Update app.py

app.py

@@ -1,7 +1,7 @@
 from openai import OpenAI  # Assuming Nvidia client is available in the same library, adjust if necessary
 import streamlit as st
-
 import os
+from datetime import datetime
 
 # Initialize Nvidia client
 client = OpenAI(
@@ -11,6 +11,13 @@ client = OpenAI(
 
 st.title("ChatGPT-like clone with Nvidia Model")
 
+# Sidebar with instructions and Clear Session button
+with st.sidebar:
+    st.markdown("### Instructions 🤖\nThis is a basic chatbot. Ask anything, and the AI will try to help you!")
+    if st.button("Clear Session"):
+        st.session_state.clear()
+    st.text(f"Copyright © 2010-{datetime.now().year} Present Yiqiao Yin")
+
 # Initialize session state variables if not already present
 if "openai_model" not in st.session_state:
     st.session_state["openai_model"] = "nvidia/llama-3.1-nemotron-70b-instruct"
@@ -33,21 +40,22 @@ if prompt := st.chat_input("What is up?"):
 
     # Display assistant's message while waiting for the response
     with st.chat_message("assistant"):
-        # Create Nvidia completion request with full conversation history
-        stream = client.chat.completions.create(
-            model=st.session_state["openai_model"],
-            messages=st.session_state.messages,  # Include all previous messages in the API call
-            temperature=0.5,
-            top_p=0.7,
-            max_tokens=1024,
-            stream=True,
-        )
-        response_chunks = []
-        for chunk in stream:
-            if chunk.choices[0].delta.content is not None:
-                response_chunks.append(chunk.choices[0].delta.content)
-        response = "".join(response_chunks)
-        st.markdown(response)
+        with st.spinner("The assistant is thinking... Please wait."):
+            # Create Nvidia completion request with full conversation history
+            stream = client.chat.completions.create(
+                model=st.session_state["openai_model"],
+                messages=st.session_state.messages,  # Include all previous messages in the API call
+                temperature=0.5,
+                top_p=0.7,
+                max_tokens=1024,
+                stream=True,
+            )
+            response_chunks = []
+            for chunk in stream:
+                if chunk.choices[0].delta.content is not None:
+                    response_chunks.append(chunk.choices[0].delta.content)
+            response = "".join(response_chunks)
+            st.markdown(response)
 
     # Store the assistant response in the session state
     st.session_state.messages.append({"role": "assistant", "content": response})