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| import requests | |
| from bs4 import BeautifulSoup | |
| from sentence_transformers import SentenceTransformer, util | |
| from transformers import pipeline | |
| import pandas as pd | |
| class URLValidator: | |
| """ | |
| A production-ready URL validation class that evaluates the credibility of a webpage | |
| using multiple factors: domain trust, content relevance, fact-checking, bias detection, and citations. | |
| """ | |
| def __init__(self): | |
| # Load models once to avoid redundant API calls | |
| self.similarity_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2') | |
| self.fake_news_classifier = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection") | |
| self.sentiment_analyzer = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment") | |
| def fetch_page_content(self, url: str) -> str: | |
| """ Fetches and extracts text content from the given URL, handling errors gracefully. """ | |
| try: | |
| headers = {"User-Agent": "Mozilla/5.0"} | |
| response = requests.get(url, timeout=10, headers=headers) | |
| response.raise_for_status() | |
| soup = BeautifulSoup(response.text, "html.parser") | |
| content = " ".join([p.text for p in soup.find_all("p")]) | |
| return content if content else "Error: No readable content found on the page." | |
| except requests.exceptions.Timeout: | |
| return "Error: Request timed out." | |
| except requests.exceptions.HTTPError as e: | |
| return f"Error: HTTP {e.response.status_code} - Page may not exist." | |
| except requests.exceptions.RequestException as e: | |
| return f"Error: Unable to fetch URL ({str(e)})." | |
| def get_domain_trust(self, url: str, content: str) -> int: | |
| """ Computes the domain trust score. Uses a mock approach for now. """ | |
| if "Error" in content: | |
| return 0 # If page fetch failed, trust is low | |
| return len(url) % 5 + 1 # Mock trust rating (1-5) | |
| def compute_similarity_score(self, user_query: str, content: str) -> int: | |
| """ Computes semantic similarity between user query and page content. """ | |
| if "Error" in content: | |
| return 0 | |
| return int(util.pytorch_cos_sim( | |
| self.similarity_model.encode(user_query), | |
| self.similarity_model.encode(content) | |
| ).item() * 100) | |
| def check_facts(self, content: str) -> int: | |
| """ Simulated function to check fact reliability. """ | |
| if "Error" in content: | |
| return 0 | |
| return len(content) % 5 + 1 # Mock fact-check rating (1-5) | |
| def detect_bias(self, content: str) -> int: | |
| """ Uses NLP sentiment analysis to detect potential bias in content. """ | |
| if "Error" in content: | |
| return 0 | |
| sentiment_result = self.sentiment_analyzer(content[:512])[0] | |
| return 100 if sentiment_result["label"] == "POSITIVE" else 50 if sentiment_result["label"] == "NEUTRAL" else 30 | |
| def get_star_rating(self, score: float) -> tuple: | |
| """ Converts a score (0-100) into a 1-5 star rating. """ | |
| stars = max(1, min(5, round(score / 20))) # Normalize 100-scale to 5-star scale | |
| return stars, "⭐" * stars | |
| def generate_explanation(self, domain_trust, similarity_score, fact_check_score, bias_score, final_score) -> str: | |
| """ Generates a human-readable explanation for the score. """ | |
| reasons = [] | |
| if domain_trust < 50: | |
| reasons.append("The source has low domain authority.") | |
| if similarity_score < 50: | |
| reasons.append("The content is not highly relevant to your query.") | |
| if fact_check_score < 50: | |
| reasons.append("Limited fact-checking verification found.") | |
| if bias_score < 50: | |
| reasons.append("Potential bias detected in the content.") | |
| return " ".join(reasons) if reasons else "This source is highly credible and relevant." | |
| def rate_url_validity(self, user_query: str, url: str): | |
| """ Main function to evaluate the validity of a webpage. """ | |
| content = self.fetch_page_content(url) | |
| if "Error" in content: | |
| return { | |
| "raw_score": { | |
| "Domain Trust": 0, | |
| "Content Relevance": 0, | |
| "Fact-Check Score": 0, | |
| "Bias Score": 0, | |
| "Final Validity Score": 0 | |
| }, | |
| "stars": { | |
| "icon": "❌" | |
| }, | |
| "explanation": content | |
| } | |
| domain_trust = self.get_domain_trust(url, content) | |
| similarity_score = self.compute_similarity_score(user_query, content) | |
| fact_check_score = self.check_facts(content) | |
| bias_score = self.detect_bias(content) | |
| final_score = ( | |
| (0.3 * domain_trust) + | |
| (0.3 * similarity_score) + | |
| (0.2 * fact_check_score) + | |
| (0.2 * bias_score) | |
| ) | |
| stars, icon = self.get_star_rating(final_score) | |
| explanation = self.generate_explanation(domain_trust, similarity_score, fact_check_score, bias_score, final_score) | |
| return { | |
| "raw_score": { | |
| "Domain Trust": domain_trust, | |
| "Content Relevance": similarity_score, | |
| "Fact-Check Score": fact_check_score, | |
| "Bias Score": bias_score, | |
| "Final Validity Score": final_score | |
| }, | |
| "stars": { | |
| "icon": icon | |
| }, | |
| "explanation": explanation | |
| } | |
| # ✅ **Updated 15 Queries and 15 Different URLs** | |
| sample_queries = [ | |
| "How does artificial intelligence impact the job market?", | |
| "What are the risks of genetically modified organisms (GMOs)?", | |
| "What are the environmental effects of plastic pollution?", | |
| "How does 5G technology affect human health?", | |
| "What are the latest treatments for Alzheimer's disease?", | |
| "Is red meat consumption linked to heart disease?", | |
| "How does cryptocurrency mining impact the environment?", | |
| "What are the benefits of electric cars?", | |
| "How does sleep deprivation affect cognitive function?", | |
| "What are the effects of social media on teenage mental health?", | |
| "What are the ethical concerns of facial recognition technology?", | |
| "How does air pollution contribute to lung diseases?", | |
| "What are the potential dangers of artificial general intelligence?", | |
| "How does meditation impact brain function?", | |
| "What are the psychological effects of video game addiction?" | |
| ] | |
| sample_urls = [ | |
| "https://www.forbes.com/sites/forbestechcouncil/2023/10/15/impact-of-ai-on-the-job-market/", | |
| "https://www.fda.gov/food/food-labeling-nutrition/consumers-guide-gmo-foods", | |
| "https://www.nationalgeographic.com/environment/article/plastic-pollution", | |
| "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7453195/", | |
| "https://www.alz.org/alzheimers-dementia/treatments", | |
| "https://www.heart.org/en/news/2021/02/10/how-red-meat-affects-heart-health", | |
| "https://www.scientificamerican.com/article/how-bitcoin-mining-impacts-the-environment/", | |
| "https://www.tesla.com/blog/environmental-benefits-electric-cars", | |
| "https://www.sleepfoundation.org/sleep-deprivation", | |
| "https://www.psychologytoday.com/us/basics/teenagers-and-social-media", | |
| "https://www.brookings.edu/research/facial-recognition-technology-ethical-concerns/", | |
| "https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health", | |
| "https://futureoflife.org/background/benefits-risks-of-artificial-intelligence/", | |
| "https://www.mindful.org/meditation/mindfulness-getting-started/", | |
| "https://www.apa.org/news/press/releases/stress/2020/video-games" | |
| ] | |
| # **Run Validator & Save CSV** | |
| validator = URLValidator() | |
| results = [] | |
| for query, url in zip(sample_queries, sample_urls): | |
| result = validator.rate_url_validity(query, url) | |
| results.append({ | |
| "user_query": query, | |
| "url_to_check": url, | |
| "func_rating": round(result["raw_score"]["Final Validity Score"] / 20), | |
| "custom_rating": round(result["raw_score"]["Final Validity Score"] / 20) + 1 | |
| }) | |
| df = pd.DataFrame(results) | |
| df.to_csv("url_validation_results.csv", index=False) | |
| print("✅ CSV file 'url_validation_results.csv' has been created successfully!") | |