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import aiohttp import asyncio from bs4 import BeautifulSoup from transformers import pipeline from flask import Flask, request, jsonify

app = Flask(name)

class EcommerceDarkPatternDetector: def init(self): self.fake_timer_keywords = ["hurry", "limited time", "almost gone", "only a few left"] self.hidden_cost_keywords = ["shipping", "tax", "additional charges", "fees"] self.learning_data = [] self.nlp_model = pipeline("feature-extraction", model="bert-base-uncased", tokenizer="bert-base-uncased")

async def async_scrape_website(self, url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()

async def detect_fake_urgency(self, product_description):
    return any(re.search(keyword, product_description, flags=re.IGNORECASE) for keyword in self.fake_timer_keywords)

async def identify_hidden_costs(self, checkout_page):
    return any(re.search(keyword, checkout_page, flags=re.IGNORECASE) for keyword in self.hidden_cost_keywords)

async def analyze_product_information(self, product_details):
    vectors = self.nlp_model([product_details] + self.learning_data)
    similarity = (vectors[0] @ vectors[1:].T).mean()
    return similarity.item()

async def check_website(self, url):
    try:
        # Implement caching mechanism here to avoid repeated requests
        website_content = await self.async_scrape_website(url)
        if not website_content:
            return None

        soup = BeautifulSoup(website_content, 'lxml')  # Use lxml for faster parsing
        product_description = soup.find('meta', {'name': 'description'})['content'] if soup.find('meta', {'name': 'description'}) else ""
        checkout_page = soup.find('div', {'class': 'checkout-page'}).text if soup.find('div', {'class': 'checkout-page'}) else ""

        fake_urgency_detected = await self.detect_fake_urgency(product_description)
        hidden_costs_identified = await self.identify_hidden_costs(checkout_page)

        # Analyze product information and update learning data
        product_similarity = await self.analyze_product_information(product_description)
        self.learning_data.append(product_description)

        return {
            'fake_urgency_detected': fake_urgency_detected,
            'hidden_costs_identified': hidden_costs_identified,
            'product_similarity': product_similarity
        }
    except Exception as e:
        print(f"Error while checking website: {e}")
        return None

ecommerce_detector = EcommerceDarkPatternDetector()

@app.route('/detect-dark-patterns', methods=['POST']) def detect_dark_patterns(): data = request.get_json() website_url = data.get('url')

if not website_url:
    return jsonify({"error": "Missing 'url' parameter"}), 400

loop = asyncio.get_event_loop()
result = loop.run_until_complete(ecommerce_detector.check_website(website_url))

if result:
    return jsonify(result)
else:
    return jsonify({"error": "Failed to fetch or analyze website content"}), 500

if name == "main": app.run(debug=True)