app.py

import time
import logging
import argparse
import os
import json
import random
import re
import uuid
from collections import defaultdict
from datetime import datetime
from typing import List, Dict, Any, Optional, Union, Tuple

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.common.keys import Keys
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions import (
    TimeoutException, NoSuchElementException, WebDriverException
)
import gradio as gr
import pandas as pd

# Setup logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)
logger = logging.getLogger(__name__)


# Predefined advertisers list
ADVERTISERS = [
    {"id": "AR10051102910143528961", "name": "Theory Sabers"},
    {"id": "AR12645693856247971841", "name": "Artsabers"},
    {"id": "AR07257050693515608065", "name": "bmlightsabers"},
    {"id": "AR01506694249926623233", "name": "Padawan Outpost Ltd"},
    {"id": "AR10584025853845307393", "name": "GalaxySabers"},
    {"id": "AR16067963414479110145", "name": "nsabers"},
    {"id": "AR12875519274243850241", "name": "es-sabers"},
    {"id": "AR05144647067079016449", "name": "Ultra Sabers"},
    {"id": "AR15581800501283389441", "name": "SuperNeox"},
    {"id": "AR06148907109187584001", "name": "Sabertrio"}
]


#####################################
### FACEBOOK SCRAPER SECTION #######
#####################################

# Constants for Facebook Scraper
FB_DEFAULT_TIMEOUT = 60  # seconds
FB_MIN_WAIT_TIME = 1  # minimum seconds for random waits
FB_MAX_WAIT_TIME = 3  # maximum seconds for random waits
FB_MAX_SCROLL_ATTEMPTS = 5  # maximum number of scroll attempts
FB_SELECTOR_HISTORY_FILE = "fb_selector_stats.json"  # File to store selector success stats

# User agents for rotation
USER_AGENTS = [
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.0 Safari/605.1.15",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36 Edg/122.0.0.0",
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:109.0) Gecko/20100101 Firefox/118.0"
]

# Viewport sizes for randomization
VIEWPORT_SIZES = [
    (1366, 768),
    (1920, 1080),
    (1536, 864),
    (1440, 900)
]


class SelectorStats:
    """Class to track and optimize selector performance"""

    def __init__(self, file_path=FB_SELECTOR_HISTORY_FILE):
        self.file_path = file_path
        self.stats = self._load_stats()

    def _load_stats(self) -> Dict:
        """Load stats from file or initialize if not exists"""
        if os.path.exists(self.file_path):
            try:
                with open(self.file_path, 'r') as f:
                    return json.load(f)
            except (json.JSONDecodeError, IOError) as e:
                logger.warning(f"Error loading selector stats: {e}, initializing new stats")

        # Initialize structure for platform stats
        return {
            "facebook": {"selectors": {}, "last_updated": datetime.now().isoformat()}
        }

    def update_selector_success(self, selector: str, count: int = 1) -> None:
        """Record successful use of a selector"""
        platform = "facebook"  # Only using Facebook for this version
        if platform not in self.stats:
            self.stats[platform] = {"selectors": {}, "last_updated": datetime.now().isoformat()}

        if selector not in self.stats[platform]["selectors"]:
            self.stats[platform]["selectors"][selector] = {"successes": 0, "attempts": 0}

        self.stats[platform]["selectors"][selector]["successes"] += count
        self.stats[platform]["selectors"][selector]["attempts"] += 1
        self.stats[platform]["last_updated"] = datetime.now().isoformat()

        # Save after each update
        self._save_stats()

    def update_selector_attempt(self, selector: str) -> None:
        """Record attempt to use a selector regardless of success"""
        platform = "facebook"  # Only using Facebook for this version
        if platform not in self.stats:
            self.stats[platform] = {"selectors": {}, "last_updated": datetime.now().isoformat()}

        if selector not in self.stats[platform]["selectors"]:
            self.stats[platform]["selectors"][selector] = {"successes": 0, "attempts": 0}

        self.stats[platform]["selectors"][selector]["attempts"] += 1
        self.stats[platform]["last_updated"] = datetime.now().isoformat()

        # Don't save on every attempt to reduce disk I/O

    def get_best_selectors(self, min_attempts: int = 3, max_count: int = 10) -> List[str]:
        """Get the best performing selectors for Facebook"""
        platform = "facebook"  # Only using Facebook for this version
        if platform not in self.stats:
            return []

        selectors = []
        for selector, data in self.stats[platform]["selectors"].items():
            if data["attempts"] >= min_attempts:
                success_rate = data["successes"] / data["attempts"] if data["attempts"] > 0 else 0
                selectors.append((selector, success_rate))

        # Sort by success rate (descending)
        selectors.sort(key=lambda x: x[1], reverse=True)

        # Return top N selectors
        return [s[0] for s in selectors[:max_count]]

    def _save_stats(self) -> None:
        """Save stats to file"""
        try:
            with open(self.file_path, 'w') as f:
                json.dump(self.stats, f, indent=2)
        except IOError as e:
            logger.error(f"Error saving selector stats: {e}")


class FacebookAdsScraper:
    def __init__(self, headless=True, debug_mode=False):
        """Initialize the ads scraper with browser configuration"""
        self.debug_mode = debug_mode
        self.headless = headless
        self.driver = self._setup_driver(headless)
        # Initialize selector stats tracker
        self.selector_stats = SelectorStats()
        # Track navigation history for smart retry
        self.navigation_history = []
        # Track success/failure for self-healing
        self.success_rate = defaultdict(lambda: {"success": 0, "failure": 0})
        # Generate a session ID for this scraping session
        self.session_id = str(uuid.uuid4())[:8]

    def _setup_driver(self, headless):
        """Set up and configure the Chrome WebDriver with anti-detection measures"""
        chrome_options = Options()
        if headless:
            chrome_options.add_argument("--headless")

        # Select a random user agent
        user_agent = random.choice(USER_AGENTS)
        chrome_options.add_argument(f"--user-agent={user_agent}")
        logger.info(f"Using user agent: {user_agent}")

        # Select a random viewport size
        viewport_width, viewport_height = random.choice(VIEWPORT_SIZES)
        chrome_options.add_argument(f"--window-size={viewport_width},{viewport_height}")
        logger.info(f"Using viewport size: {viewport_width}x{viewport_height}")

        # Add common options to avoid detection
        chrome_options.add_argument("--disable-blink-features=AutomationControlled")
        chrome_options.add_argument("--no-sandbox")
        chrome_options.add_argument("--disable-dev-shm-usage")
        chrome_options.add_argument("--disable-gpu")
        chrome_options.add_argument("--start-maximized")
        chrome_options.add_argument("--enable-unsafe-swiftshader")

        # Performance improvements
        chrome_options.add_argument("--disable-extensions")
        chrome_options.add_argument("--disable-notifications")
        chrome_options.add_argument("--blink-settings=imagesEnabled=true")

        # Add experimental options to avoid detection
        chrome_options.add_experimental_option("excludeSwitches", ["enable-automation"])
        chrome_options.add_experimental_option("useAutomationExtension", False)

        # Additional preferences to improve performance
        chrome_options.add_experimental_option("prefs", {
            "profile.default_content_setting_values.notifications": 2,
            "profile.managed_default_content_settings.images": 1,
            "profile.managed_default_content_settings.cookies": 1,
            # Add some randomness to the profile
            "profile.default_content_setting_values.plugins": random.randint(1, 3),
            "profile.default_content_setting_values.popups": random.randint(1, 2)
        })

        try:
            # Try to create driver with service for newer Selenium versions
            service = Service()
            driver = webdriver.Chrome(service=service, options=chrome_options)

            # Execute CDP commands to avoid detection (works in newer Chrome versions)
            driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument", {
                "source": """

                    Object.defineProperty(navigator, 'webdriver', {

                        get: () => undefined

                    });



                    // Overwrite the languages with random order

                    Object.defineProperty(navigator, 'languages', {

                        get: () => ['en-US', 'en', 'de'].sort(() => 0.5 - Math.random())

                    });



                    // Modify plugins length

                    Object.defineProperty(navigator, 'plugins', {

                        get: () => {

                            // Randomize plugins length between 3 and 7

                            const len = Math.floor(Math.random() * 5) + 3;

                            const plugins = { length: len };

                            for (let i = 0; i < len; i++) {

                                plugins[i] = {

                                    name: ['Flash', 'Chrome PDF Plugin', 'Native Client', 'Chrome PDF Viewer'][Math.floor(Math.random() * 4)],

                                    filename: ['internal-pdf-viewer', 'mhjfbmdgcfjbbpaeojofohoefgiehjai', 'internal-nacl-plugin'][Math.floor(Math.random() * 3)]

                                };

                            }

                            return plugins;

                        }

                    });

                """
            })
        except TypeError:
            # Fallback for older Selenium versions
            driver = webdriver.Chrome(options=chrome_options)
        except Exception as e:
            # If there's an issue with CDP, continue anyway
            logger.warning(f"CDP command failed, continuing: {e}")
            driver = webdriver.Chrome(options=chrome_options)

        # Set default timeout
        driver.set_page_load_timeout(FB_DEFAULT_TIMEOUT)
        return driver

    def random_wait(self, min_time=None, max_time=None):
        """Wait for a random amount of time to simulate human behavior"""
        min_time = min_time or FB_MIN_WAIT_TIME
        max_time = max_time or FB_MAX_WAIT_TIME
        wait_time = random.uniform(min_time, max_time)
        time.sleep(wait_time)
        return wait_time

    def human_like_scroll(self, scroll_attempts=None):
        """Scroll down the page in a human-like way"""
        attempts = scroll_attempts or random.randint(3, FB_MAX_SCROLL_ATTEMPTS)

        # Get page height before scrolling
        initial_height = self.driver.execute_script("return document.body.scrollHeight")

        for i in range(attempts):
            # Calculate a random scroll amount (25-90% of viewport)
            scroll_percent = random.uniform(0.25, 0.9)
            viewport_height = self.driver.execute_script("return window.innerHeight")
            scroll_amount = int(viewport_height * scroll_percent)

            # Scroll with a random speed
            scroll_steps = random.randint(5, 15)
            current_position = self.driver.execute_script("return window.pageYOffset")
            target_position = current_position + scroll_amount

            for step in range(scroll_steps):
                # Calculate next position with easing
                t = (step + 1) / scroll_steps
                # Ease in-out function
                factor = t * t * (3.0 - 2.0 * t)
                next_position = current_position + (target_position - current_position) * factor
                self.driver.execute_script(f"window.scrollTo(0, {next_position})")
                time.sleep(random.uniform(0.01, 0.05))

            # Occasionally pause longer as if reading content
            if random.random() < 0.3:  # 30% chance to pause
                self.random_wait(1.5, 3.5)
            else:
                self.random_wait(0.5, 1.5)

            # Log progress
            logger.info(f"Human-like scroll {i + 1}/{attempts} completed")

            # Check if we've reached the bottom of the page
            new_height = self.driver.execute_script("return document.body.scrollHeight")
            if new_height == initial_height and i > 1:
                # We haven't loaded new content after a couple of scrolls
                # Do one big scroll to the bottom to trigger any lazy loading
                self.driver.execute_script("window.scrollTo(0, document.body.scrollHeight)")
                self.random_wait()
            initial_height = new_height

    def simulate_human_behavior(self):
        """Simulate random human-like interactions with the page"""
        # Random chance to move the mouse around
        if random.random() < 0.7:  # 70% chance
            try:
                # Find a random element to hover over
                elements = self.driver.find_elements(By.CSS_SELECTOR, "a, button, input, div")
                if elements:
                    element = random.choice(elements)
                    ActionChains(self.driver).move_to_element(element).perform()
                    self.random_wait(0.2, 1.0)
            except:
                # Ignore any errors, this is just for randomness
                pass

        # Random chance to click somewhere non-interactive
        if random.random() < 0.2:  # 20% chance
            try:
                # Find a safe area to click (like a paragraph or heading)
                safe_elements = self.driver.find_elements(By.CSS_SELECTOR, "p, h1, h2, h3, h4, span")
                if safe_elements:
                    safe_element = random.choice(safe_elements)
                    ActionChains(self.driver).move_to_element(safe_element).click().perform()
                    self.random_wait(0.2, 1.0)
            except:
                # Ignore any errors, this is just for randomness
                pass

    def check_headless_visibility(self):
        """

        Check if elements are visible in headless mode

        Returns True if everything is working properly

        """
        if not self.headless:
            # If not in headless mode, no need to check
            return True

        logger.info("Performing headless visibility check...")

        # Use a simpler page for testing interactivity
        test_url = "https://www.example.com"
        try:
            self.driver.get(test_url)

            # Just check if the page loads at all - don't try to interact with elements
            WebDriverWait(self.driver, 10).until(
                EC.presence_of_element_located((By.TAG_NAME, "body"))
            )

            logger.info("Headless check passed: Page loaded successfully")
            return True

        except Exception as e:
            logger.error(f"Headless check failed: {e}")

            # Try switching to non-headless mode
            logger.info("Switching to non-headless mode...")
            self.driver.quit()
            self.headless = False
            self.driver = self._setup_driver(headless=False)

            return True  # Continue without rechecking

    def fetch_facebook_ads(self, query):
        """Fetch ads from Facebook's Ad Library with anti-detection measures"""
        ads_data = []
        base_url = "https://www.facebook.com/ads/library/"

        logger.info(f"Fetching Facebook ads for {query}")

        try:
            # Add some randomness to URL parameters
            params = {
                "active_status": "all",
                "ad_type": "all",
                "country": "ALL",
                "q": query,
                # Random parameters to avoid fingerprinting
                "_": int(time.time() * 1000),
                "session_id": self.session_id
            }

            # Construct URL with parameters
            url = base_url + "?" + "&".join(f"{k}={v}" for k, v in params.items())
            logger.info(f"Navigating to Facebook URL: {url}")

            # Navigate to the URL
            self.driver.get(url)

            # Wait for page to initially load
            try:
                WebDriverWait(self.driver, FB_DEFAULT_TIMEOUT).until(
                    EC.any_of(
                        EC.presence_of_element_located((By.CSS_SELECTOR, "div[role='main']")),
                        EC.presence_of_element_located((By.TAG_NAME, "body"))
                    )
                )
            except TimeoutException:
                logger.warning("Timeout waiting for Facebook page to load initially, continuing anyway")

            # Human-like scrolling to trigger lazy loading
            self.human_like_scroll()

            # Simulate human behavior
            self.simulate_human_behavior()

            # Save debug data at this point
            if self.debug_mode:
                self._save_debug_data("facebook_after_scroll", query)

            # Find ad elements using self-healing selectors
            ad_elements = self._find_facebook_ad_elements()

            if not ad_elements:
                logger.info("No Facebook ads found")

                if self.debug_mode:
                    self._save_debug_data("facebook_no_ads", query)

                # Return placeholder data as fallback
                return self._generate_placeholder_facebook_data(query)

            # Process the found ad elements
            for i, ad in enumerate(ad_elements[:10]):  # Limit to 10 ads for performance
                try:
                    ad_data = {
                        "platform": "Facebook",
                        "query": query,
                        "timestamp": datetime.now().isoformat(),
                        "index": i + 1,
                        "session_id": self.session_id
                    }

                    # Extract data using smarter methods
                    full_text = ad.text.strip()

                    # Log first ad text for debugging
                    if i == 0:
                        logger.info(f"First Facebook ad full text (first 150 chars): {full_text[:150]}...")

                    # Smart data extraction
                    extracted_data = self._extract_facebook_ad_data(ad, full_text)

                    # Merge extracted data
                    ad_data.update(extracted_data)

                    # Add fallback values if needed
                    if "advertiser" not in ad_data or not ad_data["advertiser"]:
                        ad_data["advertiser"] = "Unknown Advertiser"
                    if "text" not in ad_data or not ad_data["text"]:
                        ad_data["text"] = "Ad content not available"

                    ads_data.append(ad_data)

                except Exception as e:
                    logger.warning(f"Error processing Facebook ad {i + 1}: {e}")

            return ads_data if ads_data else self._generate_placeholder_facebook_data(query)

        except Exception as e:
            logger.error(f"Error fetching Facebook ads: {e}")

            if self.debug_mode:
                self._save_debug_data("facebook_error", query)

            return self._generate_placeholder_facebook_data(query)

    def _find_facebook_ad_elements(self):
        """Find Facebook ad elements using a self-healing selector strategy"""
        # Historical best performers
        historical_best = self.selector_stats.get_best_selectors()

        # Base selectors
        base_selectors = [
            "div[class*='_7jvw']",
            "div[data-testid='ad_library_card']",
            "div[class*='AdLibraryCard']",
            "div.AdLibraryCard",
            "div[class*='adCard']",
            "div[class*='ad_card']"
        ]

        # Combine selectors, prioritizing historical best
        combined_selectors = historical_best + [s for s in base_selectors if s not in historical_best]

        # Try each selector
        for selector in combined_selectors:
            try:
                # Record attempt
                self.selector_stats.update_selector_attempt(selector)

                elements = self.driver.find_elements(By.CSS_SELECTOR, selector)
                if elements:
                    logger.info(f"Found {len(elements)} Facebook ads using selector: {selector}")

                    # Record success
                    self.selector_stats.update_selector_success(selector, len(elements))

                    return elements
            except Exception as e:
                logger.debug(f"Facebook selector {selector} failed: {e}")

        # No elements found with standard selectors, try a more aggressive approach
        try:
            # Look for text patterns that typically appear in ads
            patterns = [
                "//div[contains(., 'Library ID:')]",
                "//div[contains(., 'Sponsored')]",
                "//div[contains(., 'Active')][contains(., 'Library ID')]",
                "//div[contains(., 'Inactive')][contains(., 'Library ID')]"
            ]

            for pattern in patterns:
                elements = self.driver.find_elements(By.XPATH, pattern)
                if elements:
                    ad_containers = []
                    for element in elements:
                        try:
                            # Try to find containing card by navigating up
                            container = element
                            for _ in range(5):  # Try up to 5 levels up
                                if container.get_attribute("class") and "card" in container.get_attribute(
                                        "class").lower():
                                    ad_containers.append(container)
                                    break
                                container = container.find_element(By.XPATH, "..")
                        except:
                            continue

                    if ad_containers:
                        logger.info(f"Found {len(ad_containers)} Facebook ads using text pattern approach")

                        # Record this special method
                        self.selector_stats.update_selector_success("text_pattern_method", len(ad_containers))

                        return ad_containers
        except Exception as e:
            logger.debug(f"Facebook text pattern approach failed: {e}")

        return []

    def _extract_facebook_ad_data(self, ad_element, full_text):
        """Extract data from Facebook ad using multiple intelligent methods"""
        extracted_data = {}

        # Process text content if available
        if full_text:
            # Split into lines
            lines = full_text.split('\n')

            # Check for status (Active/Inactive)
            if lines and lines[0] in ["Active", "Inactive"]:
                extracted_data["status"] = lines[0]

                # Look for advertiser - typically after "See ad details"
                for i, line in enumerate(lines):
                    if "See ad details" in line or "See summary details" in line:
                        if i + 1 < len(lines):
                            extracted_data["advertiser"] = lines[i + 1].strip()
                            break
            else:
                # First line is likely the advertiser
                if lines:
                    extracted_data["advertiser"] = lines[0].strip()

            # Extract ad content
            # Look for patterns to determine content boundaries
            content_start_idx = -1
            content_end_idx = len(lines)

            # Find where "Sponsored" appears
            for i, line in enumerate(lines):
                if "Sponsored" in line:
                    content_start_idx = i + 1
                    break

            # If no "Sponsored" found, look for advertiser + status
            if content_start_idx == -1:
                # Skip metadata lines
                metadata_patterns = [
                    "Library ID:",
                    "Started running on",
                    "Platforms",
                    "Open Drop-down",
                    "See ad details",
                    "See summary details",
                    "This ad has multiple versions"
                ]

                for i, line in enumerate(lines):
                    if any(pattern in line for pattern in metadata_patterns):
                        continue

                    if i > 0:  # Skip first line (advertiser)
                        content_start_idx = i
                        break

            # Find where UI elements start
            ui_elements = [
                "Like", "Comment", "Share", "Learn More", "Shop Now",
                "Sign Up", "Visit Instagram profile", "See More"
            ]

            for i, line in enumerate(lines):
                # Skip lines before content start
                if i <= content_start_idx:
                    continue

                if any(ui in line for ui in ui_elements):
                    content_end_idx = i
                    break

            # Extract content between boundaries
            if content_start_idx != -1 and content_start_idx < content_end_idx:
                content_lines = lines[content_start_idx:content_end_idx]
                extracted_data["text"] = "\n".join(content_lines).strip()

        # If text extraction failed, try element-based approaches
        if "text" not in extracted_data or not extracted_data["text"]:
            facebook_text_selectors = [
                "div[data-ad-preview='message']",  # Direct message container
                "div[class*='_7jy6']",  # Known ad text container
                "div[data-testid='ad-creative-text']",  # Test ID for ad text
                "div[class*='_38ki']",  # Another text container
                "span[class*='_7oe']",  # Text span
                "div.text_exposed_root"  # Exposed text root
            ]

            for selector in facebook_text_selectors:
                try:
                    elements = ad_element.find_elements(By.CSS_SELECTOR, selector)
                    text_content = " ".join([e.text.strip() for e in elements if e.text.strip()])
                    if text_content:
                        extracted_data["text"] = text_content
                        break
                except:
                    pass

        # If advertiser extraction failed, try element-based approaches
        if "advertiser" not in extracted_data or not extracted_data["advertiser"]:
            facebook_advertiser_selectors = [
                "span[class*='fsl']",  # Facebook specific large text class
                "a[aria-label*='profile']",  # Profile links often contain advertiser name
                "h4",  # Often contains advertiser name
                "div[class*='_8jh5']",  # Known advertiser class
                "a[role='link']",  # Links are often advertiser names
                "div[class*='_3qn7']",  # Another known advertiser container
                "div[class*='_7jvw'] a",  # Links within the ad card
            ]

            for selector in facebook_advertiser_selectors:
                try:
                    elements = ad_element.find_elements(By.CSS_SELECTOR, selector)
                    for element in elements:
                        text = element.text.strip()
                        if text and len(text) < 50:  # Advertiser names are usually short
                            extracted_data["advertiser"] = text
                            break
                    if "advertiser" in extracted_data and extracted_data["advertiser"]:
                        break
                except:
                    pass

        return extracted_data

    def _generate_placeholder_facebook_data(self, query):
        """Generate placeholder Facebook ad data when real ads cannot be scraped"""
        logger.info(f"Returning placeholder Facebook ad data for query: {query}")
        return [
            {
                "platform": "Facebook",
                "query": query,
                "advertiser": "Placeholder Advertiser 1",
                "text": f"This is a placeholder ad for {query} since no actual ads could be scraped.",
                "timestamp": datetime.now().isoformat(),
                "index": 1,
                "is_placeholder": True,
                "session_id": self.session_id
            },
            {
                "platform": "Facebook",
                "query": query,
                "advertiser": "Placeholder Advertiser 2",
                "text": f"Another placeholder ad for {query}. Please check your scraping settings.",
                "timestamp": datetime.now().isoformat(),
                "index": 2,
                "is_placeholder": True,
                "session_id": self.session_id
            }
        ]

    def _save_debug_data(self, prefix, query):
        """Save debugging data for investigation"""
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        debug_dir = "debug_data"

        if not os.path.exists(debug_dir):
            os.makedirs(debug_dir)

        # Save screenshot
        screenshot_path = f"{debug_dir}/{prefix}_{query}_{timestamp}.png"
        self.driver.save_screenshot(screenshot_path)
        logger.info(f"Saved debug screenshot to {screenshot_path}")

        # Save HTML
        html_path = f"{debug_dir}/{prefix}_{query}_{timestamp}.html"
        with open(html_path, "w", encoding="utf-8") as f:
            f.write(self.driver.page_source)
        logger.info(f"Saved debug HTML to {html_path}")

        # Save sample of first ad structure if available
        try:
            ad_elements = self.driver.find_elements(By.CSS_SELECTOR, "div[class*='_7jvw']")
            if ad_elements:
                first_ad = ad_elements[0]
                # Get sample HTML structure
                first_ad_html = first_ad.get_attribute('outerHTML')
                # Save first ad HTML
                sample_path = f"{debug_dir}/{prefix}_sample_ad_{timestamp}.html"
                with open(sample_path, "w", encoding="utf-8") as f:
                    f.write(first_ad_html)
                logger.info(f"Saved sample ad HTML to {sample_path}")

                # Log the text structure
                logger.info(f"Sample ad text structure: {first_ad.text[:300]}...")
        except Exception as e:
            logger.error(f"Error saving ad sample: {e}")

    def close(self):
        """Close the WebDriver and save stats"""
        if self.driver:
            self.driver.quit()

        # Save selector stats one last time
        self.selector_stats._save_stats()


# Facebook Gradio Interface Function
def fetch_facebook_ads(query):
    """Fetch Facebook ads only for Gradio interface"""
    logger.info(f"Processing Facebook ad search for: {query}")

    scraper = FacebookAdsScraper(headless=True, debug_mode=True)

    # Perform headless check first
    visibility_ok = scraper.check_headless_visibility()
    if not visibility_ok:
        logger.warning("Headless visibility check failed, results may be affected")

    # Fetch ads from Facebook
    facebook_ads = scraper.fetch_facebook_ads(query)

    # Format for display
    formatted_results = []
    for ad in facebook_ads:
        formatted_ad = f"Platform: {ad['platform']}\n"

        # Include status if available
        if 'status' in ad:
            formatted_ad += f"Status: {ad['status']}\n"

        formatted_ad += f"Advertiser: {ad['advertiser']}\n"

        # Format ad text with word wrapping
        text_lines = []
        if ad['text'] and ad['text'] != "Ad content not available":
            # Split long text into readable chunks
            words = ad['text'].split()
            current_line = ""
            for word in words:
                if len(current_line) + len(word) + 1 <= 80:  # 80 chars per line
                    current_line += (" " + word if current_line else word)
                else:
                    text_lines.append(current_line)
                    current_line = word
            if current_line:
                text_lines.append(current_line)

            formatted_text = "\n".join(text_lines)
        else:
            formatted_text = ad['text']

        formatted_ad += f"Ad Text: {formatted_text}\n"
        formatted_ad += f"Timestamp: {ad['timestamp']}\n"
        if ad.get('is_placeholder', False):
            formatted_ad += "[THIS IS PLACEHOLDER DATA]\n"
        formatted_ad += "-" * 50
        formatted_results.append(formatted_ad)

    scraper.close()

    return "\n\n".join(formatted_results) if formatted_results else "No Facebook ads found for your query."


# Create a function to save ads to JSON
def save_ads_to_json(ads, query):
    """Save ads to a JSON file"""
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    filename = f"facebook_ads_{query.replace(' ', '_')}_{timestamp}.json"

    try:
        with open(filename, 'w', encoding='utf-8') as f:
            json.dump(ads, f, indent=2, ensure_ascii=False)
        logger.info(f"Saved ads to {filename}")
        return filename
    except Exception as e:
        logger.error(f"Error saving ads to JSON: {e}")
        return None


#####################################
### GOOGLE ADS SCRAPER SECTION #####
#####################################

# Constants for Google Ads Scraper
MAX_ADS_DEFAULT = 5

# Import the actual GoogleAds class and regions
try:
    from GoogleAds.main import GoogleAds, show_regions_list
    from GoogleAds.regions import Regions

    USING_ACTUAL_GOOGLE_ADS = True
    logger.info("Successfully imported GoogleAds module")
except ImportError as e:
    # Fallback to mock implementation if module is missing
    logger.warning(f"GoogleAds module not found: {e}. Using mock implementation.")
    USING_ACTUAL_GOOGLE_ADS = False

    # Mock Regions dictionary - only used if real module fails to import
    Regions = {
        "GB": {"Region": "United Kingdom"}
    }


    def show_regions_list():
        """Mock function - only used if real module fails to import"""
        return [("GB", "United Kingdom"), ("US", "United States")]


    # Mock GoogleAds class - only used if real module fails to import
    class GoogleAds:
        def __init__(self, region="GB"):
            self.region = region
            logger.warning(f"Using MOCK GoogleAds implementation with region: {region}")
            logger.warning("Please install the GoogleAds module for actual data")

        def creative_search_by_advertiser_id(self, advertiser_id, count=5):
            # Mock implementation - only used if real module fails to import
            logger.warning(f"MOCK: Searching for creatives from advertiser {advertiser_id}")
            return [f"creative_{i}_{advertiser_id}" for i in range(min(count, 3))]

        def get_detailed_ad(self, advertiser_id, creative_id):
            # Mock implementation - only used if real module fails to import
            logger.warning(f"MOCK: Getting details for creative {creative_id}")

            # Find advertiser name
            advertiser_name = "Unknown"
            for adv in ADVERTISERS:
                if adv["id"] == advertiser_id:
                    advertiser_name = adv["name"]
                    break

            # Return mock ad details
            return {
                "Ad Format": "Text",
                "Advertiser": advertiser_name,
                "Advertiser Name": advertiser_name,
                "Ad Title": f"MOCK DATA - INSTALL GOOGLE ADS MODULE",
                "Ad Body": f"This is MOCK data because the GoogleAds module is not installed. Please install the proper module.",
                "Last Shown": datetime.now().strftime("%Y-%m-%d"),
                "Creative Id": creative_id,
                "Ad Link": "#"
            }


def clean_ad_text(text):
    """Clean ad text by removing special characters and formatting issues."""
    if text is None or not isinstance(text, str):
        return ""

    # Remove Unicode special characters often found in Google ads data
    cleaned = text.replace('â¦', '')  # Opening symbol
    cleaned = cleaned.replace('â©', '')  # Closing symbol
    cleaned = cleaned.replace('<dynamically generated based on landing page content>', '[Dynamic Content]')

    # Remove any other strange characters that might appear
    cleaned = re.sub(r'[^\x00-\x7F]+', '', cleaned)

    return cleaned.strip()


def get_regions_list():
    """Get a limited list of regions - only GB and anywhere."""
    regions = [
        ("anywhere", "Global (anywhere)"),
        ("GB", f"{Regions['GB']['Region']} (GB)")
    ]
    return regions


def search_by_advertiser_id(advertiser_id: str, max_ads=MAX_ADS_DEFAULT, region="GB", progress=gr.Progress(),

                            provided_name=None) -> Tuple[
    str, Optional[pd.DataFrame], Optional[Dict]]:

    try:
        progress(0, desc="Initializing scraper...")

        # Fix for region handling
        region_val = region
        if isinstance(region, tuple) and len(region) > 0:
            region_val = region[0]

        # Ensure 'anywhere' is handled correctly
        if region_val == "Global (anywhere)" or "anywhere" in str(region_val).lower():
            region_val = "anywhere"

        # Initialize the Google Ads scraper
        scraper = GoogleAds(region=region_val)

        progress(0.2, desc=f"Fetching ads for advertiser ID: {advertiser_id}")

        # Get creative IDs for this advertiser
        creative_ids = scraper.creative_search_by_advertiser_id(advertiser_id, count=max_ads)

        if not creative_ids:
            return f"No ads found for advertiser ID: {advertiser_id}", None, None

        progress(0.3, desc=f"Found {len(creative_ids)} ads. Fetching details...")

        # Fetch detailed information for each ad
        ads_data = []
        ad_formats = {}

        for i, creative_id in enumerate(creative_ids):
            progress_val = 0.3 + (0.7 * (i / len(creative_ids)))
            progress(progress_val, desc=f"Processing ad {i + 1}/{len(creative_ids)}")

            try:
                ad_details = scraper.get_detailed_ad(advertiser_id, creative_id)

                # Fix encoding issues for Ad Title and Ad Body fields
                if 'Ad Title' in ad_details:
                    ad_details['Ad Title'] = clean_ad_text(ad_details['Ad Title'])

                if 'Ad Body' in ad_details:
                    ad_details['Ad Body'] = clean_ad_text(ad_details['Ad Body'])

                ads_data.append(ad_details)

                # Count ad formats
                ad_format = ad_details.get("Ad Format", "Unknown")
                ad_formats[ad_format] = ad_formats.get(ad_format, 0) + 1

                # Brief pause to avoid overwhelming the server
                time.sleep(0.2)
            except Exception as e:
                print(f"Error fetching details for ad {creative_id}: {e}")

        if not ads_data:
            return f"Retrieved creative IDs but couldn't fetch ad details for advertiser ID: {advertiser_id}", None, None

        # Create a DataFrame for display
        df = pd.DataFrame(ads_data)

        # Generate summary info
        timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")

        # Use provided name if available, otherwise try to determine from predefined list or ad data
        advertiser_name = "Unknown"

        # First, use the provided name if it exists
        if provided_name:
            advertiser_name = provided_name
        else:
            # Check our predefined list
            for adv in ADVERTISERS:
                if adv["id"] == advertiser_id:
                    advertiser_name = adv["name"]
                    break

            # If still unknown, try to get from the ad data
            if advertiser_name == "Unknown" and ads_data and len(ads_data) > 0:
                # The field might be "Advertiser" or "Advertiser Name" depending on the version
                for field in ["Advertiser", "Advertiser Name", "advertiser_name"]:
                    if field in ads_data[0]:
                        advertiser_name = ads_data[0][field]
                        break

        summary = {
            'advertiser_id': advertiser_id,
            'advertiser_name': advertiser_name,
            'ads_count': len(ads_data),
            'timestamp': timestamp,
            'region': region_val,
            'ad_formats': ad_formats
        }

        # Find the earliest and latest ad
        dates = []
        for ad in ads_data:
            # The field might be "Last Shown" or "last_shown_date" depending on the version
            for field in ["Last Shown", "last_shown_date"]:
                if field in ad and ad[field]:
                    dates.append(ad[field])
                    break

        if dates:
            summary['earliest_ad'] = min(dates)
            summary['latest_ad'] = max(dates)

        # Don't save the data, just prepare the summary info
        summary = {
            'advertiser_id': advertiser_id,
            'advertiser_name': advertiser_name,
            'ads_count': len(ads_data),
            'timestamp': timestamp,
            'region': region_val,
            'ad_formats': ad_formats
        }

        # Find the earliest and latest ad
        dates = []
        for ad in ads_data:
            # The field might be "Last Shown" or "last_shown_date" depending on the version
            for field in ["Last Shown", "last_shown_date"]:
                if field in ad and ad[field]:
                    dates.append(ad[field])
                    break

        if dates:
            summary['earliest_ad'] = min(dates)
            summary['latest_ad'] = max(dates)

        success_message = (
            f"Found {len(ads_data)} ads for advertiser '{advertiser_name}' (ID: {advertiser_id})."
        )

        progress(1.0, desc="Complete!")
        return success_message, df, summary

    except Exception as e:
        error_message = f"Error searching for advertiser ID: {str(e)}"
        return error_message, None, None


def process_advertiser_search(advertiser_selection, region, max_ads, progress=gr.Progress()):
    """Handle the advertiser selection form submission and update the UI."""

    # Extract advertiser ID and name from the selection format "ID: Name"
    if not advertiser_selection:
        return "Please select an advertiser to search", None, None, None

    # Split the selection string to get the ID and name
    parts = advertiser_selection.split(":", 1)
    advertiser_id = parts[0].strip()
    advertiser_name = parts[1].strip() if len(parts) > 1 else "Unknown"

    # Perform the search
    result_message, ads_df, summary_info = search_by_advertiser_id(
        advertiser_id, max_ads, region, progress, advertiser_name
    )

    # Generate analysis if data is available
    analysis_html = analyze_ads(ads_df, summary_info) if ads_df is not None and not ads_df.empty else None

    return result_message, ads_df, analysis_html, summary_info


def analyze_ads(df: pd.DataFrame, summary: Dict) -> str:
    """

    Analyze ads data and generate insights.



    Args:

        df: DataFrame containing ad data

        summary: Dictionary with summary information



    Returns:

        HTML string with analysis results

    """
    if df is None or df.empty or summary is None:
        return "<h3>No data available for analysis</h3>"

    try:
        # Create a simple HTML report with the analysis
        html = f"""

        <div style="font-family: Arial, sans-serif;">

            <h2>{summary.get('advertiser_name', 'Unknown Advertiser')} - Ad Analysis</h2>



            <div style="background-color: #f5f5f5; padding: 15px; border-radius: 5px; margin-bottom: 20px;">

                <h3>Overview</h3>

                <p><b>Advertiser ID:</b> {summary.get('advertiser_id', 'Unknown')}</p>

                <p><b>Total Ads Found:</b> {summary['ads_count']}</p>

                <p><b>Region:</b> {summary['region']}</p>

                <p><b>Data Collected:</b> {summary['timestamp'].replace('_', ' ').replace('-', '/')}</p>



                {f"<p><b>Ad Date Range:</b> {summary.get('earliest_ad')} to {summary.get('latest_ad')}</p>" if 'earliest_ad' in summary else ""}

            </div>



            <div style="display: flex; margin-bottom: 20px;">

                <div style="flex: 1; background-color: #f5f5f5; padding: 15px; border-radius: 5px; margin-right: 10px;">

                    <h3>Ad Format Distribution</h3>

                    <table style="width: 100%; border-collapse: collapse;">

                        <tr style="background-color: #eaeaea;">

                            <th style="text-align: left; padding: 8px; border-bottom: 1px solid #ddd;">Format</th>

                            <th style="text-align: center; padding: 8px; border-bottom: 1px solid #ddd;">Count</th>

                            <th style="text-align: center; padding: 8px; border-bottom: 1px solid #ddd;">Percentage</th>

                        </tr>

        """

        total = sum(summary['ad_formats'].values())
        for format_name, count in summary['ad_formats'].items():
            percentage = (count / total) * 100
            html += f"""

                <tr>

                    <td style="padding: 8px; border-bottom: 1px solid #ddd;">{format_name}</td>

                    <td style="text-align: center; padding: 8px; border-bottom: 1px solid #ddd;">{count}</td>

                    <td style="text-align: center; padding: 8px; border-bottom: 1px solid #ddd;">{percentage:.1f}%</td>

                </tr>

            """

        html += """

                    </table>

                </div>

        """

        # Common words in ad titles
        if 'Ad Title' in df.columns and not df['Ad Title'].isna().all():
            from collections import Counter
            import re

            # Extract words from titles
            all_titles = ' '.join(df['Ad Title'].dropna().astype(str).tolist())
            words = re.findall(r'\b\w+\b', all_titles.lower())

            # Remove common stop words
            stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'with', 'by', 'of', 'is',
                          'are'}
            filtered_words = [word for word in words if word not in stop_words and len(word) > 2]

            # Count word frequencies
            word_counts = Counter(filtered_words).most_common(10)

            if word_counts:
                html += """

                <div style="flex: 1; background-color: #f5f5f5; padding: 15px; border-radius: 5px;">

                    <h3>Most Common Words in Ad Titles</h3>

                    <table style="width: 100%; border-collapse: collapse;">

                        <tr style="background-color: #eaeaea;">

                            <th style="text-align: left; padding: 8px; border-bottom: 1px solid #ddd;">Word</th>

                            <th style="text-align: center; padding: 8px; border-bottom: 1px solid #ddd;">Frequency</th>

                        </tr>

                """

                for word, count in word_counts:
                    html += f"""

                        <tr>

                            <td style="padding: 8px; border-bottom: 1px solid #ddd;">{word}</td>

                            <td style="text-align: center; padding: 8px; border-bottom: 1px solid #ddd;">{count}</td>

                        </tr>

                    """

                html += """

                    </table>

                </div>

                """

        html += """

            </div>



            <h3>SEO & Marketing Insights</h3>

            <div style="background-color: #f5f5f5; padding: 15px; border-radius: 5px; margin-bottom: 20px;">

        """

        # Add general insights
        html += f"""

            <h4>Competitive Intelligence</h4>

            <ul>

                <li>The advertiser has been active in advertising until {summary.get('latest_ad', 'recently')}</li>

                <li>Their ad strategy focuses primarily on {max(summary['ad_formats'].items(), key=lambda x: x[1])[0]} ads</li>

                <li>Consider monitoring changes in their ad frequency and creative strategy over time</li>

            </ul>



            <h4>UK Market Insights</h4>

            <ul>

                <li>The ads were collected for the {summary['region']} market</li>

                <li>Regular monitoring can reveal seasonal UK advertising patterns</li>

                <li>Compare with other regions to identify UK-specific marketing approaches</li>

            </ul>

        """

        html += """

            </div>



            <h3>All Ad Examples</h3>

        """

        # Add example ads (all of them, not just the most recent)
        if not df.empty:
            # Sort by Last Shown date if available
            if 'Last Shown' in df.columns:
                df = df.sort_values(by='Last Shown', ascending=False)

            # Get all ads, not just the top 3
            for i, (_, ad) in enumerate(df.iterrows()):
                html += f"""

                <div style="background-color: #f5f5f5; padding: 15px; border-radius: 5px; margin-bottom: 15px;">

                    <h4>Ad {i + 1}: {ad.get('Creative Id', '')}</h4>

                    <p><b>Format:</b> {ad.get('Ad Format', 'Unknown')}</p>

                    <p><b>Last Shown:</b> {ad.get('Last Shown', 'Unknown')}</p>

                """

                # Display title and body if available
                if 'Ad Title' in ad and pd.notna(ad['Ad Title']) and ad['Ad Title']:
                    html += f"<p><b>Title:</b> {ad['Ad Title']}</p>"

                if 'Ad Body' in ad and pd.notna(ad['Ad Body']) and ad['Ad Body']:
                    body = ad['Ad Body']
                    if len(body) > 150:
                        body = body[:150] + "..."
                    html += f"<p><b>Body:</b> {body}</p>"

                # Display image or video links if available
                if 'Image URL' in ad and pd.notna(ad['Image URL']) and ad['Image URL']:
                    html += f"""<p><img src="{ad['Image URL']}" style="max-width: 300px; max-height: 200px;" /></p>"""

                if 'Ad Link' in ad and pd.notna(ad['Ad Link']) and ad['Ad Link'] and ad.get('Ad Format') != 'Text':
                    html += f"""<p><b>Ad Link:</b> <a href="{ad['Ad Link']}" target="_blank">View Ad</a></p>"""

                html += "</div>"

        html += """

        </div>

        """

        return html

    except Exception as e:
        return f"<h3>Error analyzing data: {str(e)}</h3>"


#####################################
### COMBINED INTERFACE SECTION #####
#####################################

def create_combined_app():
    """Create the combined Gradio interface with Facebook and Google Ads scrapers"""

    # Create dropdown choices for advertiser selection
    advertiser_choices = [f"{adv['id']}: {adv['name']}" for adv in ADVERTISERS]

    with gr.Blocks(title="Combined Ads Transparency Scraper") as app:
        gr.Markdown("# Combined Ads Transparency Scraper")
        gr.Markdown("## Search for ads from Facebook and Google Ads transparency tools")

        # Create tabs for the two different scrapers
        with gr.Tabs() as tabs:
            # Tab 1: Facebook Ad Library Scraper
            with gr.TabItem("Facebook Ad Library"):
                gr.Markdown("### Facebook Ad Library Search")
                gr.Markdown("Search for ads by brand, domain, or keyword")

                with gr.Row():
                    fb_query_input = gr.Textbox(
                        label="Search Query",
                        placeholder="Enter brand, domain or product name",
                        value=""
                    )
                    fb_search_button = gr.Button("Find Facebook Ads", variant="primary")

                fb_results_output = gr.Textbox(label="Search Results", lines=20)
                fb_save_button = gr.Button("Save Results to JSON")
                fb_save_status = gr.Textbox(label="Save Status", lines=1)

                # Define the save function for Facebook
                def save_fb_results(query, results_text):
                    if not results_text or "No Facebook ads found" in results_text:
                        return "No ads to save"

                    # Get the scraper to fetch fresh ads for JSON format
                    scraper = FacebookAdsScraper(headless=True, debug_mode=False)
                    ads = scraper.fetch_facebook_ads(query)
                    scraper.close()

                    # Save to JSON
                    filename = save_ads_to_json(ads, query)
                    if filename:
                        return f"Saved {len(ads)} ads to {filename}"
                    else:
                        return "Error saving ads to JSON"

                # Connect Facebook interface components
                fb_search_button.click(
                    fn=fetch_facebook_ads,
                    inputs=[fb_query_input],
                    outputs=[fb_results_output]
                )

                fb_save_button.click(
                    fn=save_fb_results,
                    inputs=[fb_query_input, fb_results_output],
                    outputs=[fb_save_status]
                )

            # Tab 2: Lightsaber Companies Google Ads Scraper
            with gr.TabItem("Google Ads (Lightsaber Companies)"):
                gr.Markdown("### Lightsaber Companies Ads Transparency Scraper")
                gr.Markdown("View Google Ads data for popular lightsaber companies")

                with gr.Row():
                    with gr.Column(scale=3):
                        advertiser_dropdown = gr.Dropdown(
                            choices=advertiser_choices,
                            label="Select Lightsaber Company",
                            info="Choose a company to view their Google Ads data"
                        )

                        with gr.Row():
                            region_dropdown = gr.Dropdown(
                                choices=get_regions_list(),
                                value="GB",  # UK is the default
                                label="Region",
                                info="Choose between Global or UK"
                            )

                            max_ads_slider = gr.Slider(
                                minimum=1,
                                maximum=10,
                                value=5,
                                step=1,
                                label="Max Ads to Retrieve"
                            )

                        search_button = gr.Button("Search Ads", variant="primary")

                    with gr.Column(scale=2):
                        result_message = gr.Markdown(label="Search Result")

                # Tabs for displaying Google Ads search results
                with gr.Tabs() as google_result_tabs:
                    with gr.Tab("Analysis"):
                        analysis_html = gr.HTML()

                    with gr.Tab("Raw Data"):
                        ads_table = gr.DataFrame()

                # State for storing summary info
                summary_info = gr.State()

                # Connect the Google Ads inputs to the output function
                search_button.click(
                    fn=process_advertiser_search,
                    inputs=[advertiser_dropdown, region_dropdown, max_ads_slider],
                    outputs=[result_message, ads_table, analysis_html, summary_info]
                )

        # About section for the combined app
        with gr.Accordion("About This Tool", open=False):
            gr.Markdown("""

            ## About Combined Ads Transparency Scraper



            This tool combines two different ad transparency scrapers:



            1. **Facebook Ad Library Scraper**: Search for any advertiser's ads on Facebook.

            2. **Google Ads Transparency Scraper**: View ads for popular lightsaber companies.



            ### Technical Details



            - The Facebook scraper uses Selenium WebDriver with anti-detection techniques.

            - The Google Ads scraper leverages the Google Ad Transparency API.

            - Both scrapers include adaptive error handling and fallback mechanisms.



            ### Usage Notes



            - Facebook scraping may take 30-60 seconds to complete

            - Search results are not stored permanently

            - Use the "Save Results" button to save data for later analysis



            **Note**: This tool is intended for research and educational purposes only.

            """)

    return app

    # Main execution


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Combined Ads Transparency Scraper")
    parser.add_argument("--headless", action="store_true", default=True, help="Run in headless mode")
    parser.add_argument("--debug", action="store_true", help="Enable debug mode with extra logging")
    parser.add_argument("--fb-query", type=str, help="Facebook search query to run directly without Gradio")
    parser.add_argument("--google-advertiser", type=str, help="Google Ads advertiser ID to run directly without Gradio")
    parser.add_argument("--save", action="store_true", help="Save results to JSON file when using direct query")

    args = parser.parse_args()

    if args.fb_query:
        # Run direct query mode for Facebook
        scraper = FacebookAdsScraper(headless=args.headless, debug_mode=args.debug)
        scraper.check_headless_visibility()

        facebook_ads = scraper.fetch_facebook_ads(args.fb_query)

        # Display results
        print(f"\nFound {len(facebook_ads)} Facebook ads for '{args.fb_query}'")

        if facebook_ads:
            for i, ad in enumerate(facebook_ads):
                print(f"\n--- Ad {i + 1} ---")
                print(f"Platform: {ad['platform']}")
                if 'status' in ad:
                    print(f"Status: {ad['status']}")
                print(f"Advertiser: {ad['advertiser']}")
                print(f"Text: {ad['text']}")
                if ad.get('is_placeholder', False):
                    print("[THIS IS PLACEHOLDER DATA]")

            # Save to JSON if requested
            if args.save:
                filename = save_ads_to_json(facebook_ads, args.fb_query)
                if filename:
                    print(f"\nSaved {len(facebook_ads)} ads to {filename}")
        else:
            print("No Facebook ads found.")

        scraper.close()

    elif args.google_advertiser:
        # Run direct query mode for Google Ads
        advertiser_id = args.google_advertiser

        # Find advertiser name if it's in our list
        advertiser_name = "Unknown"
        for adv in ADVERTISERS:
            if adv["id"] == advertiser_id:
                advertiser_name = adv["name"]
                break

        print(f"\nSearching for Google Ads from advertiser '{advertiser_name}' (ID: {advertiser_id})")


        # Use a dummy progress object for CLI
        class DummyProgress:
            def __call__(self, value, desc=None):
                if desc:
                    print(f"{desc} ({value * 100:.0f}%)")


        result_message, ads_df, summary_info = search_by_advertiser_id(
            advertiser_id,
            max_ads=5,
            region="GB",
            progress=DummyProgress(),
            provided_name=advertiser_name
        )

        print(f"\n{result_message}")

        if ads_df is not None and not ads_df.empty:
            print("\nFound ads:")
            for i, (_, ad) in enumerate(ads_df.iterrows()):
                print(f"\n--- Ad {i + 1} ---")
                print(f"Format: {ad.get('Ad Format', 'Unknown')}")
                print(f"Title: {ad.get('Ad Title', 'Unknown')}")
                body_text = ad.get('Ad Body', 'Unknown')
                if len(body_text) > 100:
                    body_text = body_text[:100] + "..."
                print(f"Body: {body_text}")
                print(f"Last Shown: {ad.get('Last Shown', 'Unknown')}")
                print(f"Creative ID: {ad.get('Creative Id', 'Unknown')}")
        else:
            print("No Google ads found or error occurred.")

    else:
        # Run Gradio interface
        app = create_combined_app()
        print("Starting Combined Ads Transparency Scraper")
        print("Facebook: Search for any brand or company")
        print("Google Ads: Available lightsaber companies:")
        for adv in ADVERTISERS:
            print(f"  - {adv['name']}")
        app.launch()