requirements.txt

streamlit
transformers
sentence-transformers
beautifulsoup4
requests

app.py

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+import streamlit as st
+import requests
+from bs4 import BeautifulSoup
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer, util
+
+class URLValidator:
+    def __init__(self):
+        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):
+        try:
+            response = requests.get(url, timeout=10)
+            response.raise_for_status()
+            soup = BeautifulSoup(response.text, "html.parser")
+            return " ".join([p.text for p in soup.find_all("p")])
+        except:
+            return ""
+
+    def compute_similarity_score(self, user_query, content):
+        if not content:
+            return 0
+        return int(util.pytorch_cos_sim(
+            self.similarity_model.encode(user_query),
+            self.similarity_model.encode(content)
+        ).item() * 100)
+
+    def detect_bias(self, content):
+        if not content:
+            return 50
+        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):
+        stars = max(1, min(5, round(score / 20)))
+        return stars, "⭐" * stars
+
+    def generate_explanation(self, domain_trust, similarity_score, fact_check_score, bias_score, citation_score, final_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.")
+        if citation_score < 30:
+            reasons.append("Few citations found for this content.")
+        return " ".join(reasons) if reasons else "This source is highly credible and relevant."
+
+    def rate_url_validity(self, user_query, url):
+        content = self.fetch_page_content(url)
+        similarity_score = self.compute_similarity_score(user_query, content)
+        bias_score = self.detect_bias(content)
+        
+        domain_trust = 60  # Placeholder
+        fact_check_score = 70  # Placeholder
+        citation_score = 50  # Placeholder
+
+        final_score = (0.3 * domain_trust) + (0.3 * similarity_score) + (0.2 * fact_check_score) + (0.1 * bias_score) + (0.1 * citation_score)
+
+        stars, icon = self.get_star_rating(final_score)
+        explanation = self.generate_explanation(domain_trust, similarity_score, fact_check_score, bias_score, citation_score, final_score)
+
+        return {
+            "Final Score": f"{final_score:.2f}%",
+            "Star Rating": icon,
+            "Explanation": explanation
+        }
+
+# Initialize validator
+validator = URLValidator()
+
+st.title("URL Credibility Checker")
+url = st.text_input("Enter URL")
+query = st.text_input("Enter Search Query")
+
+if st.button("Check Credibility"):
+    if url and query:
+        result = validator.rate_url_validity(query, url)
+        st.write("**Final Score:**", result["Final Score"])
+        st.write("**Star Rating:**", result["Star Rating"])
+        st.write("**Explanation:**", result["Explanation"])
+    else:
+        st.warning("Please enter both a URL and a search query.")