add temperature

app.py

@@ -91,6 +91,7 @@ def normalize_query(user_query):
     try:
         response = client.chat.completions.create(
             model="llama-3.1-70b-versatile",
+            temperature=0.5,
             messages=[{
                 "role": "user",
                 "content": f"""
@@ -111,7 +112,7 @@ def get_user_embedding(query):
         return model.encode(query)
     except Exception as e:
         print(f"Error generating embedding: {e}")
-        return np.zeros(512)  # Assuming 384 as default embedding size
+        return np.zeros()  
 
 # Find similar places based on cosine similarity
 def get_similar_places(user_embedding):
@@ -153,6 +154,7 @@ def get_top_5_destinations(user_query):
             'similarity': similarity
         })
     
+    print(normalized_query)
     return top_places
 
 # Generate response to user using Groq VM
@@ -175,6 +177,7 @@ def generate_response(user_query, top_places):
         # Generate the response using the model
         response = client.chat.completions.create(
             model="llama-3.1-70b-versatile",
+            temperature=0.2,
             messages=[
                 {"role": "system", "content": system_prompt},  # System prompt defines behavior
                 {"role": "user", "content": f"Berikut adalah rekomendasi berdasarkan data: {destinations_data}"}
@@ -195,9 +198,6 @@ def chatbot(user_query):
     if isinstance(top_places, str):  # Error case, e.g. "No places found"
         return top_places
 
-    # Step 2: Generate the chatbot's response
-    # response = generate_response(user_query, top_places)
-
     # only the first 5 element of top_places
     response = generate_response(user_query, top_places[:5])