main.py

# main.py
import os
import streamlit as st
import anthropic

from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.vectorstores import SupabaseVectorStore

from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory

from supabase import Client, create_client
from streamlit.logger import get_logger
from stats import get_usage, add_usage

supabase_url = st.secrets.SUPABASE_URL
supabase_key = st.secrets.SUPABASE_KEY
openai_api_key = st.secrets.openai_api_key
anthropic_api_key = st.secrets.anthropic_api_key
hf_api_key = st.secrets.hf_api_key
username = st.secrets.username

supabase: Client = create_client(supabase_url, supabase_key)
logger = get_logger(__name__)

embeddings = HuggingFaceInferenceAPIEmbeddings(
    api_key=hf_api_key,
    model_name="BAAI/bge-large-en-v1.5"
)

if 'chat_history' not in st.session_state:
    st.session_state['chat_history'] = []

vector_store = SupabaseVectorStore(supabase, embeddings, query_name='match_documents', table_name="documents")
memory = ConversationBufferMemory(memory_key="chat_history", input_key='question', output_key='answer', return_messages=True)

# model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
model = "meta-llama/Meta-Llama-3.1-70B-Instruct"
# model = "meta-llama/Meta-Llama-3.1-405B-Instruct-FP8"
temperature = 0.1
max_tokens = 500
stats = str(get_usage(supabase)) 

def response_generator(query):
    qa = None
    add_usage(supabase, "chat", "prompt" + query, {"model": model, "temperature": temperature})
    logger.info('Using HF model %s', model)
    # print(st.session_state['max_tokens'])
    endpoint_url = ("https://api-inference.huggingface.co/models/"+ model)
    model_kwargs = {"temperature" : temperature,
                    "max_new_tokens" : max_tokens,
                    # "repetition_penalty" : 1.1,
                    "return_full_text" : False}
    hf = HuggingFaceEndpoint(
        endpoint_url=endpoint_url,
        task="text-generation",
        huggingfacehub_api_token=hf_api_key,
        model_kwargs=model_kwargs
    )
    qa = ConversationalRetrievalChain.from_llm(hf, retriever=vector_store.as_retriever(search_kwargs={"score_threshold": 0.6, "k": 4,"filter": {"user": username}}), memory=memory, verbose=True, return_source_documents=True)
    
    # Generate model's response 
    model_response = qa({"question": query})
    logger.info('Result: %s', model_response["answer"])
    sources = model_response["source_documents"]
    logger.info('Sources: %s', model_response["source_documents"])

    if len(sources) > 0:
        response = model_response["answer"]
    else:
        response = "I am sorry, I do not have enough information to provide an answer. If there is a public source of data that you would like to add, please email copilot@securade.ai."
    
    return response
    
# Set the theme
st.set_page_config(
    page_title="Securade.ai - Safety Copilot",
    page_icon="https://securade.ai/favicon.ico",
    layout="centered",
    initial_sidebar_state="collapsed",
    menu_items={
        "About": "# Securade.ai Safety Copilot v0.1\n [https://securade.ai](https://securade.ai)",
        "Get Help" : "https://securade.ai",
        "Report a Bug": "mailto:hello@securade.ai"
    }
)

st.title("👷‍♂️ Safety Copilot 🦺")

st.markdown("Chat with your personal safety assistant about any health & safety related queries.")
# st.markdown("Up-to-date with latest OSH regulations for Singapore, Indonesia, Malaysia & other parts of Asia.")
st.markdown("_"+ stats + " queries answered!_")

if 'chat_history' not in st.session_state:
    st.session_state['chat_history'] = []
    
# Display chat messages from history on app rerun
for message in st.session_state.chat_history:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])
        
# Accept user input
if prompt := st.chat_input("Ask a question"): 
    # print(prompt)
    # Add user message to chat history
    st.session_state.chat_history.append({"role": "user", "content": prompt})
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)
    
    with st.spinner('Safety briefing in progress...'):
        response = response_generator(prompt)
    
    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        st.markdown(response)
    # Add assistant response to chat history
    # print(response)
    st.session_state.chat_history.append({"role": "assistant", "content": response})

# query = st.text_area("## Ask a question (" + stats + " queries answered so far)", max_chars=500)
# columns = st.columns(2)
# with columns[0]:
#     button = st.button("Ask")
# with columns[1]:
#     clear_history = st.button("Clear History", type='secondary')
    
# st.markdown("---\n\n")

# if clear_history:
#     # Clear memory in Langchain
#     memory.clear()
#     st.session_state['chat_history'] = []
#     st.experimental_rerun()