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import streamlit as st
from gradio_client import Client
# Constants
TITLE = "Llama2 70B Chatbot"
DESCRIPTION = """
This Space demonstrates model [Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) by Meta,
a Llama 2 model with 70B parameters fine-tuned for chat instructions.
"""
# Initialize client
client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/")
# Prediction function
def predict(message, system_prompt="", temperature=0.9, max_new_tokens=4096):
with st.status("Requesting LLama-2"):
st.write("Requesting API")
response = client.predict(
message, # str in 'Message' Textbox component
system_prompt, # str in 'Optional system prompt' Textbox component
temperature, # int | float (numeric value between 0.0 and 1.0)
max_new_tokens, # int | float (numeric value between 0 and 4096)
0.3, # int | float (numeric value between 0.0 and 1)
1, # int | float (numeric value between 1.0 and 2.0)
api_name="/chat"
)
st.write("Done")
return response
# Streamlit UI
st.title(TITLE)
st.write(DESCRIPTION)
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# React to user input
if prompt := st.chat_input("Ask LLama-2-70b anything..."):
# Display user message in chat message container
st.chat_message("human",avatar = "🧑💻").markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "human", "content": prompt})
response = predict(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant", avatar='🦙'):
st.markdown(response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response}) |