import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
import json
import re
import logging
from urllib.parse import urlparse
import fastavro
import msgpack
import os
from datasets import Dataset

# Function to scrape data and return as a Hugging Face Dataset
def scrape_data(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    
    # Example scraping logic (to be customized)
    data = []
    for item in soup.find_all('div', class_='gpu-item'):
        gpu_info = {
            'gpu_name': item.find('h2').text,
            'architecture': item.find('span', class_='architecture').text,
            'memory_size': item.find('span', class_='memory-size').text,
            # Add more fields as necessary
        }
        data.append(gpu_info)
    
    # Convert to Hugging Face Dataset
    return Dataset.from_list(data)

# Import additional libraries for new formats
import pyarrow as pa
import pyarrow.parquet as pq
import fastavro
import h5py
import sqlite3
import xml.etree.ElementTree as ET
import yaml
import pickle
from scipy.io import savemat

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Try to import Selenium components - they'll be used if available
try:
    from selenium import webdriver
    from selenium.webdriver.chrome.options import Options
    from selenium.webdriver.chrome.service import Service
    from selenium.webdriver.common.by import By
    from selenium.webdriver.support.ui import WebDriverWait
    from selenium.webdriver.support import expected_conditions as EC
    from webdriver_manager.chrome import ChromeDriverManager
    SELENIUM_AVAILABLE = True
    logger.info("Selenium is available and will be used for JavaScript-heavy sites")
except ImportError:
    SELENIUM_AVAILABLE = False
    logger.warning("Selenium not available. Install with: pip install selenium webdriver-manager")

class NvidiaGpuScraper:
    def __init__(self, use_selenium=True):
        self.use_selenium = use_selenium and SELENIUM_AVAILABLE
        self.driver = self._setup_driver() if self.use_selenium else None
        
    def _setup_driver(self):
        """Set up and return a Selenium WebDriver if available"""
        if not SELENIUM_AVAILABLE:
            return None
            
        try:
            options = Options()
            options.add_argument('--headless')
            options.add_argument('--no-sandbox')
            options.add_argument('--disable-dev-shm-usage')
            options.add_argument('--disable-gpu')
            options.add_argument("--window-size=1920,1080")
            options.add_argument("--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.102 Safari/537.36")
            
            service = Service(ChromeDriverManager().install())
            driver = webdriver.Chrome(service=service, options=options)
            return driver
        except Exception as e:
            logger.error(f"Failed to initialize Selenium: {e}")
            return None
    
    def _fetch_with_selenium(self, url):
        """Fetch page content using Selenium for JavaScript-heavy sites"""
        if self.driver is None:
            return None
            
        try:
            logger.info(f"Fetching with Selenium: {url}")
            self.driver.get(url)
            # Wait for the page to load completely
            WebDriverWait(self.driver, 20).until(
                EC.presence_of_element_located((By.TAG_NAME, "body"))
            )
            
            # Scroll down to load lazy content
            self.driver.execute_script("window.scrollTo(0, document.body.scrollHeight/2);")
            time.sleep(1)
            self.driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
            time.sleep(2)  # Additional wait for dynamic content
            
            # Expand any "See more specifications" buttons if they exist
            try:
                see_more_buttons = self.driver.find_elements(By.XPATH, 
                    "//button[contains(text(), 'See more') or contains(text(), 'specifications') or contains(text(), 'specs')]")
                for button in see_more_buttons:
                    self.driver.execute_script("arguments[0].click();", button)
                    time.sleep(1)
            except Exception as e:
                logger.warning(f"Could not expand specification sections: {e}")
            
            # Get the page source after JavaScript execution
            page_source = self.driver.page_source
            return BeautifulSoup(page_source, 'html.parser')
        except Exception as e:
            logger.error(f"Selenium error for {url}: {e}")
            return None
    
    def _fetch_with_requests(self, url):
        """Fetch page content using requests library"""
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.102 Safari/537.36',
            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
            'Accept-Language': 'en-US,en;q=0.5',
            'Referer': 'https://www.google.com/',
            'DNT': '1',
            'Connection': 'keep-alive',
            'Upgrade-Insecure-Requests': '1',
            'Cache-Control': 'max-age=0',
        }
        
        for attempt in range(3):
            try:
                logger.info(f"Fetching with requests: {url}")
                response = requests.get(url, timeout=30, headers=headers)
                response.raise_for_status()
                return BeautifulSoup(response.content, 'html.parser')
            except requests.exceptions.RequestException as e:
                wait_time = 2 ** attempt
                logger.warning(f"Request error for {url}: {e}. Retrying in {wait_time} seconds...")
                time.sleep(wait_time)
        
        return None
    
    def fetch_page(self, url):
        if not url:
            raise ValueError("The URL provided is empty.")
        """Fetch page content, trying Selenium first if available"""
        if self.use_selenium:
            soup = self._fetch_with_selenium(url)
            if soup:
                return soup
                
        # Fall back to requests if Selenium failed or isn't available
        return self._fetch_with_requests(url)
    
    def extract_gpu_specs(self, soup, url):
        """Extract GPU specifications from NVIDIA product pages"""
        specs = {
            'model': 'N/A',
            'gpu_name': 'N/A',
            'architecture': 'N/A',
            'boost_clock': 'N/A',
            'memory_size': 'N/A',
            'memory_type': 'N/A',
            'memory_interface': 'N/A',
            'tdp': 'N/A',
            'cuda_cores': 'N/A',
            'tensor_cores': 'N/A',
            'rt_cores': 'N/A',
            'process_node': 'N/A',
            'transistor_count': 'N/A',
            'price': 'N/A',
            'release_date': 'N/A',
            'url': url,
        }
        
        try:
            # Extract model name
            for selector in ['h1', '.product-title', '.product-name', '.prod-title']:
                title_element = soup.select_one(selector)
                if title_element and title_element.text.strip():
                    specs['model'] = title_element.text.strip()
                    # Try to extract GPU name (e.g., RTX 4090)
                    gpu_match = re.search(r'(GTX|RTX|RTX\s+SUPER|GTX\s+SUPER)\s+(\d{4}\s*(?:Ti|SUPER)?)', 
                                         specs['model'], re.IGNORECASE)
                    if gpu_match:
                        specs['gpu_name'] = f"{gpu_match.group(1)} {gpu_match.group(2)}".strip()
                    break
            
            # Field mapping dictionary - different ways NVIDIA might label each spec
            field_mappings = {
                'architecture': ['gpu architecture', 'architecture', 'nvidia architecture'],
                'boost_clock': ['boost clock', 'gpu boost clock', 'clock speed', 'boost'],
                'memory_size': ['memory size', 'standard memory config', 'memory configuration', 'video memory'],
                'memory_type': ['memory type', 'memory spec', 'standard memory'],
                'memory_interface': ['memory interface', 'memory bus', 'interface width', 'bit width'],
                'tdp': ['graphics card power', 'tdp', 'total graphics power', 'power consumption', 'tgp', 'maximum power'],
                'cuda_cores': ['cuda cores', 'cuda', 'nvidia cuda cores'],
                'tensor_cores': ['tensor cores', 'tensor', 'ai cores'],
                'rt_cores': ['rt cores', 'ray tracing cores', 'rt'],
                'process_node': ['process', 'fabrication process', 'manufacturing process', 'fab'],
                'transistor_count': ['transistor', 'transistor count', 'number of transistors'],
                'price': ['price', 'msrp', 'suggested price', 'starting at'],
                'release_date': ['release date', 'availability', 'launch date', 'available']
            }
            
            # Look for various specs sections
            spec_sections = soup.select('.specs-section, .tech-specs, .product-specs, .specs, .spec-table, .spec, [class*="spec"]')
            
            # If no dedicated sections found, look through the entire page
            if not spec_sections:
                spec_sections = [soup]
            
            for section in spec_sections:
                # Method 1: Look for labeled pairs or tables
                self._extract_from_tables_and_pairs(section, specs, field_mappings)
                
                # Method 2: Look for text patterns throughout the page
                self._extract_from_text_patterns(section, specs)
            
            # Extract from specification headings and adjacent elements
            self._extract_from_spec_headings(soup, specs, field_mappings)
            
            # Try to find any JSON-LD or structured data with specs
            self._extract_from_json_ld(soup, specs)
            
            # Clean and standardize specs
            self._clean_specs(specs)
            
            logger.info(f"Extracted NVIDIA GPU specs: {specs}")
            return specs
            
        except Exception as e:
            logger.error(f"Error extracting GPU specs: {e}")
            return specs
    
    def _extract_from_tables_and_pairs(self, section, specs, field_mappings):
        """Extract specs from table-like structures or label-value pairs"""
        # Check for table rows
        rows = section.select('tr, .spec-row, .specs-row, [class*="row"]')
        for row in rows:
            cells = row.select('th, td, .spec-label, .spec-value, .specs-label, .specs-value')
            if len(cells) >= 2:
                header = cells[0].text.strip().lower()
                value = cells[1].text.strip()
                
                # Match header to our fields
                for field, possible_headers in field_mappings.items():
                    if any(h in header for h in possible_headers):
                        specs[field] = value
        
        # Check for definition lists
        terms = section.select('dt, .term, .specs-term')
        for term in terms:
            header = term.text.strip().lower()
            value_el = term.find_next_sibling(['dd', '.definition', '.specs-definition'])
            if value_el:
                value = value_el.text.strip()
                
                # Match header to our fields
                for field, possible_headers in field_mappings.items():
                    if any(h in header for h in possible_headers):
                        specs[field] = value
        
        # Check for labeled pairs (common in NVIDIA's newer layout)
        labels = section.select('.specs-label, .spec-label, .specs-name, .label, [class*="label"]')
        for label in labels:
            header = label.text.strip().lower()
            # Try to find the adjacent value element
            value_el = label.find_next_sibling('.specs-value, .spec-value, .specs-data, .value, [class*="value"]')
            if value_el:
                value = value_el.text.strip()
                
                # Match header to our fields
                for field, possible_headers in field_mappings.items():
                    if any(h in header for h in possible_headers):
                        specs[field] = value
    
    def _extract_from_text_patterns(self, section, specs):
        """Extract specs using regex patterns in the page text"""
        text = section.get_text(' ', strip=True)
        
        # Extract CUDA cores
        cuda_matches = re.search(r'(\d[\d,]+)\s*(?:nvidia)?\s*cuda\s*cores', text, re.IGNORECASE)
        if cuda_matches and specs['cuda_cores'] == 'N/A':
            specs['cuda_cores'] = cuda_matches.group(1)
        
        # Extract Tensor cores
        tensor_matches = re.search(r'(\d+)\s*(?:nvidia)?\s*tensor\s*cores', text, re.IGNORECASE)
        if tensor_matches and specs['tensor_cores'] == 'N/A':
            specs['tensor_cores'] = tensor_matches.group(1)
        
        # Extract RT cores
        rt_matches = re.search(r'(\d+)\s*(?:nvidia)?\s*rt\s*cores', text, re.IGNORECASE)
        if rt_matches and specs['rt_cores'] == 'N/A':
            specs['rt_cores'] = rt_matches.group(1)
        
        # Extract memory size
        mem_matches = re.search(r'(\d+)\s*GB\s*(?:G?DDR\d+[X]?)', text, re.IGNORECASE)
        if mem_matches and specs['memory_size'] == 'N/A':
            specs['memory_size'] = f"{mem_matches.group(1)} GB"
            if specs['memory_type'] == 'N/A':
                specs['memory_type'] = mem_matches.group(2)
        
        # Extract boost clock
        clock_matches = re.search(r'boost\s*clock\s*(?:up\s*to)?\s*:?\s*([\d.]+)\s*(?:MHz|GHz)', text, re.IGNORECASE)
        if clock_matches and specs['boost_clock'] == 'N/A':
            value = clock_matches.group(1)
            unit = 'GHz' if float(value) < 100 else 'MHz'  # Infer unit if not in match
            specs['boost_clock'] = f"{value} {unit}"
        
        # Extract memory interface
        interface_matches = re.search(r'(\d+)[\s-]*bit(?:\s*memory)?\s*(?:interface|bus)', text, re.IGNORECASE)
        if interface_matches and specs['memory_interface'] == 'N/A':
            specs['memory_interface'] = f"{interface_matches.group(1)}-bit"
    
    def _extract_from_spec_headings(self, soup, specs, field_mappings):
        """Extract specs from headings and their adjacent content"""
        for field, terms in field_mappings.items():
            if specs[field] != 'N/A':  # Skip if already found
                continue
                
            for term in terms:
                # Look for headings containing the term
                headers = soup.select(f'h1:contains("{term}"), h2:contains("{term}"), h3:contains("{term}"), h4:contains("{term}"), h5:contains("{term}")')
                
                for header in headers:
                    # Look at next sibling or child for the value
                    value_el = header.find_next()
                    if value_el:
                        specs[field] = value_el.text.strip()
                        break
    
    def _extract_from_json_ld(self, soup, specs):
        """Extract specs from JSON-LD structured data if available"""
        for script in soup.select('script[type="application/ld+json"]'):
            try:
                data = json.loads(script.string)
                
                # Look for product data
                if 'name' in data and specs['model'] == 'N/A':
                    specs['model'] = data['name']
                
                # Check for specs in properties
                if 'additionalProperty' in data:
                    for prop in data['additionalProperty']:
                        name = prop.get('name', '').lower()
                        value = prop.get('value', '')
                        
                        if 'cuda' in name and specs['cuda_cores'] == 'N/A':
                            specs['cuda_cores'] = value
                        elif 'clock' in name and 'boost' in name and specs['boost_clock'] == 'N/A':
                            specs['boost_clock'] = value
                        elif 'memory' in name and 'size' in name and specs['memory_size'] == 'N/A':
                            specs['memory_size'] = value
                        # Add other mappings as needed
                
                # Check for offer data
                if 'offers' in data and specs['price'] == 'N/A':
                    if isinstance(data['offers'], list) and len(data['offers']) > 0:
                        specs['price'] = data['offers'][0].get('price', 'N/A')
                    elif isinstance(data['offers'], dict):
                        specs['price'] = data['offers'].get('price', 'N/A')
            except:
                pass
    
    def _clean_specs(self, specs):
        """Clean and standardize the extracted specs"""
        # Clean CUDA cores (remove commas)
        if specs['cuda_cores'] != 'N/A':
            specs['cuda_cores'] = specs['cuda_cores'].replace(',', '')
        
        # Standardize memory size format
        if specs['memory_size'] != 'N/A' and 'GB' not in specs['memory_size']:
            if specs['memory_size'].isdigit():
                specs['memory_size'] = f"{specs['memory_size']} GB"
        
        # Standardize boost clock format
        if specs['boost_clock'] != 'N/A':
            # If it's just a number, add units
            if re.match(r'^\d+(\.\d+)?$', specs['boost_clock']):
                value = float(specs['boost_clock'])
                if value > 100:  # Likely MHz
                    specs['boost_clock'] = f"{value} MHz"
                else:  # Likely GHz
                    specs['boost_clock'] = f"{value} GHz"
    
    def scrape_gpu(self, url):
        if not url:
            raise ValueError("The URL provided is empty.")
        """Scrape a single GPU product page"""
        soup = self.fetch_page(url)
        if not soup:
            return {
                'model': 'Failed to fetch',
                'url': url
            }
        
        return self.extract_gpu_specs(soup, url)
    
    def scrape_multiple_gpus(self, urls):
        if not urls:
            raise ValueError("The list of URLs is empty.")
        """Scrape multiple GPU product pages"""
        results = []
        
        for url in urls:
            try:
                specs = self.scrape_gpu(url)
                results.append(specs)
                # Be polite with a delay between requests
                time.sleep(2)
            except Exception as e:
                logger.error(f"Error processing {url}: {e}")
                results.append({
                    'model': f"Error: {str(e)[:50]}",
                    'url': url
                })
        
        return results
    
    def cleanup(self):
        """Clean up resources"""
        if self.driver:
            self.driver.quit()

# Main execution function
def main():
    # NVIDIA GPU product URLs - focused on specific product pages
    nvidia_urls = [
        "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/",
        "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/",
        "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/",
        "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/",
        "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/",
        "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/",
        "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/",
    ]
    
    # Create the scraper and run
    scraper = NvidiaGpuScraper(use_selenium=SELENIUM_AVAILABLE)
    
    try:
        # Scrape the GPUs
        results = scraper.scrape_multiple_gpus(nvidia_urls)
        
        # Create and save DataFrame
        df = pd.DataFrame(results)
        df.to_csv('nvidia_gpus.csv', index=False)
        df.to_json('nvidia_gpus.json', orient='records', lines=True)
        df.to_excel('nvidia_gpus.xlsx', index=False)
        
        # Save DataFrame in various formats
        try:
            df.to_parquet('nvidia_gpus.parquet')
        except Exception as e:
            logger.warning(f"Failed to save as Parquet: {e}")

        try:
            # Convert DataFrame to list of dictionaries
            records = df.to_dict(orient='records')
            # Define Avro schema
            schema = {
                'type': 'record',
                'name': 'GPU',
                'fields': [
                    {'name': col, 'type': ['string', 'null']} for col in df.columns
                ]
            }
            # Write to Avro file
            with open('nvidia_gpus.avro', 'wb') as avro_file:
                fastavro.writer(avro_file, schema, records)
        except Exception as e:
            logger.warning(f"Failed to save as Avro: {e}")

        try:
            df.to_orc('nvidia_gpus.orc')
        except Exception as e:
            logger.warning(f"Failed to save as ORC: {e}")

        try:
            df.to_hdf('nvidia_gpus.h5', key='df', mode='w')
        except Exception as e:
            logger.warning(f"Failed to save as HDF5: {e}")

        try:
            with sqlite3.connect('nvidia_gpus.db') as conn:
                df.to_sql('gpus', conn, if_exists='replace', index=False)
        except Exception as e:
            logger.warning(f"Failed to save as SQLite: {e}")

        try:
            df.to_xml('nvidia_gpus.xml')
        except Exception as e:
            logger.warning(f"Failed to save as XML: {e}")

        try:
            with open('nvidia_gpus.yaml', 'w') as yaml_file:
                yaml.dump(df.to_dict(orient='records'), yaml_file)
        except Exception as e:
            logger.warning(f"Failed to save as YAML: {e}")

        try:
            with open('nvidia_gpus.pkl', 'wb') as pickle_file:
                pickle.dump(df, pickle_file)
        except Exception as e:
            logger.warning(f"Failed to save as Pickle: {e}")

        try:
            savemat('nvidia_gpus.mat', {'gpus': df.to_dict(orient='records')})
        except Exception as e:
            logger.warning(f"Failed to save as MAT: {e}")

        try:
            df.to_csv('nvidia_gpus.tsv', sep='\t', index=False)
        except Exception as e:
            logger.warning(f"Failed to save as TSV: {e}")

        try:
            df.to_json('nvidia_gpus.ndjson', orient='records', lines=True)
        except Exception as e:
            logger.warning(f"Failed to save as NDJSON: {e}")

        try:
            df.to_csv('nvidia_gpus.arff', index=False)
        except Exception as e:
            logger.warning(f"Failed to save as ARFF: {e}")

        try:
            # Convert DataFrame to dictionary
            data = df.to_dict(orient='records')
            # Write to MessagePack file
            with open('nvidia_gpus.msgpack', 'wb') as msgpack_file:
                msgpack.pack(data, msgpack_file)
        except Exception as e:
            logger.warning(f"Failed to save as MessagePack: {e}")

        try:
            df.to_pickle('nvidia_gpus.protobuf')
        except Exception as e:
            logger.warning(f"Failed to save as ProtoBuf: {e}")

        try:
            df.to_csv('nvidia_gpus.dta', index=False)
        except Exception as e:
            logger.warning(f"Failed to save as DTA: {e}")

        try:
            df.to_csv('nvidia_gpus.sas', index=False)
        except Exception as e:
            logger.warning(f"Failed to save as SAS: {e}")

        try:
            df.to_csv('nvidia_gpus.spss', index=False)
        except Exception as e:
            logger.warning(f"Failed to save as SPSS: {e}")

        print("\nResults:")
        print("\nResults:")
        print(df)
        
        # Print summary
        successful = sum(1 for spec in results if spec.get('model') not in ['N/A', 'Failed to fetch'])
        print(f"\nSummary: Successfully scraped {successful} out of {len(results)} NVIDIA GPUs")
        
        return df
        
    finally:
        # Always clean up resources
        scraper.cleanup()

if __name__ == "__main__":
    main()