Create app.py

app.py

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+import os
+import streamlit as st
+
+import requests
+from bs4 import BeautifulSoup
+from sentence_transformers import SentenceTransformer, util
+from transformers import pipeline
+
+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):
+        # SerpAPI Key
+        self.serpapi_key = os.getenv("SERPAPI_API_KEY")
+
+        # 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. """
+        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")])  # Extract paragraph text
+        except requests.RequestException:
+            return ""  # Fail gracefully by returning an empty string
+
+    def get_domain_trust(self, url: str, content: str) -> int:
+        """ Computes the domain trust score based on available data sources. """
+        trust_scores = []
+
+        # Hugging Face Fake News Detector
+        if content:
+            try:
+                trust_scores.append(self.get_domain_trust_huggingface(content))
+            except:
+                pass
+
+        # Compute final score (average of available scores)
+        return int(sum(trust_scores) / len(trust_scores)) if trust_scores else 50
+
+    def get_domain_trust_huggingface(self, content: str) -> int:
+        """ Uses a Hugging Face fake news detection model to assess credibility. """
+        if not content:
+            return 50  # Default score if no cont