import os
import re
import pandas as pd
import requests
from bs4 import BeautifulSoup
import time
import random

# 1. Load your CSV
df = pd.read_csv("fighter_stats.csv")

# List of different user agents to rotate between
USER_AGENTS = [
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36',
    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:89.0) Gecko/20100101 Firefox/89.0',
    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.1 Safari/605.1.15',
    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36 Edg/91.0.864.59',
    'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
]

# List of different accept languages
ACCEPT_LANGUAGES = [
    'en-US,en;q=0.9',
    'en-GB,en;q=0.9',
    'en-CA,en;q=0.9',
    'en-AU,en;q=0.9',
    'en-NZ,en;q=0.9',
]

def get_random_headers():
    """Generate a random set of headers for each request."""
    return {
        'User-Agent': random.choice(USER_AGENTS),
        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
        'Accept-Language': random.choice(ACCEPT_LANGUAGES),
        'Connection': 'keep-alive',
        'Upgrade-Insecure-Requests': '1',
        'Cache-Control': 'max-age=0',
        'Sec-Fetch-Dest': 'document',
        'Sec-Fetch-Mode': 'navigate',
        'Sec-Fetch-Site': 'none',
        'Sec-Fetch-User': '?1',
        'DNT': '1',
    }

# 2. Generate UFC profile URLs
def make_url(name):
    """Turn a fighter name into a UFC athlete URL slug."""
    if not isinstance(name, str) or not name.strip():
        return None
    slug = name.lower().strip()
    slug = re.sub(r"[''']", "", slug)        # Remove apostrophes
    slug = re.sub(r"[^a-z\s-]", "", slug)   # Keep letters, spaces, hyphens
    slug = re.sub(r"\s+", "-", slug)        # Spaces → hyphens
    return f"https://www.ufc.com/athlete/{slug}"

df["UFC_Profile_URL"] = df["name"].apply(make_url)

# 3. Prepare output folder
output_folder = "fighter_images"
os.makedirs(output_folder, exist_ok=True)

# 4. Scrape & download each image
def download_ufc_image(url, counter):
    """Fetch the UFC athlete page, parse out the main profile image, and save it."""
    try:
        # Derive filename from URL slug
        slug = url.rstrip("/").split("/")[-1]
        filename = f"{slug}.jpg"
        path = os.path.join(output_folder, filename)
        
        # Check if image already exists
        if os.path.exists(path):
            print(f"[i] Image already exists for {filename}")
            return
        
        # Get fresh random headers for each request
        headers = get_random_headers()
        
        resp = requests.get(url, headers=headers)
        resp.raise_for_status()
        soup = BeautifulSoup(resp.text, "html.parser")
        img_tag = soup.select_one("div.hero-profile__image-wrap img.hero-profile__image")
        if not img_tag or not img_tag.get("src"):
            print(f"[!] No image found at {url}")
            return
        img_url = img_tag["src"]
        
        # Get fresh random headers for image download
        headers = get_random_headers()
        img_data = requests.get(img_url, headers=headers).content
        with open(path, "wb") as f:
            f.write(img_data)
        print(f"[✓] Saved {filename}")
        
        # Add random delay between 1-3 seconds
        time.sleep(random.uniform(1, 3))
        
        # After every 100 downloads, take a longer break
        if counter % 100 == 0:
            print(f"[i] Taking a 30-second break after {counter} downloads...")
            time.sleep(30)
            
    except Exception as e:
        print(f"[✗] Error at {url}: {e}")

# 5. Iterate and download
counter = 0
for url in df["UFC_Profile_URL"].dropna().unique():
    counter += 1
    download_ufc_image(url, counter)