Create app.py

app.py

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+from openai import OpenAI  # Assuming Nvidia client is available in the same library, adjust if necessary
+import streamlit as st
+
+# Initialize Nvidia client
+client = OpenAI(
+    base_url="https://integrate.api.nvidia.com/v1",  # Nvidia API endpoint
+    api_key=st.secrets["NVIDIA_API_KEY"]  # Nvidia API Key from Streamlit secrets
+)
+
+st.title("ChatGPT-like clone with Nvidia Model")
+
+# 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"
+
+if "messages" not in st.session_state:
+    # Adding the initial system message
+    st.session_state.messages = [{"role": "system", "content": "You are a helpful assistant."}]
+
+# Render the chat history
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Get new user input
+if prompt := st.chat_input("What is up?"):
+    # Add user message to the session state
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # 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 = ""
+        for chunk in stream:
+            if chunk.choices[0].delta.content is not None:
+                chunk_text = chunk.choices[0].delta.content
+                response += chunk_text
+                st.write(chunk_text, end="")
+
+    # Store the assistant response in the session state
+    st.session_state.messages.append({"role": "assistant", "content": response})