First Commit of App

app.py

@@ -1,3 +1,169 @@
+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
+        # This api key is acquired from SerpAPI website.
+        self.serpapi_key = os.getenv("SERPAPI_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 content available
+        result = self.fake_news_classifier(content[:512])[0]  # Process only first 512 characters
+        return 100 if result["label"] == "REAL" else 30 if result["label"] == "FAKE" else 50
+
+    def compute_similarity_score(self, user_query: str, content: str) -> int:
+        """ Computes semantic similarity between user query and page 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 check_facts(self, content: str) -> int:
+        """ Cross-checks extracted content with Google Fact Check API. """
+        if not content:
+            return 50
+        api_url = f"https://toolbox.google.com/factcheck/api/v1/claimsearch?query={content[:200]}"
+        try:
+            response = requests.get(api_url)
+            data = response.json()
+            return 80 if "claims" in data and data["claims"] else 40
+        except:
+            return 50  # Default uncertainty score
+
+    def check_google_scholar(self, url: str) -> int:
+        """ Checks Google Scholar citations using SerpAPI. """
+        serpapi_key = self.serpapi_key
+        params = {"q": url, "engine": "google_scholar", "api_key": serpapi_key}
+        try:
+            response = requests.get("https://serpapi.com/search", params=params)
+            data = response.json()
+            return min(len(data.get("organic_results", [])) * 10, 100)  # Normalize
+        except:
+            return 0  # Default to no citations
+
+    def detect_bias(self, content: str) -> int:
+        """ Uses NLP sentiment analysis to detect potential bias in 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) -> 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, citation_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.")
+        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: str, url: str) -> dict:
+        """ Main function to evaluate the validity of a webpage. """
+        content = self.fetch_page_content(url)
+
+        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)
+        citation_score = self.check_google_scholar(url)
+
+        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 {
+            "raw_score": {
+                "Domain Trust": domain_trust,
+                "Content Relevance": similarity_score,
+                "Fact-Check Score": fact_check_score,
+                "Bias Score": bias_score,
+                "Citation Score": citation_score,
+                "Final Validity Score": final_score
+            },
+            "stars": {
+                "score": stars,
+                "icon": icon
+            },
+            "explanation": explanation
+        }
+    
 st.write("LEVEL1 TITLE: APP")
-st.write ("This is  my first app")
+st.write ("This is  my first app")
+
+# User input fields
+user_prompt = "I have just been on an international flight, can I come back home to hold my 1-month-old newborn?"
+url_to_check = "https://www.mayoclinic.org/healthy-lifestyle/infant-and-toddler-health/expert-answers/air-travel-with-infant/faq-20058539"
+
+# Run validation when the button is clicked
+if st.button("Validate URL"):
+    if not user_prompt.strip() or not url_to_check.strip():
+        st.warning("Please enter both a search query and a URL.")
+    else:
+        with st.spinner("Validating URL..."):
+            # Instantiate the URLValidator class
+            validator = URLValidator()
+            result = validator.rate_url_validity(user_prompt, url_to_check)
+
+        # Display results in JSON format
+        st.subheader("Validation Results")
+        st.json(result)

requirements.txt

@@ -1 +1,5 @@
 streamlit
+requests
+beautifulsoup4
+sentence-transformers transformers
+torch