Update app.py

app.py

@@ -91,26 +91,21 @@ def rag_process(query, k=2):
         return "Invalid query. Please enter a valid question.", [], {}
     
     start_time = time.perf_counter()
-    
-    # Embed query
     try:
         query_embedding = embedder.encode([query], show_progress_bar=False)
         embed_time = time.perf_counter() - start_time
     except Exception as e:
         return f"Error embedding query: {str(e)}", [], {}
     
-    # Retrieve FAQs
     start_time = time.perf_counter()
     distances, indices = index.search(query_embedding.astype(np.float32), k)
     retrieved_faqs = faq_data.iloc[indices[0]][['question', 'answer']].to_dict('records')
     retrieval_time = time.perf_counter() - start_time
     
-    # Generate response (rule-based for free tier)
     start_time = time.perf_counter()
     response = retrieved_faqs[0]['answer'] if retrieved_faqs else "Sorry, I couldn't find an answer."
     generation_time = time.perf_counter() - start_time
     
-    # Metrics
     metrics = {
         'embed_time': embed_time * 1000,
         'retrieval_time': retrieval_time * 1000,
@@ -159,7 +154,7 @@ def chat_interface(query):
             f"(removed {cleanup_details['removed']} junk entries: "
             f"{cleanup_details['nulls_removed']} nulls, "
             f"{cleanup_details['duplicates_removed']} duplicates, "
-            f"{cleanup_details['short_removed']} short entries, "
+            f"{cleanup_details['short_removed']} short, "
             f"{cleanup_details['malformed_removed']} malformed)"
         )