# Python for fetching Google Trends data (simplified example)
import requests
import json
from datetime import datetime, timedelta

def get_google_trends_data(keyword, geo='IN', timeframe='today 1-m'):
    """
    Fetches Google Trends data for a given keyword.
    Note: Real Google Trends API access might require more complex authentication
    or libraries like 'pytrends'. This is a conceptual example using a mock API or
    a service wrapper for demonstration.
    """
    print(f"Fetching trends for: '{keyword}' in {geo} for {timeframe}...")
    # In a real scenario, you'd use a library like pytrends or a specific API.
    # For demonstration, let's simulate an API call.
    mock_api_endpoint = "https://api.example.com/google_trends_proxy" # Placeholder
    params = {
        'keyword': keyword,
        'geo': geo,
        'timeframe': timeframe,
        'tz': '-330' # India Standard Time offset
    }
    try:
        response = requests.get(mock_api_endpoint, params=params)
        response.raise_for_status() # Raise an exception for HTTP errors
        data = response.json()
        return data.get('trends_data', [])
    except requests.exceptions.RequestException as e:
        print(f"Error fetching Google Trends data: {e}")
        return []

if __name__ == "__main__':
    # Example: Trends for 'smart home devices India' over the last month
    trends = get_google_trends_data('smart home devices India', geo='IN')
    if trends:
        print("\n--- Google Trends Data (Sample) ---")
        for entry in trends[:5]: # Show first 5 entries
            print(f"Date: {entry.get('date')}, Interest: {entry.get('value')}")
    else:
        print("No trends data received or an error occurred.")

    # You'd then use SerpAPI to analyze SERPs for these keywords,
    # focusing on question-based queries and featured snippets.
    # E.g., for "best air fryer for Indian cooking":
    # serpapi_key = "YOUR_SERPAPI_KEY"
    # url = f"https://serpapi.com/search.json?engine=google&q=best+air+fryer+for+Indian+cooking&api_key={serpapi_key}"
    # response = requests.get(url).json()
    # questions = [q['question'] for q in response.get('related_questions', [])]
    # print(f"\nRelated Questions from SERP: {questions}")

# Python for content generation using a GPT-5 (or similar LLM) via LangChain
# Assuming you have an API key for a GPT-5 equivalent service and LangChain installed.
from langchain.chat_models import ChatOpenAI # Or other LLM providers
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

def generate_voice_optimized_content(product_name, category, target_audience, key_features, current_price_range):
    """
    Generates a voice-optimized product review/overview.
    """
    # In 2025, assume 'ChatOpenAI' refers to a stable, advanced model like GPT-5
    llm = ChatOpenAI(model_name="gpt-5-turbo", temperature=0.7, openai_api_key="YOUR_GPT5_API_KEY")

    template = """
    You are an expert content creator specializing in voice commerce and Indian e-commerce.
    Your task is to create a short, conversational, and highly informative overview of a product,
    optimized for Amazon's Alexa+ LLM. Focus on answers to common questions and key benefits
    for the specified target audience.

    Product: {product_name}
    Category: {category}
    Target Audience: {target_audience} (e.g., 'young urban professionals in India looking for convenience')
    Key Features: {key_features} (comma-separated, e.g., 'fast cooking, oil-free, compact design')
    Current Price Range (INR): {current_price_range}

    Generate a conversational overview (approx. 150-200 words) that sounds natural when spoken by Alexa.
    Include a clear call-to-action to check Amazon.in.
    """

    prompt = PromptTemplate(
        input_variables=["product_name", "category", "target_audience", "key_features", "current_price_range"],
        template=template
    )

    chain = LLMChain(llm=llm, prompt=prompt)

    response = chain.run(
        product_name=product_name,
        category=category,
        target_audience=target_audience,
        key_features=key_features,
        current_price_range=current_price_range
    )
    return response

if __name__ == "__main__":
    # Example usage for an AI-powered air fryer
    product_overview = generate_voice_optimized_content(
        product_name="SmartChef AI Air Fryer X2",
        category="Kitchen Appliances",
        target_audience="Health-conscious Indian families in metro cities",
        key_features="Oil-free cooking, voice-activated recipes, auto-cleaning, 5-liter capacity, Alexa integrated",
        current_price_range="8,000 - 10,000"
    )
    print("\n--- Generated Voice-Optimized Content ---")
    print(product_overview)

# Python for auto-publishing to WordPress via XML-RPC (or REST API)
# For REST API, you'd use 'requests' with JWT authentication or Application Passwords.
from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost
from wordpress_xmlrpc.methods.media import UploadFile
import base64

def publish_to_wordpress(wp_url, wp_username, wp_password, title, content, categories, tags, image_path=None):
    """
    Publishes a new post to WordPress.
    """
    client = Client(wp_url, wp_username, wp_password)
    post = WordPressPost()
    post.title = title
    post.content = content
    post.post_status = 'publish' # or 'draft'
    post.terms_names = {
        'category': categories,
        'post_tag': tags
    }

    if image_path:
        # Upload featured image
        with open(image_path, 'rb') as img:
            data = {
                'name': 'featured_image.jpg',
                'type': 'image/jpeg',  # Adjust type based on your image
                'bits': base64.b64encode(img.read())
            }
            response = client.call(UploadFile(data))
            post.thumbnail = response['id']

    post_id = client.call(NewPost(post))
    print(f"Post '{title}' published successfully with ID: {post_id}")
    return post_id

if __name__ == "__main__":
    # Replace with your WordPress details
    wp_url = "https://yourblog.com/xmlrpc.php" # Or use REST API endpoint
    wp_username = "your_wp_user"
    wp_password = "your_wp_password"

    post_title = "SmartChef AI Air Fryer X2: The Future of Healthy Indian Cooking"
    post_content = product_overview # From previous step
    post_categories = ["Kitchen Appliances", "Smart Home"]
    post_tags = ["Air Fryer", "AI Cooking", "Alexa Integration", "Healthy Indian Food", "2025"]

    # Example: create a dummy image file for demonstration if not exists
    try:
        with open("dummy_image.jpg", "wb") as f:
            f.write(base64.b64decode("iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII="))
    except FileExistsError:
        pass # File already exists

    publish_to_wordpress(
        wp_url,
        wp_username,
        wp_password,
        post_title,
        post_content,
        post_categories,
        post_tags,
        image_path="dummy_image.jpg" # Optional: add a real image path
    )

# Python for scraping product data (simplified example)
import requests
from bs4 import BeautifulSoup
import pandas as pd

def scrape_amazon_product_data(product_url):
    """
    Scrapes product title, price, and a few reviews from a simplified Amazon-like page.
    Note: Real Amazon scraping is complex, against ToS, and requires sophisticated
    parsers/proxies. This is for illustrative purposes on a mock structure.
    """
    print(f"Attempting to scrape: {product_url}")
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36'
    }
    try:
        response = requests.get(product_url, headers=headers)
        response.raise_for_status() # Raise an exception for HTTP errors
        soup = BeautifulSoup(response.text, 'html.parser')

        product_data = {}
        product_data['title'] = soup.find('h1', id='productTitle').text.strip() if soup.find('h1', id='productTitle') else 'N/A'
        product_data['price'] = soup.find('span', class_='a-price-whole').text.strip() if soup.find('span', class_='a-price-whole') else 'N/A'

        reviews = []
        for review_div in soup.find_all('div', {'data-hook': 'review'}):
            review_text_tag = review_div.find('span', {'data-hook': 'review-body'})
            if review_text_tag:
                reviews.append(review_text_tag.text.strip())
            if len(reviews) >= 3: # Get top 3 reviews
                break
        product_data['reviews'] = reviews

        return product_data
    except requests.exceptions.RequestException as e:
        print(f"Error scraping data: {e}")
        return {}

if __name__ == "__main__":
    # This URL is illustrative. Do not use for actual Amazon scraping without permission.
    # Real scraping for Amazon requires respecting robots.txt, using proxies, and potentially CAPTCHA solving.
    mock_product_url = "https://www.example.com/mock-amazon-product-page.html"

    # For demonstration, let's create a very basic mock HTML file
    mock_html_content = """
    <html><body>
        <h1 id="productTitle">Awesome AI Gadget Pro</h1>
        <span class="a-price-whole">₹12,999</span>
        <div data-hook="review"><span data-hook="review-body">This gadget is truly amazing! Highly recommend.</span></div>
        <div data-hook="review"><span data-hook="review-body">Works perfectly, great value for money.</span></div>
        <div data-hook="review"><span data-hook="review-body">A must-have for tech enthusiasts.</span></div>
    </body></html>
    """
    with open("mock-amazon-product-page.html", "w") as f:
        f.write(mock_html_content)

    scraped_data = scrape_amazon_product_data(mock_product_url)
    if scraped_data:
        df = pd.DataFrame([scraped_data])
        print("\n--- Scraped Product Data (Pandas DataFrame) ---")
        print(df)
    else:
        print("Failed to scrape data.")