import requests
import pandas as pd
import json

# --- Simulated Google Trends API (replace with actual API if you have access) ---
def get_trending_searches(country_code='US'):
    # In a real scenario, you'd use Google Trends API or a similar service
    # For demonstration, we'll return mock data.
    mock_trends = {
        'US': ['AI content automation 2025', 'Grok 3 review', 'Monetize AI blog', 'Best LLM for solo creators'],
        'GB': ['New AI tools UK', 'Passive income AI 2025', 'Content strategy automation']
    }
    return mock_trends.get(country_code, [])

# --- Simulated SerpAPI or similar for competitive analysis ---
def get_serp_data(keyword):
    # In a real scenario, you'd use SerpAPI or BrightData/ScrapingBee
    # For demonstration, we'll return mock data.
    mock_serp = {
        'AI content automation 2025': {'top_results': ['Forbes: Future of AI', 'TechCrunch: AI Content', 'Blog: AI Automation Guide']},
        'Grok 3 review': {'top_results': ['Official Grok site', 'Early review blog', 'AI news site']}
    }
    return mock_serp.get(keyword, {'top_results': []})

# --- LLM API Call (Generic for Grok 3 or ChatGPT API) ---
def call_llm_api(prompt, model="grok-3-turbo", max_tokens=300):
    # Replace with actual API endpoint and key for xAI Grok 3 or OpenAI GPT-5/6
    # This is a placeholder structure.
    try:
        # Example using a requests call for a hypothetical API
        # response = requests.post(
        #     "https://api.xai.com/v1/chat/completions", # Or api.openai.com
        #     headers={"Authorization": f"Bearer YOUR_API_KEY"},
        #     json={"model": model, "messages": [{"role": "user", "content": prompt}], "max_tokens": max_tokens}
        # ).json()
        
        # Mocking LLM response for demonstration:
        if "long-tail keywords" in prompt:
            return {
                "choices": [{"message": {"content": 
                    "- Grok 3 vs GPT-5 API comparison for developers\n"
                    "- How to build automated content pipelines with xAI Grok 3\n"
                    "- Monetizing AI-generated content in 2025 with Grok\n"
                    "- Step-by-step guide: WordPress auto-posting with Python and LLMs\n"
                    "- Best practices for AI content quality control 2025"
                }}]
            }
        return {"choices": [{"message": {"content": "LLM response about " + prompt[:50] + "..."}}]}
    except Exception as e:
        print(f"Error calling LLM API: {e}")
        return None

# --- Workflow ---
if __name__ == "__main__":
    trends = get_trending_searches('US')
    print("--- Top Trending Searches (Simulated) ---")
    for trend in trends:
        print(f"- {trend}")
        serp_info = get_serp_data(trend)
        print(f"  Top SERP results: {', '.join(serp_info['top_results'])}")

    print("\n--- Generating Long-Tail Keywords with LLM ---")
    topic = trends[0] # Using the first trending topic as a base
    llm_prompt = f"Given the trending topic '{topic}', suggest 5 highly specific, long-tail keywords for a blog post aimed at solo content creators looking to monetize. Ensure they are relevant for 2025."
    
    llm_response = call_llm_api(llm_prompt)
    if llm_response and llm_response.get('choices'):
        print(llm_response['choices'][0]['message']['content'])

# Assuming 'call_llm_api' is defined as before, updated for content generation
def generate_article_draft(keyword, model="grok-3-turbo"):
    prompt = f"""
    You are an expert technical blogger specializing in AI automation and monetization for solo creators.
    Write a detailed, engaging blog post draft (approx. 800 words) about: "{keyword}".
    
    Structure the article with:
    1.  Catchy Introduction (hook the reader)
    2.  Section on the problem or opportunity
    3.  Practical solution/workflow using AI
    4.  Monetization angles (how solo creators make money)
    5.  Conclusion with a strong call to action.
    
    Ensure the tone is clear, energetic, smart, but never hyped. Use Markdown for headings and lists.
    Focus on practical advice, API usage, and real-world results. Do not include a table of contents.
    """
    
    # For a full article, you'd iterate with smaller chunks or increase max_tokens significantly
    # Note: Generating 800 words in one go might hit max_tokens limits depending on the model/API.
    # A more robust solution involves breaking it into sections and combining.
    
    # Mocking for brevity:
    if "Grok 3 vs GPT-5 API" in keyword:
        return {
            "choices": [{"message": {"content": 
                "## Grok 3 vs GPT-5 API: A Developer's Showdown for Content Automation\n\n"
                "In 2025, the API landscape for LLMs is more competitive than ever. For developers building content automation tools, the choice between xAI's Grok 3 and OpenAI's GPT-5 (or its successor) is critical...\n\n"
                "### Performance and Latency\n"
                "Grok 3, with its rumored optimized architecture for real-time data, could offer lower latency for time-sensitive content generation...\n\n"
                "### Feature Set for Monetization\n"
                "GPT-5's fine-tuning capabilities remain unparalleled for highly niche content, while Grok 3's real-time information access could unlock new revenue streams from trend-jacking...\n\n"
                "This article continues with details on API access, cost analysis, and specific code examples for integrating both into a Python pipeline."
            }}]
        }
    return None

if __name__ == "__main__":
    target_keyword = "- Grok 3 vs GPT-5 API comparison for developers" # From our earlier output
    print(f"\n--- Generating Article Draft for: {target_keyword} ---")
    article_response = generate_article_draft(target_keyword, model="grok-3-turbo") # Or "gpt-5-turbo"
    if article_response and article_response.get('choices'):
        print(article_response['choices'][0]['message']['content'])

from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost
from wordpress_xmlrpc.methods.media import UploadFile
from wordpress_xmlrpc.compat import xmlrpc_client
import os

# --- WordPress Configuration ---
# Replace with your actual WordPress details
WP_URL = 'https://yourdomain.com/xmlrpc.php'
WP_USERNAME = 'your_wp_username'
WP_PASSWORD = 'your_wp_password'

# --- LLM-Generated Content Placeholder ---
# In a real scenario, this would come from your generate_article_draft function
article_title = "Monetizing AI-Generated Content in 2025: A Solo Creator's Guide"
article_content = """
    ## Monetizing AI-Generated Content in 2025: A Solo Creator's Guide

    The dream of passive income is closer than ever thanks to advancements in AI content generation. As a solo creator, you can leverage tools like Grok 3 and GPT-5 to build entire content empires.

    ### Affiliate Marketing Niches
    Identify high-commission niches. Use AI to generate product reviews, comparison guides, and FAQs. Grok 3's real-time understanding can help you jump on emerging product trends faster.

    ### Display Ads and Programmatic Revenue
    High-volume, evergreen content can drive significant traffic, and thus, ad revenue. Automate content for informational queries and build out topical authority clusters.

    ### Selling Digital Products
    AI can help draft eBooks, online course material, and even simple software. Use Grok 3 or ChatGPT to outline, write, and refine your product descriptions and sales copy.

    By embracing these strategies, you can transform AI into a powerful engine for monetization. Start small, experiment, and scale up your automated workflows.
    """

# --- Optional: Generate a featured image with an AI image generator API (e.g., DALL-E, Midjourney, Stable Diffusion) ---
# For demonstration, we'll use a placeholder image path.
def generate_image_with_ai(prompt, filename="featured_image.png"):
    # In a real scenario, call an AI image generation API
    # For now, let's just create a dummy file.
    with open(filename, 'w') as f:
        f.write("DUMMY IMAGE CONTENT") # This won't be a valid image but serves the purpose for file existence
    return filename

# --- Main Auto-Posting Function ---
def auto_post_to_wordpress(title, content, categories=[], tags=[], featured_image_path=None):
    try:
        client = Client(WP_URL, WP_USERNAME, WP_PASSWORD)

        post = WordPressPost()
        post.title = title
        post.content = content
        post.terms_names = {
            'category': categories,
            'post_tag': tags
        }
        post.post_status = 'publish' # Use 'draft' for review before publishing

        # Upload featured image if provided
        if featured_image_path and os.path.exists(featured_image_path):
            data = {'name': os.path.basename(featured_image_path), 'type': 'image/png'}
            with open(featured_image_path, 'rb') as img:
                data['bits'] = xmlrpc_client.Binary(img.read())
            
            response = client.call(UploadFile(data))
            attachment_id = response['id']
            post.thumbnail = attachment_id # Set the featured image

        post_id = client.call(NewPost(post))
        print(f"Successfully posted! New post ID: {post_id}")
        print(f"View post at: {WP_URL.replace('/xmlrpc.php', '')}/?p={post_id}") # Basic URL construction

    except Exception as e:
        print(f"Error posting to WordPress: {e}")

if __name__ == "__main__":
    # Generate a dummy image file
    image_file = generate_image_with_ai("AI content automation graphic", "ai_automation_graphic.png")

    auto_post_to_wordpress(
        title=article_title,
        content=article_content,
        categories=['Monetization', 'AI Blogging', 'Automation', '2025'],
        tags=['AI', 'Grok', 'ChatGPT', 'Passive Income', 'Content Creation'],
        featured_image_path=image_file
    )
    
    # Clean up dummy image
    if os.path.exists(image_file):
        os.remove(image_file)