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| import streamlit as st | |
| import replicate | |
| import os | |
| # App title | |
| st.set_page_config(page_title="🦙💬 Llama 2 Chatbot") | |
| # Replicate Credentials | |
| with st.sidebar: | |
| st.title('🦙💬 Llama 2 Chatbot') | |
| if 'REPLICATE_API_TOKEN' in st.secrets: | |
| st.success('API key already provided!', icon='✅') | |
| replicate_api = st.secrets['REPLICATE_API_TOKEN'] | |
| else: | |
| replicate_api = st.text_input('Enter Replicate API token:', type='password') | |
| if not (replicate_api.startswith('r8_') and len(replicate_api)==40): | |
| st.warning('Please enter your credentials!', icon='⚠️') | |
| else: | |
| st.success('Proceed to entering your prompt message!', icon='👉') | |
| # Refactored from https://github.com/a16z-infra/llama2-chatbot | |
| st.subheader('Models and parameters') | |
| selected_model = st.sidebar.selectbox('Choose a Llama2 model', ['Llama2-7B', 'Llama2-13B', 'Llama2-70B'], key='selected_model') | |
| if selected_model == 'Llama2-7B': | |
| llm = 'a16z-infra/llama7b-v2-chat:4f0a4744c7295c024a1de15e1a63c880d3da035fa1f49bfd344fe076074c8eea' | |
| elif selected_model == 'Llama2-13B': | |
| llm = 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5' | |
| else: | |
| llm = 'replicate/llama70b-v2-chat:e951f18578850b652510200860fc4ea62b3b16fac280f83ff32282f87bbd2e48' | |
| temperature = st.sidebar.slider('temperature', min_value=0.01, max_value=5.0, value=0.1, step=0.01) | |
| top_p = st.sidebar.slider('top_p', min_value=0.01, max_value=1.0, value=0.9, step=0.01) | |
| max_length = st.sidebar.slider('max_length', min_value=64, max_value=4096, value=512, step=8) | |
| # st.markdown('📖 Learn how to build this app in this [blog](https://blog.streamlit.io/how-to-build-a-llama-2-chatbot/)!') | |
| os.environ['REPLICATE_API_TOKEN'] = replicate_api | |
| # Store LLM generated responses | |
| if "messages" not in st.session_state.keys(): | |
| st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}] | |
| # Display or clear chat messages | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.write(message["content"]) | |
| def clear_chat_history(): | |
| st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}] | |
| st.sidebar.button('Clear Chat History', on_click=clear_chat_history) | |
| # Function for generating LLaMA2 response | |
| def generate_llama2_response(prompt_input): | |
| string_dialogue = "You are a helpful assistant. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'." | |
| for dict_message in st.session_state.messages: | |
| if dict_message["role"] == "user": | |
| string_dialogue += "User: " + dict_message["content"] + "\n\n" | |
| else: | |
| string_dialogue += "Assistant: " + dict_message["content"] + "\n\n" | |
| output = replicate.run(llm, | |
| input={"prompt": f"{string_dialogue} {prompt_input} Assistant: ", | |
| "temperature":temperature, "top_p":top_p, "max_length":max_length, "repetition_penalty":1}) | |
| return output | |
| # User-provided prompt | |
| if prompt := st.chat_input(disabled=not replicate_api): | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.write(prompt) | |
| # Generate a new response if last message is not from assistant | |
| if st.session_state.messages[-1]["role"] != "assistant": | |
| with st.chat_message("assistant"): | |
| with st.spinner("Thinking..."): | |
| response = generate_llama2_response(prompt) | |
| placeholder = st.empty() | |
| full_response = '' | |
| for item in response: | |
| full_response += item | |
| placeholder.markdown(full_response) | |
| placeholder.markdown(full_response) | |
| message = {"role": "assistant", "content": full_response} | |
| st.session_state.messages.append(message) |