import requests
import json
import os

# Using a hypothetical SERP API key from environment variables
SERP_API_KEY = os.getenv('SERP_API_KEY', 'YOUR_ACTUAL_SERP_API_KEY')
BASE_URL = "https://api.serpapi.com/search"

def get_related_searches(query):
    params = {
        'api_key': SERP_API_KEY,
        'q': query,
        'engine': 'google',
        'hl': 'en',
        'gl': 'us'
    }
    try:
        response = requests.get(BASE_URL, params=params)
        response.raise_for_status() # Raise an exception for HTTP errors
        data = response.json()

        related_queries = []
        if 'related_searches' in data:
            for item in data['related_searches']:
                related_queries.append(item.get('query'))
        
        # Also check 'people_also_ask' for content ideas
        people_also_ask = []
        if 'answer_box' in data and 'people_also_ask' in data['answer_box']:
             for item in data['answer_box']['people_also_ask']:
                 people_also_ask.append(item.get('question'))

        return related_queries, people_also_ask

    except requests.exceptions.RequestException as e:
        print(f"Error fetching data: {e}")
        return [], []

if __name__ == "__main__":
    main_query = "Meta Llama military helmets"
    related, paas = get_related_searches(main_query)

    print(f"Related Searches for '{main_query}':")
    for q in related:
        print(f"- {q}")
    
    print("\nPeople Also Ask:")
    for p in paas:
        print(f"- {p}")

import requests
import json
import os

# Assume GPT-5 API endpoint and key are available
GPT5_API_URL = "https://api.openai.com/v1/engines/gpt-5/completions" # Placeholder for GPT-5
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY', 'YOUR_ACTUAL_OPENAI_KEY_HERE')

def generate_article_draft(topic, keywords, length_words=1000):
    prompt = f"""
    Write a comprehensive, engaging, and informative blog post about "{topic}".
    Incorporate the following keywords naturally: {', '.join(keywords)}.
    Discuss the implications, potential benefits, and challenges of this technology.
    Aim for a detailed structure with an introduction, several body paragraphs with subheadings, and a conclusion.
    The tone should be smart, clear, and balanced.
    Target word count: approximately {length_words} words.
    """
    
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {OPENAI_API_KEY}"
    }
    
    data = {
        "prompt": prompt,
        "max_tokens": int(length_words * 1.5), # Allow some buffer
        "n": 1,
        "stop": None,
        "temperature": 0.7,
        "model": "gpt-5" # Or specific model like 'gpt-5-turbo-instruct'
    }

    try:
        response = requests.post(GPT5_API_URL, headers=headers, json=data)
        response.raise_for_status()
        result = response.json()
        return result['choices'][0]['text'].strip()
    except requests.exceptions.RequestException as e:
        print(f"Error calling GPT-5 API: {e}")
        return None

if __name__ == "__main__":
    topic = "Meta's Llama in Defense: AI Helmets for US Military by 2026"
    keywords = ["AI military helmets", "Llama defense applications", "situational awareness", "future warfare ethics", "Project Iron Man 2025"]
    
    article_content = generate_article_draft(topic, keywords, length_words=1200)
    
    if article_content:
        print("--- Generated Article Draft ---")
        print(article_content[:500] + "...\n") # Print first 500 chars for brevity
        print("Consider reviewing and fact-checking this draft thoroughly.")
    else:
        print("Failed to generate article.")

from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost
from wordpress_xmlrpc.methods.users import GetUserInfo
import os

# Your WordPress credentials and URL
WP_URL = os.getenv('WP_URL', 'https://yourwebsite.com/xmlrpc.php')
WP_USERNAME = os.getenv('WP_USERNAME', 'your_wordpress_username')
WP_PASSWORD = os.getenv('WP_PASSWORD', 'your_wordpress_password')

def publish_post_to_wordpress(title, content, categories, tags, status='publish'):
    try:
        client = Client(WP_URL, WP_USERNAME, WP_PASSWORD)
        
        # Verify connection (optional but good for debugging)
        user = client.call(GetUserInfo())
        print(f"Connected to WordPress as: {user.display_name}")

        post = WordPressPost()
        post.title = title
        post.content = content
        post.post_status = status # 'publish', 'draft', 'pending'
        post.categories = categories
        post.tags = tags
        
        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 to WordPress: {e}")
        return None

if __name__ == "__main__":
    # Example usage with content from Step 2
    article_title = "The Future of Combat: Meta's Llama-Powered AI Helmets"
    article_draft = """
    # The Future of Combat: Meta's Llama-Powered AI Helmets

    The convergence of artificial intelligence and military technology is no longer the stuff of science fiction. In 2025, reports suggest a significant leap forward with the potential deployment of AI-powered helmets for US military personnel by 2026, leveraging advanced large language models like Meta's Llama. These innovative 'smart helmets' are poised to revolutionize battlefield situational awareness, soldier autonomy, and overall operational effectiveness.

    ## Enhancing Cognitive Overload in Combat

    Modern warfare is incredibly complex, with soldiers often overwhelmed by disparate data streams. Imagine an AI combat helmet that acts as an intelligent assistant, filtering noise, prioritizing threats, and delivering critical information in real-time. This could include visual overlays identifying friend or foe, acoustic alerts for incoming threats, or even real-time language translation. Such systems aim to mitigate cognitive overload, allowing soldiers to make faster, more informed decisions under extreme pressure.

    ## Llama's Role in Tactical Intelligence

    Meta's Llama, known for its powerful natural language understanding and generation capabilities, could be instrumental in processing vast amounts of unstructured battlefield data. From intercepted communications to sensor readings, a localized Llama model could synthesize this information into actionable intelligence, presented directly to the soldier's display. This moves beyond simple data display to genuine tactical intelligence, offering a profound advantage.

    ## Ethical Considerations and the Path Forward

    The deployment of AI in defense raises significant ethical questions. Who is accountable when AI systems make critical decisions? How do we ensure these systems adhere to international laws of armed conflict? These are vital discussions that must accompany technological advancement. However, the drive for technological superiority ensures that research and development in this domain will continue apace. The move towards AI military integration represents not just a technological shift but a profound rethinking of military strategy and human-machine teaming.

    """
    
    post_categories = ['AI', 'Military Technology', 'Future Tech', '2025']
    post_tags = ['Llama', 'Defense AI', 'Smart Helmets', 'US Military', 'AI Ethics']
    
    # You'll need to install 'python-wordpress-xmlrpc': pip install python-wordpress-xmlrpc
    # Ensure your WordPress site has XML-RPC enabled (often found under Settings -> Writing).
    
    post_id = publish_post_to_wordpress(
        title=article_title,
        content=article_draft,
        categories=post_categories,
        tags=post_tags,
        status='publish'
    )
    if post_id:
        print(f"Check your site at: {WP_URL.replace('/xmlrpc.php', '')}/?p={post_id}")