import requests
import json
import pandas as pd
from datetime import datetime

# SerpAPI config (replace with your actual API key)
SERPAPI_KEY = "YOUR_SERPAPI_API_KEY"

def get_google_trends_data(keyword, timeframe="today 3-m"):
    """Fetches Google Trends interest over time data."""
    # Note: Google Trends API is often restricted or requires complex authentication.
    # For robust automation, consider using a wrapper like PyTrends or a third-party service.
    # This is a conceptual example.
    print(f"Simulating Google Trends for '{keyword}'...")
    # In a real scenario, you'd use a library like pytrends or a specialized API.
    # For demonstration, we'll return mock data.
    return {"trend_score": 75, "rising_queries": ["ola electric scooter", "krutrim ai ev"]}

def get_serp_results(query, gl="in", hl="en"):
    """Fetches organic search results using SerpAPI."""
    url = "https://serpapi.com/search"
    params = {
        "engine": "google",
        "q": query,
        "gl": gl,  # Geo-location: India
        "hl": hl,  # Host language: English
        "api_key": SERPAPI_KEY
    }
    response = requests.get(url, params=params)
    response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)
    return response.json()

# Main workflow
niche_keywords = ["Ola Electric Car", "Krutrim AI applications in India", "sustainable transport India"]

print("--- Niche & Keyword Discovery ---")
for keyword in niche_keywords:
    # Get trend data
    trends_data = get_google_trends_data(keyword)
    print(f"Keyword: '{keyword}' - Trend Score: {trends_data['trend_score']}")
    print(f"  Rising Queries: {', '.join(trends_data['rising_queries'])}")

    # Get SERP data for competitive analysis
    search_results = get_serp_results(keyword)
    if "organic_results" in search_results:
        print(f"  Top 3 Organic Results for '{keyword}':")
        for i, result in enumerate(search_results["organic_results"][:3]):
            print(f"    {i+1}. {result.get('title')} ({result.get('link')})")
    print("-" * 30)

from openai import OpenAI # Assuming OpenAI API for GPT-5

# Initialize OpenAI client (replace with your actual API key)
client = OpenAI(api_key="YOUR_OPENAI_API_KEY")

def generate_content_brief(topic, serp_data, target_audience="solo content creators and bloggers"):
    """Generates a detailed content brief using GPT-5."""
    serp_summary = "\n".join([f"- {res.get('title')}: {res.get('snippet')}" for res in serp_data.get('organic_results', [])[:5]])

    prompt = f"""
    You are an expert content strategist. Your task is to create a detailed content brief for a blog post on the topic: "{topic}".
    The target audience is {target_audience} interested in automation and monetization.

    Here are some top search results for this topic:
    {serp_summary}

    Based on the topic and the search results, generate a comprehensive content brief that includes:
    1.  **Proposed Title Options** (3-5 options, including a viral but not clickbait one)
    2.  **Target Keywords** (main and LSI keywords)
    3.  **Audience Persona**
    4.  **Key Questions to Answer**
    5.  **Main Sections/Headings** (detailed outline)
    6.  **Call to Action (CTA) Suggestions**
    7.  **Tone and Style Guidelines** (e.g., informative, energetic, practical)
    8.  **Monetization Opportunities** (e.g., affiliate products, services)

    Focus on providing actionable insights for content automation.
    """

    response = client.chat.completions.create(
        model="gpt-5-turbo", # Assuming GPT-5 equivalent model in 2025
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ],
        max_tokens=1500,
        temperature=0.7
    )
    return response.choices[0].message.content

# Example usage (using mock SerpAPI data from previous step)
mock_serp_data = {
    "organic_results": [
        {"title": "Krutrim AI: India's Answer to OpenAI", "snippet": "Ola's Krutrim aims to challenge global AI players with a focus on Indian languages."},
        {"title": "How Krutrim AI Works - Technical Deep Dive", "snippet": "An overview of Krutrim's architecture and training data."},
        {"title": "Monetizing AI Content in 2025", "snippet": "Strategies for bloggers to earn from AI-generated content."},
        {"title": "The Rise of Localized LLMs", "snippet": "Examining the impact of region-specific AI models."},
        {"title": "Automated Blogging Workflows", "snippet": "Tools and techniques for full-stack content automation."}
    ]
}

topic_for_brief = "Krutrim AI's impact on automated content creation and monetization for solo bloggers"
content_brief = generate_content_brief(topic_for_brief, mock_serp_data)
print("\n--- Generated Content Brief ---")
print(content_brief)

# Conceptual example demonstrating multi-model content generation
# Assuming Krutrim AI has an accessible API for content generation
from openai import OpenAI # For GPT-5
# from krutrim_ai import KrutrimClient # Hypothetical Krutrim AI client

# client_gpt5 = OpenAI(api_key="YOUR_OPENAI_API_KEY")
# client_krutrim = KrutrimClient(api_key="YOUR_KRUTRIM_API_KEY") # Hypothetical

def generate_draft_section(llm_client, prompt, section_title, max_tokens=700):
    """Generates a section of the content using the specified LLM client."""
    print(f"Generating section '{section_title}' with {llm_client.__class__.__name__}...")
    try:
        response = llm_client.chat.completions.create(
            model="gpt-5-turbo" if isinstance(llm_client, OpenAI) else "krutrim-large", # Choose model based on client
            messages=[
                {"role": "system", "content": "You are an expert content writer."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=max_tokens,
            temperature=0.8
        )
        return response.choices[0].message.content
    except Exception as e:
        print(f"Error generating section with {llm_client.__class__.__name__}: {e}")
        return f"Failed to generate content for {section_title}."

# Simulating the content generation process
# For this example, we'll use OpenAI's client for both general and "localized" parts
# In a real 2025 scenario, you'd swap `client_gpt5` for `client_krutrim` where appropriate.

content_brief_sections = {
    "Introduction: The New Frontier": "Write an engaging introduction about Krutrim AI and its impact on automated content creation, setting the stage for solo creators.",
    "Krutrim AI Unpacked: What It Is and Why It Matters": "Detail Krutrim AI's features, especially its focus on Indian languages and cultural nuances, explaining its strategic advantage for niche markets. Emphasize its role as a potential challenger to global LLMs.",
    "Monetization Strategies for Automated Content": "Elaborate on concrete monetization avenues like affiliate marketing, information products, and services using AI-generated content."
}

full_article_sections = {}

print("\n--- Generating Article Draft ---")
for title, prompt_instruction in content_brief_sections.items():
    if "Indian languages" in prompt_instruction or "niche markets" in prompt_instruction:
        # Conceptual: If a section is highly localized, use Krutrim (or a highly prompted GPT-5)
        # prompt = f"Using a localized AI perspective, {prompt_instruction}"
        # generated_content = generate_draft_section(client_krutrim, prompt, title) # Use Krutrim client
        # For demo, use GPT-5 with specific prompt for localization
        prompt = f"Adopt an expert tone for content creators. Focus on the unique advantages of Krutrim AI for localized content in India. {prompt_instruction}"
        generated_content = generate_draft_section(client_gpt5, prompt, title)
    else:
        # Use GPT-5 for general sections
        generated_content = generate_draft_section(client_gpt5, prompt_instruction, title)

    full_article_sections[title] = generated_content
    print(f"\nGenerated '{title}' (first 200 chars):\n{generated_content[:200]}...\n")

# Refinement & SEO Optimization using GPT-5
from openai import OpenAI
import json

client = OpenAI(api_key="YOUR_OPENAI_API_KEY")

def refine_and_optimize_content(article_sections, primary_keyword, existing_links=None):
    """Refines content, generates meta description, and suggests internal/external links."""
    full_text = "\n\n".join([f"## {title}\n\n{content}" for title, content in article_sections.items()])

    prompt = f"""
    You are an expert SEO and content editor. Your task is to refine the following article draft,
    optimize it for SEO, and generate a meta description and internal/external link suggestions.

    **Primary Keyword:** {primary_keyword}

    **Article Draft:**
    {full_text}

    ---

    **Instructions:**
    1.  **Refinement:** Improve flow, readability, and ensure a consistent, energetic, smart tone. Do not rewrite completely, just enhance.
    2.  **SEO Integration:** Naturally weave in the primary keyword and related LSI keywords. Ensure headings are clear.
    3.  **Meta Description:** Generate a compelling meta description (150-160 characters) for the article.
    4.  **Internal/External Link Suggestions:** Suggest 3-5 relevant internal links (to hypothetical related articles on the same blog) and 3-5 external links (to authoritative sources like academic papers, official company pages, or industry reports). Format as Markdown links.
    5.  **Output Format:** Provide the refined content first, then the meta description, then the link suggestions.
    """

    response = client.chat.completions.create(
        model="gpt-5-turbo",
        messages=[
            {"role": "system", "content": "You are a helpful SEO and content optimization assistant."},
            {"role": "user", "content": prompt}
        ],
        max_tokens=2000,
        temperature=0.7
    )
    return response.choices[0].message.content

# Using the previously generated mock article sections
primary_keyword_for_seo = "Krutrim AI content automation"
refined_output = refine_and_optimize_content(full_article_sections, primary_keyword_for_seo)

print("\n--- Refined and Optimized Content ---")
print(refined_output)

from wordpress_xmlrpc import Client, WordPressPost
from wordpress_xmlrpc.methods.posts import NewPost
from wordpress_xmlrpc.methods.media import UploadFile
from wordpress_xmlrpc.compat import xmlrpc_client
import os

# WordPress configuration (replace with your details)
WORDPRESS_URL = "https://yourblog.com/xmlrpc.php"
WORDPRESS_USERNAME = "your_username"
WORDPRESS_PASSWORD = "your_application_password" # Use application password for security

def upload_image_to_wp(client, image_path):
    """Uploads an image to WordPress and returns its URL."""
    data = {
        'name': os.path.basename(image_path),
        'type': 'image/jpeg',  # Adjust if not JPEG
    }
    with open(image_path, 'rb') as img:
        data['bits'] = xmlrpc_client.Binary(img.read())

    response = client.call(UploadFile(data))
    return response['url']

def publish_post_to_wp(title, content, categories, tags, image_url=None, status='publish'):
    """Publishes a new post to WordPress."""
    try:
        client = Client(WORDPRESS_URL, WORDPRESS_USERNAME, WORDPRESS_PASSWORD)

        post = WordPressPost()
        post.title = title
        post.content = content
        post.categories = categories
        post.tags = tags
        post.post_status = status

        if image_url:
            # Example of adding an image using HTML <img> tag
            # For setting as featured image, you'd need the image ID and specific meta
            post.content = f'<img src="{image_url}" alt="{title} featured image" style="width:100%; height:auto;">\n\n' + post.content

        new_post_id = client.call(NewPost(post))
        print(f"Successfully published post with ID: {new_post_id}")
        return new_post_id

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

# Example usage (using parts of the previous output)
post_title = "Krutrim AI: Your New Secret Weapon for Niche Blogging Profits"
post_content = refined_output # Use the full refined content from the previous step
post_categories = ["AI Blogging", "Automation"]
post_tags = ["Krutrim AI", "LLM 2025", "Content Automation", "Monetization"]

# Create a dummy image for upload demonstration
# In a real scenario, this would be an AI-generated image or a relevant stock photo
dummy_image_path = "krutrim_ai_banner.jpg"
with open(dummy_image_path, "wb") as f:
    f.write(b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00\x00\x01\x08\x06\x00\x00\x00\x1f\x15\xc4\x89\x00\x00\x00\x0cIDATx\xda\xed\xc1\x01\x01\x00\x00\x00\xc2\xa0\xf7Om\x00\x00\x00\x00IEND\xaeB`\x82") # A very small valid PNG

# Simulate image upload and then post
print("\n--- Publishing Post to WordPress ---")
# image_url = upload_image_to_wp(Client(WORDPRESS_URL, WORDPRESS_USERNAME, WORDPRESS_PASSWORD), dummy_image_path)
# if image_url:
#     print(f"Image uploaded to: {image_url}")
#     publish_post_to_wp(post_title, post_content, post_categories, post_tags, image_url=image_url)
# else:
#     print("Image upload failed. Publishing without image.")
#     publish_post_to_wp(post_title, post_content, post_categories, post_tags)

# For demonstration without actual WordPress connection, simulate success:
print("Simulating WordPress post publication...")
simulated_post_id = 12345
print(f"Successfully published post with ID: {simulated_post_id}")
# Clean up dummy image
if os.path.exists(dummy_image_path):
    os.remove(dummy_image_path)