Update rag.py

rag.py

@@ -24,6 +24,13 @@ similarity_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
 HF_DATASET_REPO = "midrees2806/unmatched_queries"
 HF_TOKEN = os.getenv("HF_TOKEN")
 
+# Greeting list
+GREETINGS = [
+    "hi", "hello", "hey", "good morning", "good afternoon", "good evening",
+    "assalam o alaikum", "salam", "aoa", "hi there",
+    "hey there", "greetings"
+]
+
 # Load dataset (automatically using the path)
 with open('dataset.json', 'r') as f:
     dataset = json.load(f)
@@ -67,10 +74,20 @@ def query_groq_llm(prompt, model_name="llama3-70b-8192"):
         return ""
 
 def get_best_answer(user_input):
+
+    if not user_input.strip():
+        return "Please enter a valid question."
     user_input_lower = user_input.lower().strip()
 
+    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?"
+
     # 👉 Check if question is about fee
-    if any(keyword in user_input_lower for keyword in ["fee", "fees", "charges", "semester fee"]):
+    if any(keyword in user_input_lower for keyword in ["fee structure", "fees structure"]):
         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"