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| import streamlit as st | |
| import replicate | |
| import os | |
| from transformers import AutoTokenizer | |
| # # Assuming you have a specific tokenizers for Llama; if not, use an appropriate one like this | |
| # tokenizer = AutoTokenizer.from_pretrained("allenai/llama") | |
| # text = "Example text to tokenize." | |
| # tokens = tokenizer.tokenize(text) | |
| # num_tokens = len(tokens) | |
| # print("Number of tokens:", num_tokens) | |
| # App title | |
| st.set_page_config(page_title="Snowflake Arctic") | |
| # Replicate Credentials | |
| with st.sidebar: | |
| st.title('Snowflake Arctic') | |
| if 'REPLICATE_API_TOKEN' in st.secrets: | |
| #st.success('API token loaded!', 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 Replicate API token.', icon='⚠️') | |
| st.markdown("**Don't have an API token?** Head over to [Replicate](https://replicate.com) to sign up for one.") | |
| #else: | |
| # st.success('API token loaded!', icon='✅') | |
| os.environ['REPLICATE_API_TOKEN'] = replicate_api | |
| st.subheader("Adjust model parameters") | |
| temperature = st.sidebar.slider('temperature', min_value=0.01, max_value=5.0, value=0.3, step=0.01) | |
| top_p = st.sidebar.slider('top_p', min_value=0.01, max_value=1.0, value=0.9, step=0.01) | |
| # Store LLM-generated responses | |
| if "messages" not in st.session_state.keys(): | |
| st.session_state.messages = [{"role": "assistant", "content": "Hi. I'm Arctic, a new, efficient, intelligent, and truly open language model created by Snowflake AI Research. Ask me anything."}] | |
| # 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": "Hi. I'm Arctic, a new, efficient, intelligent, and truly open language model created by Snowflake AI Research. Ask me anything."}] | |
| st.sidebar.button('Clear chat history', on_click=clear_chat_history) | |
| st.sidebar.caption('Built by [Snowflake](https://snowflake.com/) to demonstrate [Snowflake Arctic](https://www.snowflake.com/blog/arctic-open-and-efficient-foundation-language-models-snowflake). App hosted on [Streamlit Community Cloud](https://streamlit.io/cloud). Model hosted by [Replicate](https://replicate.com/snowflake/snowflake-arctic-instruct).') | |
| def get_tokenizer(): | |
| """Get a tokenizer to make sure we're not sending too much text | |
| text to the Model. Eventually we will replace this with ArcticTokenizer | |
| """ | |
| return AutoTokenizer.from_pretrained("huggyllama/llama-7b") | |
| def get_num_tokens(prompt): | |
| """Get the number of tokens in a given prompt""" | |
| tokenizer = get_tokenizer() | |
| tokens = tokenizer.tokenize(prompt) | |
| return len(tokens) | |
| # Function for generating Snowflake Arctic response | |
| def generate_arctic_response(): | |
| prompt = [] | |
| for dict_message in st.session_state.messages: | |
| if dict_message["role"] == "user": | |
| prompt.append("<|im_start|>user\n" + dict_message["content"] + "<|im_end|>") | |
| else: | |
| prompt.append("<|im_start|>assistant\n" + dict_message["content"] + "<|im_end|>") | |
| prompt.append("<|im_start|>assistant") | |
| prompt.append("") | |
| prompt_str = "\n".join(prompt) | |
| if get_num_tokens(prompt_str) >= 4096: | |
| st.error("Conversation length too long. Please keep it under 4096 tokens.") | |
| st.button('Clear chat history', on_click=clear_chat_history, key="clear_chat_history") | |
| st.stop() | |
| for event in replicate.stream("snowflake/snowflake-arctic-instruct", | |
| input={"prompt": prompt_str, | |
| "prompt_template": r"{prompt}", | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| }): | |
| yield str(event) | |
| # 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"): | |
| response = generate_arctic_response() | |
| full_response = st.write_stream(response) | |
| message = {"role": "assistant", "content": full_response} | |
| st.session_state.messages.append(message) |