File size: 4,663 Bytes
62b9e5b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
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}")
|