Upload app.py

app.py

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+import streamlit as st
+import openai
+
+# Set up the Streamlit page configuration
+st.set_page_config(page_title="NVIDIA OpenAI ChatGPT", layout="centered")
+
+# Sidebar for API key input
+with st.sidebar:
+    st.header("API Key Configuration")
+    api_key = st.text_input("Enter your NVIDIA API Key:")
+
+st.sidebar.markdown(
+    """
+    <div style="padding: 20px; background-color: #292b2c; border-radius: 10px; color: #f0f0f0;">
+        <h3 style="text-align: center;">Steps to Get an API Key from NVIDIA</h3>
+        <ul style="font-size: 14px; color: #dcdcdc;">
+            <li><strong>1. Create an NVIDIA Developer Account:</strong> Go to <a href="https://developer.nvidia.com/" target="_blank" style="color: #4caf50;">NVIDIA Developer's website</a>. Sign up or log in.</li>
+            <li><strong>2. Access NVIDIA's Cloud AI Services:</strong> Once logged in, navigate to the <a href="https://www.nvidia.com/en-us/cloud/" target="_blank" style="color: #4caf50;">NVIDIA Cloud page</a>.</li>
+            <li><strong>3. Find the API Access Section:</strong> Look for the API section for models like LLaMA or similar in the "Generative AI" or "NLP" tools category.</li>
+            <li><strong>4. Request Access (if needed):</strong> Some APIs may require joining a waitlist or submitting a request form to get access.</li>
+            <li><strong>5. Get the API Key:</strong> Once granted access, you'll receive an API key for authentication in your requests.</li>
+        </ul>
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# Initialize session state for chat history
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [{"role": "system", "content": "You are a helpful assistant."}]
+
+# Main Title
+st.title("NVIDIA OpenAI ChatGPT Interface")
+
+if not api_key:
+    st.warning("Please enter your API key in the sidebar to start.")
+else:
+    try:
+        # Configure the OpenAI client
+        openai.api_base = "https://integrate.api.nvidia.com/v1"
+        openai.api_key = api_key
+
+        # Create containers for chat history and input field
+        chat_container = st.container()
+        input_container = st.empty()  # Ensures the input stays fixed at the bottom
+
+        # Display the conversation
+        with chat_container:
+            # Display the conversation
+            for message in st.session_state["messages"]:
+                if message["role"] == "user":
+                    st.markdown(f"**🧑 You:** {message['content']}")
+                elif message["role"] == "assistant":
+                    st.markdown(f"**🤖 Bot:** {message['content']}")
+
+        with input_container:
+            # Input form for user messages
+            with st.form("chat_form", clear_on_submit=True):
+                user_input = st.text_input("Your message:", placeholder="Type your message here...")
+                submitted = st.form_submit_button("Send")
+
+        if submitted and user_input:
+            # Add the user's message to the chat history
+            st.session_state["messages"].append({"role": "user", "content": user_input})
+
+            # Display the user's message immediately
+            st.markdown(f"**🧑 You:** {user_input}")
+
+            # Fetch the assistant's response
+            with st.spinner("🤖 Bot is typing..."):
+                try:
+                    # Call the API
+                    response = openai.ChatCompletion.create(
+                        model="nvidia/llama-3.1-nemotron-70b-instruct",
+                        messages=st.session_state["messages"],
+                        temperature=0.5,
+                        top_p=0.7,
+                        max_tokens=1024,
+                        stream=True
+                    )
+
+                    # Stream response in real-time
+                    response_container = st.empty()
+                    full_response = ""
+                    for chunk in response:
+                        if chunk.choices[0].delta.content is not None:
+                            delta_content = chunk.choices[0].delta.content
+                            full_response += delta_content
+                            response_container.markdown(f"**🤖 Bot:** {full_response}")
+
+                    # Add the assistant's response to chat history
+                    st.session_state["messages"].append({"role": "assistant", "content": full_response})
+                except Exception as e:
+                    st.error(f"An error occurred while fetching the response: {e}")
+
+    except Exception as e:
+        st.error(f"Failed to configure OpenAI API: {e}")