Update app.py

app.py

@@ -14,6 +14,7 @@ class URLValidator:
 
     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
@@ -91,4 +92,79 @@ class URLValidator:
     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, "⭐" * sta
+        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
+        }
+
+# Streamlit app
+st.write("LEVEL1 TITLE: 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()  # Corrected instantiation
+            result = validator.rate_url_validity(user_prompt, url_to_check)
+
+        # Display results in JSON format
+        st.subheader("Validation Results")
+        st.json(result)