import requests
import json
import pandas as pd
from pytrends.request import TrendReq # Assuming pytrends is updated for 2025

# --- Mock SerpAPI for illustrative purposes ---
# In a real scenario, you'd use your SerpApi key and proper endpoints.
def get_serp_data(query):
    # This is a placeholder. A real SerpAPI call would look different.
    # For example, using a library like 'google-search-results' for SerpApi.
    mock_data = {
        "search_parameters": {"q": query, "gl": "us"},
        "organic_results": [
            {"title": f"Top AI Trends in {query}", "link": "https://example.com/ai-trends"},
            {"title": f"Future of LLMs in {query}", "link": "https://example.com/llm-future"}
        ],
        "related_questions": ["How will AI data centers impact LLMs?", "What is Google's AI strategy for India?"]
    }
    return mock_data

# --- Google Trends ---
pytrends = TrendReq(hl='en-US', tz=360) # US timezone example

def get_google_trends(keywords):
    pytrends.build_payload(keywords, cat=0, timeframe='today 3-m', geo='')
    interest_df = pytrends.interest_over_time()
    if not interest_df.empty:
        return interest_df.drop(columns=['isPartial'], errors='ignore')
    return pd.DataFrame()

# Keywords related to the news
core_keywords = ['Google AI data centers', 'LLM infrastructure', 'AI compute India', 'AI growth US']

print("--- Google Trends Data ---")
trends_data = get_google_trends(core_keywords)
print(trends_data.tail()) # Show recent trends

print("\n--- SerpAPI-like Content Ideas (Conceptual) ---")
for kw in core_keywords:
    serp_results = get_serp_data(kw)
    print(f"\nIdeas for '{kw}':")
    for result in serp_results['organic_results']:
        print(f"- {result['title']} ({result['link']})")
    print(f"Related Questions: {', '.join(serp_results['related_questions'])}")

# Assuming you have an OpenAI-compatible API key for GPT-5
# from openai import OpenAI # Or similar client library for GPT-5 in 2025
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
# from langchain_community.chat_models import ChatOpenAI # Replace with GPT-5 model class

# --- Mock GPT-5 API client for illustrative purposes ---
class MockGPT5Client:
    def __init__(self, api_key):
        self.api_key = api_key
    
    def chat_completion(self, messages, model="gpt-5-turbo", temperature=0.7):
        # Simulate an LLM response based on the prompt
        prompt_text = messages[0]['content'] # Simplified: taking first message content
        if "Google AI data centers impact" in prompt_text:
            return "The new Google AI data centers will significantly boost LLM capabilities, leading to faster inference and potential for more complex models. This infrastructure expansion is critical for widespread AI adoption in various industries."
        elif "automation and monetization" in prompt_text:
            return "Solo creators can leverage automated content generation, build niche AI tools, and offer consulting services, all supported by the expanded AI infrastructure. Monetization comes via ads, affiliates, and premium content."
        return "This is a generated response based on your query regarding AI infrastructure."

# Initialize mock GPT-5 client
# client = OpenAI(api_key="YOUR_GPT5_API_KEY") # Replace with real client
client = MockGPT5Client(api_key="YOUR_MOCK_API_KEY")

def generate_article_section(topic_outline, tone="informative"):
    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are an expert technical blogger in 2025, specializing in AI and content automation. Write clear, energetic, and smart content."),
        ("user", f"Based on the following outline, write a detailed and engaging blog post section for solo content creators. Focus on practical implications and opportunities for automation and monetization.\n\nOutline: {topic_outline}\n\nTone: {tone}")
    ])
    
    # Use LangChain to chain the prompt, model call, and output parser
    # model = ChatOpenAI(model_name="gpt-5-turbo", temperature=0.7) # Replace with actual GPT-5 model
    # chain = prompt | model | StrOutputParser() # For real LangChain setup
    
    # Mocking the chain behavior for demonstration
    mock_response = client.chat_completion(messages=[{"role": "user", "content": prompt.format_messages()}])
    return mock_response

# Example outline based on our research
outline_part_1 = "How Google AI data centers impact LLMs and create new opportunities for solo creators."
outline_part_2 = "Practical steps for solo creators to leverage these advancements for content automation and monetization."

print("--- Generated Content Section 1 ---")
section_1 = generate_article_section(outline_part_1)
print(section_1)

print("\n--- Generated Content Section 2 ---")
section_2 = generate_article_section(outline_part_2)
print(section_2)

from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost
from wordpress_xmlrpc.methods.media import UploadFile
import os # For environment variables

# Configuration (use environment variables for security!)
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 post_to_wordpress(title, content, categories, tags, status='publish'):
    try:
        client = Client(WP_URL, WP_USERNAME, WP_PASSWORD)
        
        post = WordPressPost()
        post.title = title
        post.content = content
        post.post_status = status # 'publish', 'draft', etc.
        post.categories = categories
        post.tags = tags
        
        post_id = client.call(NewPost(post))
        print(f"Successfully posted '{title}' to WordPress. Post ID: {post_id}")
        return post_id

    except Exception as e:
        print(f"Error posting to WordPress: {e}")
        return None

# Example usage (using our generated content)
article_title = "Google's $3B AI Data Centers: Your Gateway to Automated AI Content"
article_content = f"Google's massive investment into AI data centers in the US and India is a pivotal moment...\n\n{section_1}\n\n{section_2}\n\nThis expansion means more robust AI infrastructure for developers and creators alike. It's time to build!"
article_categories = ['AI Trends', 'Automation', 'Monetization']
article_tags = ['Google AI 2025', 'LLMs 2025', 'Data Centers', 'Content Automation', 'Monetization Strategy']

# In a real setup, you'd uncomment the line below after setting up your WP credentials
# post_id = post_to_wordpress(article_title, article_content, article_categories, article_tags)

# For demonstration, we'll just print what would be posted
print("\n--- Mock WordPress Post Details ---")
print(f"Title: {article_title}")
print(f"Categories: {', '.join(article_categories)}")
print(f"Tags: {', '.join(article_tags)}")
print("Content (first 200 chars):", article_content[:200] + "...")
print("\n(To actually post, set up WP_URL, WP_USERNAME, WP_PASSWORD environment variables and uncomment the 'post_id = post_to_wordpress' line.)")

# Conceptual script for a mini-summarizer tool
# This would use a real LLM API or a Hugging Face pipeline
# from transformers import pipeline # If using Hugging Face locally or via inference API

def summarize_text_with_llm(text, max_length=150, min_length=50):
    # This is a conceptual function. In 2025, you might use a specific
    # 'text-summarization' pipeline from Hugging Face or a call to GPT-5.
    
    # For demonstration, a simple mock summary
    if "Google AI data centers" in text:
        return "Google's $3B data center investment in US & India fuels LLM growth, enabling faster, more powerful AI applications. Key for creators leveraging automation."
    return "This is a summarized version of the input text by an AI tool."

# Example news snippet (could be scraped from a tech news site)
news_snippet = """
BREAKING: Google announced today a fresh $3 billion investment dedicated to expanding its AI data center footprint across the United States and India. This strategic move aims to bolster the company's leading position in artificial intelligence by providing the necessary computational power for its rapidly evolving Large Language Models (LLMs) and other AI services. Industry analysts suggest this investment is a direct response to the escalating demand for generative AI capabilities and will significantly impact the speed and scale at which new AI applications can be deployed globally. The new facilities are expected to be operational by late 2025, creating thousands of jobs and driving innovation in both regions.
"""

print("--- AI-Powered News Summarizer (Conceptual) ---")
summary = summarize_text_with_llm(news_snippet)
print("Original text (first 100 chars):", news_snippet[:100] + "...")
print("Summary:", summary)