Update deliverable2.py

deliverable2.py

@@ -81,84 +81,51 @@ class URLValidator:
         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)
-
-        # Handle errors
-        if "Error" in content:
-            return {"Validation Error": 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)
+    """ Main function to evaluate the validity of a webpage. """ 
+    content = self.fetch_page_content(url)
 
+    # If content fetching failed, return a properly structured response
+    if "Error" in content:
         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
+            "raw_score": {
+                "Domain Trust": 0,
+                "Content Relevance": 0,
+                "Fact-Check Score": 0,
+                "Bias Score": 0,
+                "Final Validity Score": 0
             },
             "stars": {
-                "icon": icon
+                "icon": "❌"
             },
-            "explanation": explanation
+            "explanation": content  # Display the error message
         }
 
+    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
+    }
 
-# **✅ Sample Queries and URLs (10 Each)**
-sample_queries = [
-    "How does climate change impact global weather?",
-    "What are the latest advancements in AI?",
-    "How does diet influence mental health?",
-    "What are the effects of space travel on astronauts?",
-    "Is cryptocurrency a safe investment?",
-    "What are the advantages of renewable energy?",
-    "How does deep learning work?",
-    "What are the health risks of 5G technology?",
-    "Is intermittent fasting effective for weight loss?",
-    "How do electric vehicles compare to gas cars?"
-]
-
-sample_urls = [
-    "https://www.nationalgeographic.com/environment/article/climate-change",
-    "https://www.technologyreview.com/2023/05/01/latest-ai-advancements/",
-    "https://www.health.harvard.edu/mind-and-mood/foods-linked-to-better-brainpower",
-    "https://www.nasa.gov/hrp/long-term-health-risks-of-space-travel",
-    "https://www.investopedia.com/terms/c/cryptocurrency.asp",
-    "https://www.energy.gov/eere/renewable-energy",
-    "https://www.ibm.com/cloud/deep-learning",
-    "https://www.who.int/news-room/questions-and-answers/item/radiation-5g-mobile-networks-and-health",
-    "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167940/",
-    "https://www.tesla.com/blog/benefits-of-electric-vehicles"
-]
-
-# **✅ Running the Validator and Saving to CSV**
-validator = URLValidator()
-
-data_rows = []
-for query, url in zip(sample_queries, sample_urls):
-    result = validator.rate_url_validity(query, url)
-    func_rating = round(result["raw_score"]["Final Validity Score"] / 20)  # Convert 100-scale to 1-5
-    custom_rating = func_rating + 1 if func_rating < 5 else func_rating  # User-adjusted rating
-
-    data_rows.append([query, url, func_rating, custom_rating])
-
-# Save to CSV
-csv_filename = "url_validation_results.csv"
-df = pd.DataFrame(data_rows, columns=["user_prompt", "url_to_check", "func_rating", "custom_rating"])
-df.to_csv(csv_filename, index=False)
-
-print(f"✅ CSV file '{csv_filename}' has been created successfully!")