import openai import streamlit as st from langchain_core.messages import AIMessage, ChatMessage, HumanMessage from rag_chain.chain import get_rag_chain # Streamlit page configuration st.set_page_config(page_title="Tall Tree Integrated Health", page_icon="💬", layout="centered", initial_sidebar_state="collapsed") # Streamlit CSS configuration with open("styles/styles.css") as css: st.markdown(f"", unsafe_allow_html=True) # Error message templates base_error_message = ( "Oops! Something went wrong while processing your request. " "Please refresh the page or try again later.\n\n" "If the error persists, please contact us at " "[Tall Tree Health](https://www.talltreehealth.ca/contact-us)." ) openai_api_error_message = ( "We're sorry, but you've reached the maximum number of requests allowed per session.\n\n" "Please refresh the page to continue using the app." ) # Get chain and memory @st.cache_resource(show_spinner=False) def get_chain_and_memory(): try: # gpt-4 points to gpt-4-0613 # gpt-4-turbo-preview points to gpt-4-0125-preview # Fine-tuned: ft:gpt-3.5-turbo-1106:tall-tree::8mAkOSED # gpt-4-1106-preview return get_rag_chain(model_name="gpt-4-1106-preview", temperature=0.2) except Exception as e: st.warning(base_error_message, icon="🙁") st.stop() chain, memory = get_chain_and_memory() # Set up session state and clean memory (important to clean the memory at the end of each session) if "history" not in st.session_state: st.session_state["history"] = [] memory.clear() if "messages" not in st.session_state: st.session_state["messages"] = [] # Select locations element into a container with st.container(border=False): # Set the welcome message st.markdown( "Hello there! 👋 Need help finding the right service or practitioner? Let our AI-powered assistant give you a hand.\n\n" "To get started, please select your preferred location and share details about your symptoms or needs. " ) location = st.radio( "**Our Locations**:", ["Cordova Bay - Victoria", "James Bay - Victoria", "Comercial Drive - Vancouver"], index=None, horizontal=False, ) # Add some space between the container and the chat interface for _ in range(2): st.markdown("\n\n") # Get user input only if a location is selected prompt = "" if location: user_input = st.chat_input("Enter your message...") if user_input: st.session_state["messages"].append( ChatMessage(role="user", content=user_input)) prompt = f"{user_input}\nLocation: {location}" # Display previous messages user_avatar = "images/user.png" ai_avatar = "images/tall-tree-logo.png" for msg in st.session_state["messages"]: avatar = user_avatar if msg.role == 'user' else ai_avatar with st.chat_message(msg.role, avatar=avatar): st.markdown(msg.content) # Chat interface if prompt: # Add all previous messages to memory for human, ai in st.session_state["history"]: memory.chat_memory.add_user_message(HumanMessage(content=human)) memory.chat_memory.add_ai_message(AIMessage(content=ai)) # render the assistant's response with st.chat_message("assistant", avatar=ai_avatar): message_placeholder = st.empty() # If there is a message not None, add it to the memory try: partial_message = "" with st.spinner(" "): for chunk in chain.stream({"message": prompt}): partial_message += chunk message_placeholder.markdown(partial_message + "|") except openai.BadRequestError: st.warning(openai_api_error_message, icon="🙁") st.stop() except Exception as e: st.warning(base_error_message, icon="🙁") st.stop() message_placeholder.markdown(partial_message) # Add the full response to the history st.session_state["history"].append((prompt, partial_message)) # Add AI message to memory after the response is generated memory.chat_memory.add_ai_message(AIMessage(content=partial_message)) # add the full response to the message history st.session_state["messages"].append(ChatMessage( role="assistant", content=partial_message))