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| import streamlit as st | |
| import os | |
| from ctransformers import AutoModelForCausalLM | |
| # App title | |
| st.set_page_config(page_title="🦙💬 Llama 2 Chatbot") | |
| def ChatModel(temperature, top_p): | |
| return AutoModelForCausalLM.from_pretrained( | |
| # 'ggml-llama-2-7b-chat-q4_0.bin', | |
| 'Israr-dawar/psychology_chatbot', | |
| # model_type='llama', | |
| temperature=temperature, | |
| top_p = top_p) | |
| # Replicate Credentials | |
| with st.sidebar: | |
| st.title('🦙💬 Llama 2 Chatbot') | |
| # Refactored from <https://github.com/a16z-infra/llama2-chatbot> | |
| st.subheader('Models and parameters') | |
| temperature = st.sidebar.slider('temperature', min_value=0.01, max_value=2.0, 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) | |
| chat_model =ChatModel(temperature, top_p) | |
| # st.markdown('📖 Learn how to build this app in this [blog](#link-to-blog)!') | |
| # 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 = chat_model(f"prompt {string_dialogue} {prompt_input} Assistant: ") | |
| return output | |
| # User-provided prompt | |
| if prompt := st.chat_input(): | |
| 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) | |