Update rag.py

rag.py

@@ -87,30 +87,17 @@ def query_groq_llm(prompt, model_name="llama3-70b-8192"):
         print(f"Error querying Groq API: {e}")
         return ""
 
-def handle_submit():
-    user_input = input_field.value.strip()
-    
-    if not user_input:
-        show_message("Please enter a question")
-        return
-    
-    response = get_best_answer(user_input)
-    
-    if response.get('should_scroll', False):
-        scroll_to_answer()
-    
-    display_response(response.get('response', ''))        
-
 def get_best_answer(user_input):
+    """Core function to find or generate the best response for a user query"""
     # 1. Check for empty input
     if not user_input.strip():
-        return None  # This will be handled in the frontend to prevent submission
+        return {"response": "Please enter a valid question.", "should_scroll": False}
     
     user_input_lower = user_input.lower().strip()
     
     # 2. Check for minimum word count (3 words)
     if len(user_input_lower.split()) < 3 and not any(greet in user_input_lower for greet in GREETINGS):
-        return "Please ask your question properly with at least 3 words."
+        return {"response": "Please ask your question properly with at least 3 words.", "should_scroll": False}
     
     # 3. Handle greetings (regardless of word count)
     if any(greet in user_input_lower for greet in GREETINGS):
@@ -118,15 +105,21 @@ def get_best_answer(user_input):
             f"You are an official assistant for University of Education Lahore. "
             f"Respond to this greeting in a friendly and professional manner: {user_input}"
         )
-        return greeting_response if greeting_response else "Hello! How can I assist you today?"
+        return {
+            "response": greeting_response if greeting_response else "Hello! How can I assist you today?",
+            "should_scroll": True
+        }
     
     # 4. Check if question is about fee
     if any(keyword in user_input_lower for keyword in ["fee structure", "fees structure", "semester fees", "semester fee"]):
-        return (
-            "💰 For complete and up-to-date fee details for this program, we recommend visiting the official University of Education fee structure page.\n"
-            "You'll find comprehensive information regarding tuition, admission charges, and other applicable fees there.\n"
-            "🔗 https://ue.edu.pk/allfeestructure.php"
-        )
+        return {
+            "response": (
+                "💰 For complete and up-to-date fee details for this program, we recommend visiting the official University of Education fee structure page.\n"
+                "You'll find comprehensive information regarding tuition, admission charges, and other applicable fees there.\n"
+                "🔗 https://ue.edu.pk/allfeestructure.php"
+            ),
+            "should_scroll": True
+        }
 
     # 🔁 Continue with normal similarity-based logic
     user_embedding = similarity_model.encode(user_input_lower, convert_to_tensor=True)
@@ -166,8 +159,40 @@ def get_best_answer(user_input):
         ✉️ info@ue.edu.pk
         🌐 ue.edu.pk"""
 
-    # Return the response along with a flag to indicate auto-scrolling should happen
     return {
         "response": response,
-        "should_scroll": True  # Frontend should use this to trigger auto-scrolling
-    }
+        "should_scroll": True
+    }
+
+def handle_submit(input_field_value):
+    """Main function to handle user submissions"""
+    user_input = input_field_value.strip()
+    
+    if not user_input:
+        return {"response": "Please enter a question", "should_scroll": False}
+    
+    response = get_best_answer(user_input)
+    
+    # Ensure consistent response format
+    if isinstance(response, str):
+        return {"response": response, "should_scroll": True}
+    elif isinstance(response, dict):
+        return response
+    else:
+        return {"response": "An error occurred while processing your request.", "should_scroll": False}
+
+# Example usage
+if __name__ == "__main__":
+    # Test the system
+    test_questions = [
+        "Hello",
+        "What's the fee structure?",
+        "What are the admission requirements for BSIT?",
+        "Short"
+    ]
+    
+    for question in test_questions:
+        print(f"\nQuestion: {question}")
+        response = handle_submit(question)
+        print(f"Response: {response['response']}")
+        print(f"Should scroll: {response['should_scroll']}")