from langchain_community.tools import TavilySearchResults
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import PromptTemplate
from tavily import TavilyClient
import asyncio
import sys

import os
from dotenv import load_dotenv

# files
from crawl import seo_crawling

# Secret Key
load_dotenv(override=True)
tavily_api_key = os.getenv("TAVILY_API_KEY")
gemini_api_key = os.getenv("GEMINI_API_KEY")


llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash-exp", api_key = gemini_api_key) # type: ignore


# def tavily_search(query: str):
    
#     tavily = TavilySearchResults(
#         max_results=10,
#         search_depth="advanced",
#         include_answer=True,
#         include_images=True,
#         include_links=True, # type: ignore
#         api_key=tavily_api_key, # type: ignore
#     )
    
#     results = tavily.invoke({"query": f"{query}"})
#     return results

# results = tavily_search("UK Air Source Heat Pump Market Trends 2025")
# print(results)


def tavily_search(query):
    tavily_client = TavilyClient(api_key = tavily_api_key)
    response = tavily_client.search(query, max_results=10)
    # print(response["results"])
    return response["results"]


def match_title(title, titles):
    title_prompt_template = PromptTemplate.from_template("""
        Your task is to find the title in the List that semantically matches the User_title.
        - Don't change the title name
        - Don't give extra content. Only give the title name.
        - Only give **One title**

        List = {list}
        User_title = {title}

    """)

    prompt = title_prompt_template.invoke({"list": titles, "title": title})
    response = llm.invoke(prompt)
    return response.content


def keywords(content):
    keywords_prompt_template = PromptTemplate.from_template("""
    # **Instruction:**
    Analyze the given text and extract keywords based on their relevance to SEO. Categorize them into the following three groups:

    ## **1. Primary Keywords (High-Impact, Industry-Specific):**
       - Broad, high-volume search terms that are directly related to the main topic.
       - Common industry terms that people search for when looking for services or information.
       - Maximum **10-12 keywords**.

    ## **2. Secondary Keywords (Supporting SEO & Long-Tail Queries):**
       - More specific, longer phrases related to the main topic.
       - Keywords that provide contextual depth and support for primary keywords.
       - Maximum **10-12 keywords**.

    ## **3. Local SEO Keywords (Boosting Regional Visibility):**
       - Keywords that include location-specific terms.
       - Phrases that help rank in local search results.
       - Maximum **5-8 keywords**.
       
    # Don't give the extra content only give the SEO keywords

    # **Input:**
    {text}

    """)
    
    
    prompt = keywords_prompt_template.invoke({"text": content})
    response = llm.invoke(prompt)
    return response.content




def seo_keywords(state):
    
    topic = state["final_topic"]
    # topic = state
    
    results = tavily_search(topic)

    titles = []
    titles_url = []
    final_url = ""
    
    for t in results:
        titles.append(t['title'])
    
    for t in results:
        titles_url.append({
            "title": t['title'],
            "url": t['url']
        })  
    
    print(titles)
    print(titles_url)
   
    text = match_title(topic, titles)
    print(text)


    for title in titles_url:
        if title['title'] == text:
            final_url = title['url']

    print(final_url)
        
    crawled_content = asyncio.run(seo_crawling(final_url))
    response = keywords(crawled_content)
    print(response)
    return{"seo_keywords":response}

# a = seo_keywords("UK Air Source Heat Pump Market Trends 2025")
# print(a)