Update rag.py

rag.py

@@ -24,6 +24,13 @@ similarity_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
 HF_DATASET_REPO = "midrees2806/unmatched_queries"  # Your dataset repo
 HF_TOKEN = os.getenv("HF_TOKEN")  # From Space secrets
 
+# Greeting words list
+GREETINGS = [
+    "hi", "hello", "hey", "good morning", "good afternoon", "good evening",
+    "assalam o alaikum", "salam", "namaste", "hola", "bonjour", "hi there",
+    "hey there", "greetings", "howdy"
+]
+
 # --- Dataset Loading ---
 try:
     with open('dataset.json', 'r') as f:
@@ -64,7 +71,6 @@ def manage_unmatched_queries(query: str):
         print(f"Failed to save query: {e}")
 
 # --- Enhanced LLM Query ---        
-
 def query_groq_llm(prompt, model_name="llama3-70b-8192"):
     try:
         chat_completion = groq_client.chat.completions.create(
@@ -81,14 +87,44 @@ 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):
+    # 1. Check for empty input
+    if not user_input.strip():
+        return None  # This will be handled in the frontend to prevent submission
+    
     user_input_lower = user_input.lower().strip()
-
-    # 👉 Check if question is about fee
+    
+    # 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."
+    
+    # 3. Handle greetings (regardless of word count)
+    if any(greet in user_input_lower for greet in GREETINGS):
+        greeting_response = query_groq_llm(
+            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?"
+    
+    # 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"
+            "You'll find comprehensive information regarding tuition, admission charges, and other applicable fees there.\n"
             "🔗 https://ue.edu.pk/allfeestructure.php"
         )
 
@@ -98,7 +134,7 @@ def get_best_answer(user_input):
     best_match_idx = similarities.argmax().item()
     best_score = similarities[best_match_idx].item()
 
-    # 3. Save unmatched queries (threshold = 0.65)
+    # Save unmatched queries (threshold = 0.65)
     if best_score < 0.65:
         manage_unmatched_queries(user_input)
 
@@ -130,4 +166,8 @@ def get_best_answer(user_input):
         ✉️ [email protected]
         🌐 ue.edu.pk"""
 
-    return response
+    # 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
+    }