import json
from pydantic import BaseModel
from typing import List, Set, Tuple
from crawl4ai import (
    AsyncWebCrawler,
    BrowserConfig,
    CacheMode,
    CrawlerRunConfig,
    LLMExtractionStrategy,
)
from utils import is_duplicated
from config import LLM_MODEL, API_TOKEN


def get_browser_config() -> BrowserConfig:
    """
    Returns the browser configuration for the crawler.

    Returns:
        BrowserConfig: The configuration settings for the browser.
    """
    # https://docs.crawl4ai.com/core/browser-crawler-config/
    return BrowserConfig(
        browser_type="chromium",  # Type of browser to simulate
        headless=True,  # Whether to run in headless mode (no GUI)
        verbose=True,  # Enable verbose logging
    )


def get_llm_strategy(llm_instructions: str, output_format: BaseModel) -> LLMExtractionStrategy:
    """
    Returns the configuration for the language model extraction strategy.

    Returns:
        LLMExtractionStrategy: The settings for how to extract data using LLM.
    """
    # https://docs.crawl4ai.com/api/strategies/#llmextractionstrategy
    return LLMExtractionStrategy(
        provider=LLM_MODEL,  # Name of the LLM provider
        api_token=API_TOKEN,  # API token for authentication
        schema=output_format.model_json_schema(),  # JSON schema of the data model
        extraction_type="schema",  # Type of extraction to perform
        instruction=llm_instructions,  # Instructions for the LLM
        input_format="markdown",  # Format of the input content
        verbose=True,  # Enable verbose logging
    )

async def check_no_results(
    crawler: AsyncWebCrawler,
    url: str,
    session_id: str,
) -> bool:
    """
    Checks if the "No Results Found" message is present on the page.

    Args:
        crawler (AsyncWebCrawler): The web crawler instance.
        url (str): The URL to check.
        session_id (str): The session identifier.

    Returns:
        bool: True if "No Results Found" message is found, False otherwise.
    """
    # Fetch the page without any CSS selector or extraction strategy
    result = await crawler.arun(
        url=url,
        config=CrawlerRunConfig(
            cache_mode=CacheMode.BYPASS,
            session_id=session_id,
        ),
    )

    if result.success:
        if "No Results Found" in result.cleaned_html:
            return True
    else:
        print(
            f"Error fetching page for 'No Results Found' check: {result.error_message}"
        )

    return False


async def fetch_and_process_page(
    crawler: AsyncWebCrawler,
    page_number: int,
    base_url: str,
    css_selector: str,
    llm_strategy: LLMExtractionStrategy,
    session_id: str,
    seen_names: Set[str],
) -> Tuple[List[dict], bool]:
    """
    Fetches and processes a single page from yellowpages.

    Args:
        crawler (AsyncWebCrawler): The web crawler instance.
        page_number (int): The page number to fetch.
        base_url (str): The base URL of the website.
        css_selector (str): The CSS selector to target the content.
        llm_strategy (LLMExtractionStrategy): The LLM extraction strategy.
        session_id (str): The session identifier.
        required_keys (List[str]): List of required keys in the business data.
        seen_names (Set[str]): Set of business names that have already been seen.

    Returns:
        Tuple[List[dict], bool]:
            - List[dict]: A list of processed businesss from the page.
            - bool: A flag indicating if the "No Results Found" message was encountered.
    """
    url = base_url.format(page_number=page_number)
    print(f"Loading page {page_number}...")

    # Check if "No Results Found" message is present
    no_results = await check_no_results(crawler, url, session_id)
    if no_results:
        return [], True  # No more results, signal to stop crawling

    # Fetch page content with the extraction strategy
    result = await crawler.arun(
        url=url,
        config=CrawlerRunConfig(
            cache_mode=CacheMode.BYPASS,  # Do not use cached data
            extraction_strategy=llm_strategy,  # Strategy for data extraction
            css_selector=css_selector,  # Target specific content on the page
            session_id=session_id,  # Unique session ID for the crawl
        ),
    )
    print("-----------------------------    Result-----------------------------")
    print(result.extracted_content)

    if not (result.success and result.extracted_content):
        print(f"Error fetching page {page_number}: {result.error_message}")
        return [], False

    # Parse extracted content
    extracted_data = json.loads(result.extracted_content)
    print("----------------------------Exracted Data----------------------------")
    print(extracted_data)
    if not extracted_data:
        print(f"No businesss found on page {page_number}.")
        return [], False

    # After parsing extracted content
    print("Extracted data:", extracted_data)

    # Process businesss
    all_businesses = []
    for business in extracted_data:
        # Debugging: Print each business to understand its structure
        print("Processing business:", business)

        # Ignore the 'error' key if it's False
        if business.get("error") is False:
            business.pop("error", None)  # Remove the 'error' key if it's False

        if is_duplicated(business["name"], seen_names):
            print(f"Duplicate business '{business['name']}' found. Skipping.")
            continue  # Skip duplicate businesss

        # Add business to the list
        seen_names.add(business["name"])
        all_businesses.append(business)

    if not all_businesses:
        print(f"No complete businesss found on page {page_number}.")
        return [], False

    print(f"Extracted {len(all_businesses)} businesss from page {page_number}.")
    return all_businesses, False  # Continue crawling