app.py

import streamlit as st
import os
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.llms import Together
from langchain.prompts import ChatPromptTemplate, PromptTemplate
from langchain.schema import format_document
from typing import List
from langchain.memory import ConversationBufferMemory
from langchain_community.chat_message_histories import StreamlitChatMessageHistory
import time

# Load the embedding function
model_name = "BAAI/bge-base-en"
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
embedding_function = HuggingFaceBgeEmbeddings(model_name=model_name, encode_kwargs=encode_kwargs)

# Load the LLM
llm = Together(model="mistralai/Mixtral-8x22B-Instruct-v0.1", temperature=0.2, max_tokens=19096, top_k=10, together_api_key=os.environ['pilotikval'], streaming=True)

msgs = StreamlitChatMessageHistory(key="langchain_messages")
memory = ConversationBufferMemory(chat_memory=msgs)

DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")

def _combine_documents(docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"):
    doc_strings = [format_document(doc, document_prompt) for doc in docs]
    return document_separator.join(doc_strings)

chistory = []

def store_chat_history(role: str, content: str):
    chistory.append({"role": role, "content": content})

def render_message_with_copy_button(role: str, content: str, key: str):
    html_code = f"""
    <div class="message" style="position: relative; padding-right: 40px;">
        <div class="message-content">{content}</div>
        <button onclick="copyToClipboard('{key}')" style="position: absolute; right: 0; top: 0; background-color: transparent; border: none; cursor: pointer;">
            <img src="https://img.icons8.com/material-outlined/24/grey/copy.png" alt="Copy">
        </button>
    </div>
    <textarea id="{key}" style="display:none;">{content}</textarea>
    <script>
        function copyToClipboard(key) {{
            var copyText = document.getElementById(key);
            copyText.style.display = "block";
            copyText.select();
            document.execCommand("copy");
            copyText.style.display = "none";
            alert("Copied to clipboard");
        }}
    </script>
    """
    st.write(html_code, unsafe_allow_html=True)

def get_streaming_response(user_query, chat_history):
    template = """
    You are a knowledgeable assistant. Provide a detailed and thorough answer to the question based on the following context:

    Chat history: {chat_history}

    User question: {user_question}
    """
    prompt = ChatPromptTemplate.from_template(template)
    
    inputs = {
        "chat_history": chat_history,
        "user_question": user_query
    }

    chain = prompt | llm
    return chain.stream(inputs)

def app():
    with st.sidebar:
        st.title("dochatter")
        option = st.selectbox('Which retriever would you like to use?', ('General Medicine', 'RespiratoryFishman', 'RespiratoryMurray', 'MedMRCP2', 'OldMedicine'))
        if option == 'RespiratoryFishman':
            persist_directory = "./respfishmandbcud/"
            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="fishmannotescud")
            retriever = vectordb.as_retriever(search_kwargs={"k": 5})
        elif option == 'RespiratoryMurray':
            persist_directory = "./respmurray/"
            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="respmurraynotes")
            retriever = vectordb.as_retriever(search_kwargs={"k": 5})
        elif option == 'MedMRCP2':
            persist_directory = "./medmrcp2store/"
            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="medmrcp2notes")
            retriever = vectordb.as_retriever(search_kwargs={"k": 5})
        elif option == 'General Medicine':
            persist_directory = "./oxfordmedbookdir/"
            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="oxfordmed")
            retriever = vectordb.as_retriever(search_kwargs={"k": 7})
        else:
            persist_directory = "./mrcpchromadb/"
            vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding_function, collection_name="mrcppassmednotes")
            retriever = vectordb.as_retriever(search_kwargs={"k": 5})

    if "messages" not in st.session_state.keys():
        st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]

    st.header("Ask Away!")
    for i, message in enumerate(st.session_state.messages):
        with st.chat_message(message["role"]):
            render_message_with_copy_button(message["role"], message["content"], key=f"message-{i}")
            store_chat_history(message["role"], message["content"])

    user_query = st.chat_input("Say something")
    if user_query:
        st.session_state.messages.append({"role": "user", "content": user_query})
        with st.chat_message("user"):
            st.write(user_query)

        with st.chat_message("assistant"):
            with st.spinner("Thinking..."):
                chat_history = "\n".join([f"{msg['role']}: {msg['content']}" for msg in chistory])
                try:
                    response_generator = get_streaming_response(user_query, chat_history)
                    response_text = ""
                    for response_part in response_generator:
                        response_text += response_part
                        st.write(response_text)
                    st.session_state.messages.append({"role": "assistant", "content": response_text})
                except Exception as e:
                    st.error(f"An error occurred: {e}")

if __name__ == '__main__':
    app()