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): return 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" ) # Streamlit UI st.title(TITLE) st.write(DESCRIPTION) # Input fields message = st.text_area("Enter your message:", "") system_prompt = st.text_area("Optional system prompt:", "") temperature = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05) max_new_tokens = st.slider("Max new tokens", min_value=0, max_value=4096, value=4096, step=64) if st.button("Predict"): response = predict(message, system_prompt, temperature, max_new_tokens) st.write("Response:", response)