pip install google-search-results

import os
from serpapi import GoogleSearch

# Note: Replace 'YOUR_SERPAPI_KEY' with your actual SerpAPI key
# It's best practice to load API keys from environment variables.
SERPAPI_API_KEY = os.getenv("SERPAPI_API_KEY", "YOUR_SERPAPI_KEY")

def get_latest_ai_news_india(query, num_results=5):
    """
    Fetches the latest news articles for a given AI-related query in India.
    """
    params = {
        "engine": "google",
        "q": query,
        "location": "India", # Focus on India-specific results
        "tbm": "nws", # News results
        "num": num_results,
        "api_key": SERPAPI_API_KEY
    }

    search = GoogleSearch(params)
    results = search.get_dict()
    articles = []

    if "news_results" in results:
        for article in results["news_results"]:
            articles.append({
                "title": article.get("title"),
                "link": article.get("link"),
                "snippet": article.get("snippet"),
                "source": article.get("source"),
                "date": article.get("date")
            })
    return articles

if __name__ == "__main__":
    query_topic = "Indian LLM breakthroughs"
    news_items = get_latest_ai_news_india(query_topic)

    print(f"Latest news on '{query_topic}':\n")
    for i, item in enumerate(news_items):
        print(f"--- Article {i+1} ---")
        print(f"Title: {item['title']}")
        print(f"Source: {item['source']} ({item['date']})")
        print(f"Link: {item['link']}")
        print(f"Snippet: {item['snippet'][:150]}...") # Truncate for display
        print("-" * 20)

import openai
import os
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_openai import ChatOpenAI # For GPT-5 access

# Note: Ensure OPENAI_API_KEY is set in your environment variables
# For production, use a secure method to manage API keys.
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_OPENAI_API_KEY")

# Initialize GPT-5 via LangChain
llm = ChatOpenAI(model_name="gpt-5-turbo", temperature=0.7, openai_api_key=OPENAI_API_KEY) # Replace with actual GPT-5 model name

def generate_article_outline(topic, context_snippets):
    """
    Generates a detailed article outline based on a topic and context snippets.
    """
    template = """
    You are an expert technical blogger and AI researcher. Your goal is to create a detailed, engaging article outline
    on the topic: "{topic}".

    Here are some recent news snippets to provide context and specific angles to cover:
    {context}

    The article should be aimed at solo content creators, developers, and entrepreneurs interested in
    leveraging emerging AI trends. It should be informative, practical, and forward-looking.
    Include an engaging introduction, several main sections with sub-points, and a strong conclusion.
    Suggest a compelling title.

    Article Outline for: "{topic}"
    """
    prompt = PromptTemplate(template=template, input_variables=["topic", "context"])
    llm_chain = LLMChain(prompt=prompt, llm=llm)

    context_str = "\n".join([f"- {s['title']}: {s['snippet']}" for s in context_snippets])

    response = llm_chain.run(topic=topic, context=context_str)
    return response

if __name__ == "__main__":
    # Using the news items from the SerpAPI example
    sample_context = [
        {"title": "IIT-Delhi Researchers Unveil 'BharatGPT': A Multilingual LLM for India", "snippet": "IIT-Delhi's AI lab announced BharatGPT, a new multilingual large language model trained on Indian languages and dialects. This breakthrough marks a significant step..."},
        {"title": "The Rise of Indian AI Ethics Councils and Their Impact on LLM Development", "snippet": "As India's LLM research accelerates, so does the focus on ethical AI. New councils and regulatory frameworks are shaping how these powerful models are developed..."},
        {"title": "Understanding 'Hinglish' in AI: Why Indian LLMs Need Localized Data", "snippet": "The unique linguistic landscape of India, particularly the blend of Hindi and English ('Hinglish'), presents both challenges and opportunities for LLM training..."}
    ]
    
    topic_for_outline = "The Future of Multilingual LLMs in India: Opportunities for Content Creators"
    outline = generate_article_outline(topic_for_outline, sample_context)
    print(outline)

pip install python-wordpress-xmlrpc

import os
from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost, EditPost
from wordpress_xmlrpc.methods.media import UploadFile
import mimetypes

# Note: Replace with your WordPress URL, username, and password.
# Use environment variables for production secrets.
WP_URL = os.getenv("WP_URL", "https://yourblog.com/xmlrpc.php")
WP_USERNAME = os.getenv("WP_USERNAME", "your_api_user")
WP_PASSWORD = os.getenv("WP_PASSWORD", "your_api_password")

def publish_to_wordpress(title, content, categories=None, tags=None, status='publish', image_path=None):
    """
    Publishes a new post to WordPress using XML-RPC.
    Optionally uploads a featured image.
    """
    try:
        client = Client(WP_URL, WP_USERNAME, WP_PASSWORD)
    except Exception as e:
        print(f"Error connecting to WordPress: {e}")
        return None

    post = WordPressPost()
    post.title = title
    post.content = content
    post.post_status = status # 'publish', 'draft', 'pending'

    if categories:
        post.terms_names = {'category': categories}
    if tags:
        post.terms_names['post_tag'] = tags

    # Optional: Upload featured image
    if image_path and os.path.exists(image_path):
        data = {
            'name': os.path.basename(image_path),
            'type': mimetypes.guess_type(image_path)[0],
        }
        with open(image_path, 'rb') as img:
            data['bits'] = img.read()
        
        try:
            response = client.call(UploadFile(data))
            post.thumbnail = response['id'] # Set featured image
            print(f"Uploaded image: {response['url']}")
        except Exception as e:
            print(f"Warning: Could not upload image {image_path}: {e}")

    try:
        post_id = client.call(NewPost(post))
        print(f"Successfully published post with ID: {post_id}")
        return post_id
    except Exception as e:
        print(f"Error publishing post: {e}")
        return None

if __name__ == "__main__":
    # Example content generated by GPT-5
    example_title = "Unlocking India's Linguistic Powerhouse: How Multilingual LLMs Reshape Content Creation"
    example_content = """
    <p>India's rapid ascent in the AI landscape is not just about raw numbers; it's deeply intertwined with the nation's incredible linguistic diversity. With predictions pointing to India contributing 30% of global LLM researchers by 2026, the focus on multilingual models is becoming paramount.</p>
    <h2>The BharatGPT Revolution</h2>
    <p>Projects like IIT-Delhi's BharatGPT exemplify this shift. By training LLMs on India's unique blend of languages and dialects, including phenomena like "Hinglish" (a mix of Hindi and English), these models are designed to understand and generate content that resonates locally...</p>
    <h3>Opportunities for Creators</h3>
    <p>For solo content creators and developers, this presents a goldmine. Imagine creating hyper-localized content, building tools that serve specific linguistic communities, or providing expert insights into these niche markets...</p>
    <p>...[rest of the article content]</p>
    """ # In a real scenario, this would come from your AI generation step

    post_categories = ['AI', 'Automation', 'LLM', 'India Tech']
    post_tags = ['India AI 2025', 'Multilingual LLM', 'Content Automation', 'WordPress Automation 2025']

    # For demonstration, let's assume you have an image file named 'ai_india.jpg' in your current directory
    # image_path_to_upload = 'ai_india.jpg' 
    # For now, let's keep it without an image to ensure the example runs without a local file.
    image_path_to_upload = None 

    # To publish a draft first:
    # post_id = publish_to_wordpress(example_title, example_content, post_categories, post_tags, status='draft', image_path=image_path_to_upload)

    # To publish directly:
    post_id = publish_to_wordpress(example_title, example_content, post_categories, post_tags, status='publish', image_path=image_path_to_upload)
    
    if post_id:
        print(f"Check your blog for the new post!")