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. | Model | Llama2 | Llama2-hf | Llama2-chat | Llama2-chat-hf | |---|---|---|---|---| | 70B | [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | """ # Initialize client with st.sidebar: # system_promptSide = st.text_input("Optional system prompt:") temperatureSide = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05) max_new_tokensSide = st.slider("Max new tokens", min_value=0.0, max_value=4096.0, value=4096.0, step=64.0) # ToppSide = st.slider("Top-p (nucleus sampling)", min_value=0.0, max_value=1.0, value=0.6, step=0.05) # RepetitionpenaltySide = st.slider("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05) # Prediction function def predict(message, system_prompt='', temperature=0.7, max_new_tokens=4096,Topp=0.5,Repetitionpenalty=1.2): with st.status("Starting client"): client = Client("https://ysharma-explore-llamav2-with-tgi.hf.space/") st.write("Requesting client") 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) Topp, # int | float (numeric value between 0.0 and 1) Repetitionpenalty, # 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"], avatar=("🧑‍💻" if message["role"] == 'human' else '🦙')): 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(message=prompt)#, temperature= temperatureSide,max_new_tokens=max_new_tokensSide) # 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})