import requests
import json
import os

# Note: Replace with your actual SerpAPI key or similar search API
# For demonstration, we'll use a placeholder.
SERPAPI_KEY = os.getenv("SERPAPI_KEY", "YOUR_SERPAPI_KEY") 

def get_trending_insights(query):
    """
    Fetches trending news and related searches using a search API as a proxy for Google Trends.
    """
    url = "https://serpapi.com/search"
    params = {
        "engine": "google",
        "q": query,
        "api_key": SERPAPI_KEY,
        "gl": "us", # Target US market
        "hl": "en", # English language
        "tbm": "nws", # News results
        "num": "10" # Number of results
    }
    
    try:
        response = requests.get(url, params=params)
        response.raise_for_status() # Raise an exception for HTTP errors
        data = response.json()
        
        print(f"\n--- Trending Insights for '{query}' ---")
        if "news_results" in data:
            print("Top News Headlines:")
            for i, news in enumerate(data["news_results"][:3]): # Display top 3 headlines
                print(f"{i+1}. {news['title']} ({news['source']})")
        
        if "related_searches" in data:
            print("\nRelated Searches (Potential Keywords):")
            for i, rs in enumerate(data["related_searches"][:5]): # Display top 5 related searches
                print(f"- {rs['query']}")
        
        return data
    except requests.exceptions.RequestException as e:
        print(f"Error fetching data: {e}")
        return None

# Example usage:
trending_data = get_trending_insights("Google OpenAI LLM talent grab 2025")

# Assuming you have a GPT-5 API client setup
# In 2025, this might be a local endpoint or a cloud service like Google Cloud Vertex AI, OpenAI, etc.
# For simplicity, we'll represent it as a direct function call.

def generate_blog_outline(topic, keywords, target_audience="solo content creator"):
    """
    Generates a detailed blog post outline using GPT-5 based on a given topic and keywords.
    """
    prompt = f"""
    You are an expert content strategist for {target_audience}s.
    Generate a detailed, monetization-focused blog post outline (in Markdown) on the topic: '{topic}'.
    
    Incorporate the following keywords naturally: {', '.join(keywords)}.
    
    The post should aim to inform readers about the implications of this news and provide actionable strategies for content creation and monetization.
    Include sections for an introduction, key insights from the news, content strategy ideas, automation workflows, monetization opportunities, and a conclusion with a call to action.
    """
    
    print(f"\n--- Generating Blog Outline for '{topic}' with GPT-5 ---")
    # Simulate GPT-5 API call
    # In reality, you'd use a library like `openai.ChatCompletion.create` or `google.cloud.aiplatform.PredictionServiceClient`
    
    # Mocking GPT-5 response for demonstration
    mock_gpt_response = f"""
## **{topic}**: Your Content Goldmine

### 1. Introduction: The AI Talent Firestorm (500 words)
    - Hook: Google's $20M LLM talent grab – why it's bigger than just salaries.
    - Thesis: How this talent war impacts the future of AI and creates content opportunities for you.
    - Brief overview of what the reader will learn.

### 2. The Stakes: Why Google is Paying Big (700 words)
    - Deeper dive into the "why": Competitive advantage, innovation pace, securing foundational research.
    - Implications for the broader AI ecosystem (e.g., smaller labs, startups).
    - Keywords: `AI researcher salary 2025`, `OpenAI Google competition`, `LLM expert job market`.

### 3. Turning Trends into Content: Your Niche Strategy (800 words)
    - How to identify granular topics from broad trends.
    - Using SerpAPI/Google Trends for continuous monitoring.
    - Brainstorming content formats: deep dives, comparison articles, "how-to" guides (e.g., "How to use GPT-5 to analyze AI news").
    - Keywords: `AI content strategy`, `automated content pipeline`.

### 4. Automated Content Creation Workflows (1000 words)
    - **Data Ingestion**: Scraping news, academic papers, LinkedIn profiles (ethically!).
    - **Drafting with GPT-5 & Copilot**: From outlines to full drafts.
    - **Fact-Checking & Refinement with LangChain**: RAG for data accuracy.
    - **Publishing Automation**: Python + XML-RPC for WordPress.
    - Code snippet callouts for each step.

### 5. Monetization Beyond AdSense (800 words)
    - **Affiliate Marketing**: Linking to AI tools, courses, cloud services (e.g., specialized LLM platforms).
    - **Premium Content**: E-books on "Mastering AI Content Automation," advanced prompt engineering guides.
    - **Lead Generation**: Offering "AI Content Strategy Consulting" or custom bot development.
    - **Sponsored Content**: Partnerships with AI startups or data providers.

### 6. The Future is Automated: Conclusion & CTA (300 words)
    - Recap the power of automated, data-driven content.
    - Encourage readers to implement these strategies.
    - Call to action: "Subscribe for more AI automation tips" or "Download our free guide."
    """
    
    return mock_gpt_response

# Example usage:
topic = "Google's $20M AI Talent Grab: Content & Monetization Strategies"
keywords = ["AI researcher salary 2025", "OpenAI compensation packages", "LLM expert job market", "AI content strategy"]
outline = generate_blog_outline(topic, keywords)
print(outline)

# Example: Using GPT-5 to expand on an outline point
# Note: For robust RAG, you'd integrate a vector database and an actual LangChain pipeline.
# This is a simplified representation.

# Assuming your GPT-5 client and LangChain setup:
# from your_llm_api_client import GPT5Client
# from your_langchain_rag_system import LangChainRAG

# gpt5_client = GPT5Client(api_key="...")
# rag_system = LangChainRAG(vector_db_path="...")

def generate_section_content(section_title, outline_points, rag_query=None):
    """
    Generates content for a specific section using GPT-5, optionally enhanced by RAG.
    """
    prompt = f"""
    Write a detailed and engaging blog post section for '{section_title}'.
    Expand on these key points:
    {'- ' + '\\n- '.join(outline_points)}
    
    Ensure the tone is informative, energetic, and practical for solo content creators.
    """
    
    # If RAG query is provided, first retrieve context
    context = ""
    if rag_query:
        # Simulate RAG retrieval
        # context = rag_system.retrieve(rag_query)
        context = "Recent reports from Bloomberg indicate Google's $20M retention offers were for key researchers in the Gemini project, aimed at preventing defections to OpenAI's competitive projects."
        prompt = f"Given this context: '{context}', " + prompt
    
    print(f"\n--- Generating Content for: {section_title} ---")
    
    # Simulate GPT-5 generation
    mock_content = f"""
    ### {section_title}
    
    The rumor mill about Google's $20 million retention packages for top LLM researchers isn't just water cooler talk; it's a strategic maneuver in the high-stakes game of AI dominance. {context} These weren't just any researchers; we're talking about the architects behind foundational models like Gemini, pivotal to Google's next-gen AI capabilities.
    
    For a solo creator, this signals a gold rush. The "AI researcher salary 2025" and "LLM expert job market" are now prime keyword targets. As Google and OpenAI duke it out for talent, new innovations will accelerate, creating an endless stream of content opportunities. Think about it: every new paper, every talent acquisition, every product launch stemming from this talent war can be distilled into accessible, actionable content for your audience.
    
    Monetize this by affiliating with platforms that offer AI development courses, or tools that help *their* research.
    """
    return mock_content

# Example usage:
section_content = generate_section_content(
    "The Stakes: Why Google is Paying Big",
    [
        "Deeper dive into the 'why': Competitive advantage, innovation pace, securing foundational research.",
        "Implications for the broader AI ecosystem (e.g., smaller labs, startups).",
        "Keywords: `AI researcher salary 2025`, `OpenAI Google competition`, `LLM expert job market`."
    ],
    rag_query="Google $20M AI talent retention details"
)
print(section_content[:500] + "...") # Print first 500 chars for brevity

from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost
from markdown import markdown # For converting Markdown to HTML

# Note: Replace with your WordPress URL, username, and app password.
# It's crucial to use an "Application Password" for security, not your regular login password.
WORDPRESS_URL = "https://yourblog.com/xmlrpc.php"
WORDPRESS_USERNAME = "your_app_username"
WORDPRESS_APP_PASSWORD = "your_app_password"

def publish_to_wordpress(title, content_markdown, categories, tags, status="publish"):
    """
    Publishes a new post to WordPress using XML-RPC.
    """
    try:
        client = Client(WORDPRESS_URL, WORDPRESS_USERNAME, WORDPRESS_APP_PASSWORD)
        
        post = WordPressPost()
        post.title = title
        post.content = markdown(content_markdown) # Convert Markdown to HTML
        post.post_status = status # 'publish' or 'draft'
        post.categories = categories
        post.tags = tags
        
        # NewPost returns the ID of the new post
        post_id = client.call(NewPost(post))
        print(f"Successfully published post with ID: {post_id}")
        print(f"View your post at: {WORDPRESS_URL.replace('/xmlrpc.php', '')}/?p={post_id}")
        
        return post_id
    except Exception as e:
        print(f"Error publishing to WordPress: {e}")
        return None

# Example usage:
post_title = "The AI Talent War: Google, OpenAI, and Your Content Goldmine"
full_content_markdown = f"""
{outline}

{section_content}

---
**Disclaimer:** This post is for informational purposes. Verify all financial claims independently.
"""

# Convert tags and categories from the outline to list format for the function
post_categories = ["AI Blogging", "Automation", "Monetization"]
post_tags = ["AI 2025", "LLM", "Google", "OpenAI", "Content Automation", "Tech Trends"]

published_id = publish_to_wordpress(
    post_title,
    full_content_markdown,
    post_categories,
    post_tags,
    status="draft" # Start as draft for review
)