# python_trends_analyzer.py
from pytrends.request import TrendReq
import json
import os
from serpapi import GoogleSearch

# --- Part 1: Google Trends for initial keyword discovery ---
def get_trending_topics(keyword='AI in enterprise', geo='US', timeframe='today 3-m'):
    """Fetches trending topics related to a keyword."""
    pytrends = TrendReq(hl='en-US', tz=360)
    pytrends.build_payload([keyword], cat=0, geo=geo, timeframe=timeframe, gprop='')
    related_queries = pytrends.related_queries()
    
    if related_queries[keyword].get('top'):
        print(f"Top related queries for '{keyword}':")
        for query in related_queries[keyword]['top'].head(5).itertuples():
            print(f"- {query.query} (Value: {query.value})")
        return [q.query for q in related_queries[keyword]['top'].head(5).itertuples()]
    else:
        print(f"No top related queries found for '{keyword}'.")
        return []

# --- Part 2: SerpAPI for in-depth competitor analysis ---
def get_serp_results(query):
    """Fetches Google search results for competitive analysis."""
    # Note: Replace with your actual SerpAPI key from environment variable
    # Learn more at: https://serpapi.com/
    api_key = os.getenv("SERPAPI_API_KEY") 
    if not api_key:
        print("SERPAPI_API_KEY environment variable not set. Skipping SerpAPI.")
        return []

    params = {
        "engine": "google",
        "q": query,
        "api_key": api_key,
        "num": 5 # Get top 5 results
    }
    search = GoogleSearch(params)
    results = search.get_dict()

    if "organic_results" in results:
        print(f"\nTop search results for '{query}':")
        for i, result in enumerate(results["organic_results"]):
            print(f"{i+1}. {result.get('title')}")
            print(f"   URL: {result.get('link')}")
            if 'snippet' in result:
                print(f"   Snippet: {result['snippet'][:100]}...") # Truncate snippet
        return results["organic_results"]
    else:
        print(f"No organic results found for '{query}'.")
        return []

if __name__ == "__main__":
    initial_keyword = "Gemini 3.0 enterprise use cases"
    trending_queries = get_trending_topics(initial_keyword)
    
    if trending_queries:
        # Pick the most relevant trending query for SERP analysis
        serp_query = trending_queries[0] 
        get_serp_results(serp_query)

# content_generator.py
import requests
import pandas as pd
# from openai import OpenAI # In 2025, assume a GPT-5 class or similar is available
# from langchain.chains import LLMChain
# from langchain.prompts import PromptTemplate

# Mock LLM and Chain for demonstration
class MockLLM:
    def __init__(self, model_name="gpt-5"):
        self.model_name = model_name

    def invoke(self, prompt):
        # Simulate LLM response based on prompt structure
        if "industry statistics" in prompt:
            return "Based on industry statistics: 75% of enterprises plan significant AI investments by 2026. Data security and integration are top concerns."
        if "top solutions" in prompt:
            return "Leading solutions in enterprise AI include Google Cloud's Vertex AI, Microsoft Azure AI, and custom solutions built with open-source frameworks."
        return "This is a placeholder for a detailed AI-generated response based on the provided data."

class MockLLMChain:
    def __init__(self, llm, prompt_template):
        self.llm = llm
        self.prompt_template = prompt_template

    def invoke(self, inputs):
        # Format the prompt using the template and inputs
        formatted_prompt = self.prompt_template.format(**inputs)
        return {"text": self.llm.invoke(formatted_prompt)}

# --- Data Fetching (example: industry stats) ---
def fetch_industry_data(api_url="https://api.example.com/industry_trends"):
    """Fetches mock industry trend data."""
    try:
        # In a real scenario, this would hit a legitimate API
        response = requests.get(api_url, timeout=5) 
        response.raise_for_status() # Raise an exception for HTTP errors
        data = response.json()
        return pd.DataFrame(data['trends'])
    except requests.exceptions.RequestException as e:
        print(f"Error fetching industry data: {e}. Using dummy data.")
        return pd.DataFrame({
            'year': [2024, 2025, 2026],
            'investment_growth_percent': [15, 22, 30],
            'adoption_rate_percent': [40, 55, 75],
            'top_concern': ['Data Security', 'Integration', 'Talent Gap']
        })

# --- Content Generation with LLM ---
def generate_niche_article(topic, raw_data_df):
    """Generates a detailed article using processed data and an LLM."""
    
    # Process data with pandas
    summary_stats = raw_data_df.describe().to_string()
    top_concerns = raw_data_df['top_concern'].value_counts().head(2).index.tolist()
    
    # Define prompt template for GPT-5
    # PromptTemplate is a LangChain concept, simplified here for illustration
    prompt_template = """
    You are an expert technical blogger specializing in enterprise AI solutions.
    Your task is to write a comprehensive blog post section about "{topic}" for CTOs and IT managers.
    
    Here's relevant industry statistics:
    {summary_stats}
    
    Key enterprise concerns identified: {top_concerns_str}
    
    Please elaborate on:
    1. The current state of enterprise AI adoption, referencing the provided statistics.
    2. The primary challenges (like {top_concerns_str[0]}) and how enterprise-grade LLMs address them.
    3. Key considerations for businesses evaluating enterprise AI solutions.
    
    Ensure the tone is analytical, practical, and forward-looking. Aim for about 300 words.
    """

    llm = MockLLM()
    # prompt = PromptTemplate(template=prompt_template, input_variables=["topic", "summary_stats", "top_concerns_str"])
    # chain = LLMChain(llm=llm, prompt=prompt)
    
    # Simulating LangChain's invoke
    inputs = {
        "topic": topic,
        "summary_stats": summary_stats,
        "top_concerns_str": ", ".join(top_concerns)
    }
    
    # In a real LangChain setup: response = chain.invoke(inputs)
    response = MockLLMChain(llm, prompt_template).invoke(inputs)
    
    return response['text']

if __name__ == "__main__":
    topic = "The Impact of Gemini 3.0's Enterprise Focus on Data Security"
    industry_data = fetch_industry_data(api_url="https://api.fictionaldata.com/enterprise-ai-trends")
    
    article_section = generate_niche_article(topic, industry_data)
    print("\n--- Generated Article Section ---")
    print(article_section)

# wordpress_publisher.py
from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost
import os

def publish_post_to_wordpress(title, content, categories, tags, status='publish'):
    """
    Publishes a new post to WordPress.
    
    Note: XML-RPC can be disabled on some modern WordPress setups due to security concerns.
    Ensure it's enabled or consider using the WordPress REST API for newer installations.
    Learn more: https://developer.wordpress.org/rest-api/
    """
    wp_url = os.getenv("WP_URL") # e.g., 'https://yourdomain.com/xmlrpc.php'
    wp_username = os.getenv("WP_USERNAME")
    wp_password = os.getenv("WP_PASSWORD")

    if not all([wp_url, wp_username, wp_password]):
        print("WordPress credentials (WP_URL, WP_USERNAME, WP_PASSWORD) not set as environment variables.")
        print("Skipping WordPress publishing.")
        return

    try:
        client = Client(wp_url, wp_username, wp_password)
        
        post = WordPressPost()
        post.title = title
        post.content = content
        post.post_status = status # 'publish', 'draft', 'pending'
        post.terms_names = {
            'category': categories,
            'post_tag': tags
        }
        
        post_id = client.call(NewPost(post))
        print(f"Successfully published post '{title}' with ID: {post_id}")
    except Exception as e:
        print(f"Error publishing to WordPress: {e}")

if __name__ == "__main__":
    sample_title = "Enterprise AI Integration: Overcoming Data Silos"
    sample_content = """
    The shift towards enterprise-grade AI, epitomized by Google's Gemini 3.0, highlights the critical need for seamless data integration. 
    Many organizations struggle with fragmented data across disparate systems, creating significant hurdles for effective AI deployment. 
    Overcoming these data silos is not merely a technical challenge but a strategic imperative... (rest of your AI-generated content)
    """
    sample_categories = ['AI Solutions', 'Data Management']
    sample_tags = ['Enterprise AI', 'Gemini 3.0', 'Data Integration', 'Automation 2025']

    publish_post_to_wordpress(sample_title, sample_content, sample_categories, sample_tags)