# python_script_1_research.py
import requests
import json
import pandas as pd # Ensure pandas is installed: pip install pandas

SERPAPI_API_KEY = "YOUR_SERPAPI_KEY" # Get one from https://serpapi.com/
GOOGLE_TRENDS_BASE_URL = "https://trends.google.com/trends/api/explore" # For illustrative purposes

def get_serp_results(query, lang_code='en', location='us'):
    """Fetches organic search results and related queries via SerpAPI."""
    params = {
        "q": query,
        "hl": lang_code, # Host language (interface language)
        "gl": location, # Geo-location
        "api_key": SERPAPI_API_KEY
    }
    response = requests.get("https://serpapi.com/search", params=params)
    response.raise_for_status()
    data = response.json()
    
    # Extract related searches and questions
    related_queries = [rq['query'] for rq in data.get('related_searches', []) if 'query' in rq]
    people_also_ask = [paa['question'] for paa in data.get('knowledge_graph', {}).get('people_also_ask', []) if 'question' in paa]

    print(f"Related Queries for '{query}' ({lang_code}): {related_queries[:5]}...")
    print(f"People Also Ask for '{query}' ({lang_code}): {people_also_ask[:3]}...")
    return data

def analyze_trends(keywords, lang_code='en', geo='IN'):
    """Simulates checking Google Trends data for keywords."""
    # Note: Google Trends API is complex and typically requires a lot of setup or scraping.
    # This is a simplified placeholder. For real automation, consider libraries like 'pytrends'
    # or a dedicated data provider.
    
    print(f"\nSimulating Google Trends for '{keywords}' in {geo} ({lang_code})...")
    # In a real scenario, you'd use a robust library or direct API integration.
    # Example: pytrends.request.TrendReq().build_payload(kw_list=[keyword], geo=geo, timeframe='today 12-m')
    
    trend_data = {
        "keyword": keywords,
        "search_interest": [f"{i*10 + 50}%" for i in range(len(keywords))], # Dummy data
        "lang": lang_code
    }
    df = pd.DataFrame(trend_data)
    print(df)
    return df

if __name__ == "__main__":
    target_languages = {
        "Hindi": "hi",
        "Bengali": "bn",
        "Marathi": "mr",
        "Tamil": "ta"
    }
    
    main_query = "solar panel installation" # Your core niche topic
    
    for lang_name, lang_code in target_languages.items():
        print(f"\n--- Researching in {lang_name} ({lang_code}) ---")
        serp_data = get_serp_results(main_query, lang_code=lang_code, location='in')
        
        # Now, take the related queries and check their trends/volume
        if 'related_searches' in serp_data:
            top_related_queries = [rq['query'] for rq in serp_data['related_searches'][:3]]
            analyze_trends(top_related_queries, lang_code=lang_code, geo='IN')

    print("\nResearch phase complete. Use findings to inform content strategy.")

# python_script_2_content_generation.py
import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Set your OpenAI API key from environment variables for security
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

def generate_multi_lingual_article_section(topic, language_code, language_name):
    """
    Generates a short, informative section for an article in the specified language.
    This simulates the type of task Sarvam AI would excel at, but uses GPT-5 for now.
    """
    llm = ChatOpenAI(model="gpt-5-turbo", temperature=0.7) # Assuming GPT-5-turbo is available in 2025
    
    # Prompt engineering is key for quality multi-lingual output
    prompt_template = ChatPromptTemplate.from_messages(
        [
            ("system", "You are an expert content writer specializing in clear, concise, and SEO-friendly articles. Your goal is to write a section of an article focusing on a specific topic for a {language_name} audience."),
            ("user", "Write a 200-word section for an article about '{topic}'. Ensure the language is natural and culturally appropriate for {language_name} ({language_code}) speakers. Focus on practical benefits and address common questions. Include a relevant subheading."),
        ]
    )
    
    output_parser = StrOutputParser()
    chain = prompt_template | llm | output_parser
    
    print(f"\nGenerating content for '{topic}' in {language_name} ({language_code})...")
    content = chain.invoke({"topic": topic, "language_code": language_code, "language_name": language_name})
    return content

if __name__ == "__main__":
    article_topic = "Benefits of Home Solar Panels"
    
    target_languages_details = {
        "hi": {"name": "Hindi", "keywords": ["सोलर पैनल के फायदे", "घरेलू सौर ऊर्जा"]},
        "bn": {"name": "Bengali", "keywords": ["সৌর প্যানেলের উপকারিতা", "বাড়ির জন্য সৌর শক্তি"]},
        # Add more languages as needed, correlating with Sarvam AI's 22 languages
        "ta": {"name": "Tamil", "keywords": ["சோலார் பேனல் நன்மைகள்", "வீட்டு சூரிய சக்தி"]},
    }
    
    generated_articles = {}
    for lang_code, details in target_languages_details.items():
        lang_name = details["name"]
        generated_section = generate_multi_lingual_article_section(article_topic, lang_code, lang_name)
        generated_articles[lang_code] = generated_section
        print(f"\n--- Generated {lang_name} Content Sample ({lang_code}) ---")
        print(generated_section[:200] + "...") # Print first 200 chars for brevity

    # Example of how you might save these for later publishing
    with open("generated_multi_lingual_content.json", "w", encoding="utf-8") as f:
        json.dump(generated_articles, f, ensure_ascii=False, indent=4)
    print("\nAll content sections generated and saved to 'generated_multi_lingual_content.json'.")

# python_script_3_auto_publish.py
import json
from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost, EditPost
from wordpress_xmlrpc.methods.options import GetOptions
# Ensure you install: pip install python-wordpress-xmlrpc

# --- WordPress Configuration ---
# You should get these from your WordPress dashboard or host.
# For security, use environment variables or a secure configuration method.
WP_URL = "https://yourblog.com/xmlrpc.php"
WP_USERNAME = "your_wordpress_username"
WP_PASSWORD = "your_wordpress_application_password" # Use an Application Password for security!

def publish_to_wordpress(title, content, language_code, status='publish', categories=None, tags=None):
    """Publishes a new post to WordPress."""
    try:
        client = Client(WP_URL, WP_USERNAME, WP_PASSWORD)
        
        # You might need to adjust based on your specific WP setup,
        # e.g., if you use language plugins that manage distinct posts per language.
        # For simplicity, we'll append language to title for unique identification.
        
        post = WordPressPost()
        post.title = f"{title} ({language_code.upper()})"
        post.content = content
        post.post_status = status
        post.terms_names = {
            'category': categories if categories else ['Uncategorized'],
            'post_tag': tags if tags else ['AI Content 2025', 'Automation']
        }
        
        new_post_id = client.call(NewPost(post))
        print(f"Successfully published post with ID: {new_post_id} for {language_code.upper()}")
        return new_post_id

    except Exception as e:
        print(f"Error publishing post for {language_code.upper()}: {e}")
        return None

if __name__ == "__main__":
    # Load the generated content
    try:
        with open("generated_multi_lingual_content.json", "r", encoding="utf-8") as f:
            generated_articles = json.load(f)
    except FileNotFoundError:
        print("Error: 'generated_multi_lingual_content.json' not found. Please run content generation first.")
        exit()

    base_title = "The Power of Solar Panels for Homes"
    
    for lang_code, content_section in generated_articles.items():
        publish_to_wordpress(
            title=base_title,
            content=content_section,
            language_code=lang_code,
            categories=['AI Blogging', 'Energy', 'Multi-Lingual Content'],
            tags=['solar panels', 'renewable energy', lang_code, 'automated content 2025']
        )
    print("\nPublishing process initiated for all generated articles.")