"])
        print("\nTop Organic Results DataFrame:")
        print(df_results.head())

import os
from openai import OpenAI # Assuming OpenAI API for GPT-5

# --- Configuration ---
# Set your OpenAI API key as an environment variable or directly here
# os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY" # Recommended
client = OpenAI()

def generate_blog_post_draft(topic, serp_insights, related_searches):
    """
    Generates a blog post draft using GPT-5, leveraging scraped SERP data.
    """
    # Structure the prompt with insights from Step 1
    prompt = f"""
    You are an expert content strategist and a brilliant blogger. Your task is to write a comprehensive, engaging, and SEO-optimized blog post on the topic: "{topic}".

    Here are some key insights from top-ranking articles and related searches (USE THIS INFORMATION TO INFORM YOUR ARTICLE STRUCTURE AND CONTENT, BUT DO NOT PLAGIARIZE):

    Top 5 Search Results Snippets:
    {chr(10).join([f"- Title: {res['title']}\n  Snippet: {res['snippet']}" for res in serp_insights['top_results']])}

    Related Searches (consider these as sub-topics or reader interests):
    {chr(10).join([f"- {s}" for s in related_searches])}

    Your article should be:
    1.  **Original and insightful**, not just a rehash of existing content.
    2.  Around 800-1200 words.
    3.  Structured with an introduction, 3-5 main sections (use H2 headings), and a conclusion.
    4.  Include actionable advice or examples where appropriate.
    5.  Optimized for readability and engagement.
    6.  Focus on the *practical application* of AI for solo content creators/bloggers in 2025.

    Please provide the full blog post content in Markdown format, starting with the main heading.
    """

    print(f"Generating content for topic: '{topic}' with GPT-5...")
    try:
        response = client.chat.completions.create(
            model="gpt-5-turbo", # Assuming GPT-5 is 'gpt-5-turbo' or similar in 2025
            messages=[
                {"role": "system", "content": "You are a helpful assistant that writes comprehensive blog posts."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.7, # A bit creative, but still factual
            max_tokens=2000, # Adjust based on desired length
        )
        return response.choices[0].message.content
    except Exception as e:
        print(f"Error generating content: {e}")
        return "Content generation failed."

if __name__ == "__main__":
    # Use dummy data similar to what would come from Step 1's serp_info
    dummy_serp_info = {
      "query": "AI content automation 2025",
      "top_results": [
        {"title": "The Future of AI in Content Marketing by 2025 - Forbes", "link": "...", "snippet": "Experts predict AI will revolutionize content creation, personalization, and distribution by 2025."},
        {"title": "Automating Content: AI Tools & Strategies for 2025 - TechCrunch", "link": "...", "snippet": "Discover the latest AI-powered platforms helping businesses scale content production and optimize their digital presence."},
        {"title": "How AI Will Change Blogging in 2025 - HubSpot Blog", "link": "...", "snippet": "Explore the impact of artificial intelligence on blog content creation, from topic generation to full article drafting."}
      ],
      "related_searches": [
        "AI content tools 2025",
        "Future of content marketing AI",
        "AI content strategy"
      ]
    }
    
    topic_for_generation = "Build an AI-Powered Blog: Your 2025 Blueprint"
    generated_content = generate_blog_post_draft(
        topic_for_generation,
        dummy_serp_info,
        dummy_serp_info["related_searches"]
    )
    print("\n--- Generated Blog Post Draft (First 500 chars) ---")
    print(generated_content[:500])
    print("...")

    # Note: In a real workflow, you'd save this content to a file or database.
    # You might also use LangChain to add a 'fact-checking' or 'SEO optimization'
    # step by chaining additional AI calls or external tools.

from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost
from wordpress_xmlrpc.methods.media import UploadFile
from wordpress_xmlrpc.methods.taxonomies import GetTerms

# --- Configuration (replace with your actual WordPress details) ---
WP_URL = "https://yourblog.com/xmlrpc.php"
WP_USERNAME = "your_wordpress_username"
WP_PASSWORD = "your_wordpress_password"

def upload_image_to_wordpress(client, image_path, image_title):
    """
    Uploads an image to WordPress and returns its URL.
    """
    with open(image_path, 'rb') as img:
        data = {
            'name': os.path.basename(image_path),
            'type': 'image/jpeg',  # Or 'image/png', etc.
            'bits': img.read()
        }
    try:
        response = client.call(UploadFile(data))
        print(f"Image uploaded: {response['url']}")
        return response['url']
    except Exception as e:
        print(f"Error uploading image: {e}")
        return None

def publish_blog_post(title, content, categories, tags, image_url=None):
    """
    Publishes a blog post to WordPress.
    """
    client = Client(WP_URL, WP_USERNAME, WP_PASSWORD)
    post = WordPressPost()
    
    post.title = title
    post.content = content
    post.post_status = 'publish' # 'draft', 'pending', 'private'

    # Set categories
    # You might need to fetch available categories first via client.call(GetTerms('category'))
    post.terms_names = {
        'category': categories,
        'post_tag': tags
    }

    if image_url:
        post.thumbnail = image_url # Set featured image (requires media_id or URL depending on theme/plugin)
                                  # Note: For simple URL, your theme must support it.
                                  # For proper featured image, you'd get the media_id from UploadFile response.

    try:
        post_id = client.call(NewPost(post))
        print(f"Post '{title}' published successfully with ID: {post_id}")
        return post_id
    except Exception as e:
        print(f"Error publishing post: {e}")
        return None

if __name__ == "__main__":
    # Dummy content from Step 2
    post_title = "Building Your AI Blogging Machine: The 2025 Blueprint"
    post_content = """
    # Building Your AI Blogging Machine: The 2025 Blueprint

    Welcome to the future of blogging! In 2025, the landscape of content creation is no longer about grinding out articles manually. It's about smart automation, leveraging artificial intelligence to amplify your reach, streamline your workflow, and ultimately, supercharge your monetization efforts.

    ## The Power of Automation
    This machine automates content discovery, generation, and publishing...

    ## Monetization Potential
    By scaling your content, you open up new avenues for affiliate marketing, ad revenue, and lead generation...
    """
    post_categories = ["AI Blogging", "Automation"]
    post_tags = ["AI", "Blogging", "Automation", "2025", "GPT-5"]

    # Optional: Upload a placeholder image (ensure 'placeholder.jpg' exists in the same directory)
    # image_path = "placeholder.jpg" # Example image
    # try:
    #     client = Client(WP_URL, WP_USERNAME, WP_PASSWORD)
    #     uploaded_image_url = upload_image_to_wordpress(client, image_path, "AI Blogging Machine")
    # except Exception as e:
    #     print(f"Could not connect to WP for image upload: {e}")
    #     uploaded_image_url = None

    post_id = publish_blog_post(post_title, post_content, post_categories, post_tags, image_url=None) # Set to uploaded_image_url if successful