4G Mobile Proxies for Brand Protection

 import requests
 from bs4 import BeautifulSoup
 import pandas as pd
 import random
 import time
 import tensorflow as tf
 from PIL import Image
 import io
 
 # Your Coronium.io 4G mobile proxy details
 proxy_pool = [
     "http://user:pass@4g-proxy1.coronium.io:8080",
     "http://user:pass@4g-proxy2.coronium.io:8080",
     "http://user:pass@4g-proxy3.coronium.io:8080"
 ]
 
 # Load pre-trained image classification model (e.g., MobileNetV2)
 model = tf.keras.applications.MobileNetV2(weights='imagenet')
 
 def preprocess_image(img):
     img = img.resize((224, 224))
     img = tf.keras.preprocessing.image.img_to_array(img)
     img = tf.keras.applications.mobilenet_v2.preprocess_input(img)
     img = tf.expand_dims(img, 0)
     return img
 
 def collect_brand_data(url, brand_keywords):
     # Select a random 4G mobile proxy
     proxy = {"http": random.choice(proxy_pool), "https": random.choice(proxy_pool)}
     
     try:
         # Send a GET request through the 4G mobile proxy
         response = requests.get(url, proxies=proxy, headers={"User-Agent": "Mozilla/5.0 (iPhone; CPU iPhone OS 14_7_1 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.2 Mobile/15E148 Safari/604.1"})
         soup = BeautifulSoup(response.text, 'html.parser')
         
         # Extract text content
         page_text = soup.get_text().lower()
         text_matches = [keyword for keyword in brand_keywords if keyword.lower() in page_text]
         
         # Extract and analyze images
         img_tags = soup.find_all('img')
         image_matches = []
         
         for img in img_tags:
             img_url = img.get('src')
             if img_url:
                 img_response = requests.get(img_url, proxies=proxy)
                 img = Image.open(io.BytesIO(img_response.content))
                 processed_img = preprocess_image(img)
                 predictions = model.predict(processed_img)
                 decoded_predictions = tf.keras.applications.mobilenet_v2.decode_predictions(predictions.numpy())
                 
                 # Check if any of the top 5 predictions match brand keywords
                 for _, label, score in decoded_predictions[0]:
                     if any(keyword.lower() in label.lower() for keyword in brand_keywords):
                         image_matches.append((img_url, label, score))
         
         return {
             "url": url,
             "text_matches": text_matches,
             "image_matches": image_matches,
             "timestamp": time.time()
         }
     
     except Exception as e:
         print(f"Error occurred: {str(e)}")
         return None
 
 # Example usage
 urls_to_monitor = [
     "https://example.com/product1",
     "https://example.com/product2",
     # Add more URLs to monitor
 ]
 
 brand_keywords = ["YourBrand", "YourProduct", "YourLogo"]
 
 results = []
 
 for url in urls_to_monitor:
     result = collect_brand_data(url, brand_keywords)
     if result:
         results.append(result)
     time.sleep(random.uniform(1, 3))  # Random delay between requests
 
 # Convert results to a DataFrame
 df = pd.DataFrame(results)
 print(df)
 
 # Save results to CSV
 df.to_csv("brand_protection_results.csv", index=False)