app.py

from dataclasses import dataclass
from typing import Literal
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.callbacks import get_openai_callback
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationSummaryMemory
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
import streamlit.components.v1 as components
import os
from langchain.chains import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT

@dataclass
class Message:
    """Class for keeping track of a chat message."""
    origin: Literal["human", "ai"]
    message: str

def load_css():
    with open("static/styles.css", "r") as f:
        css = f"<style>{f.read()}</style>"
        st.markdown(css, unsafe_allow_html=True)

@st.cache_resource()
def load_index():
    loader = CSVLoader(file_path='latest_en.csv')
    doc = loader.load()
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    docs = text_splitter.split_documents(doc)
    embeddings = OpenAIEmbeddings()
    docsearch = Chroma.from_documents(docs, embeddings)

    return docsearch

vectorstore = load_index()

def initialize_session_state():
    if "history" not in st.session_state:
        st.session_state.history = []

    if "token_count" not in st.session_state:
        st.session_state.token_count = 0

    if "conversation" not in st.session_state:

        memory = ConversationSummaryMemory(
            llm=ChatOpenAI(temperature=0, model='gpt-3.5-turbo-0613'), 
            memory_key="chat_history",
            return_messages=True
        )

        question_generator = LLMChain(
            llm=ChatOpenAI(temperature=0, model="gpt-4"), 
            prompt=CONDENSE_QUESTION_PROMPT
        )
        
        doc_chain = load_qa_chain(
            ChatOpenAI(temperature=0, model='gpt-3.5-turbo-16k'), 
            chain_type="stuff"
       )

        st.session_state.conversation = ConversationalRetrievalChain(
                retriever=vectorstore.as_retriever(search_kwargs=dict(k=50)),
                question_generator=question_generator,
                combine_docs_chain=doc_chain,
                memory=memory
        )

def on_click_callback():

    with get_openai_callback() as cb:
        human_prompt = st.session_state.human_prompt
        llm_response = st.session_state.conversation.run(
            {"question": human_prompt}
        )
        st.session_state.history.append(
            Message("human", human_prompt)
        )
        st.session_state.history.append(
            Message("ai", llm_response)
        )
        st.session_state.token_count += cb.total_tokens
        
        # Reset the user input to empty string after sending the message
        st.session_state.human_prompt = ""

load_css()
initialize_session_state()

st.title("Vattenfall Competitor Monitoring")

chat_placeholder = st.container()
prompt_placeholder = st.form("chat-form")
credit_card_placeholder = st.empty()

with chat_placeholder:
    for chat in st.session_state.history:
        div = f"""
<div class="chat-row 
    {'' if chat.origin == 'ai' else 'row-reverse'}">
    <img class="chat-icon" src="https://huggingface.co/spaces/felix-weiland/vattenfall/resolve/main/static/{
        'ai_icon.png' if chat.origin == 'ai' 
                      else 'user_icon.png'}"
         width=32 height=32>
    <div class="chat-bubble
    {'ai-bubble' if chat.origin == 'ai' else 'human-bubble'}">
        &#8203;{chat.message}
    </div>
</div>
        """
        st.markdown(div, unsafe_allow_html=True)
    
    for _ in range(3):
        st.markdown("")

with prompt_placeholder:
    st.markdown("**Chat**")
    cols = st.columns((6, 1))
    cols[0].text_input(
        "Chat",
        value="",
        label_visibility="collapsed",
        key="human_prompt",
    )
    cols[1].form_submit_button(
        "Submit", 
        type="primary", 
        on_click=on_click_callback, 
    )


credit_card_placeholder.caption(f"""
Used {st.session_state.token_count} tokens \n
Debug Langchain conversation: 
{st.session_state.conversation.memory.buffer}
""")

components.html("""
<script>
const streamlitDoc = window.parent.document;

const buttons = Array.from(
    streamlitDoc.querySelectorAll('.stButton > button')
);
const submitButton = buttons.find(
    el => el.innerText === 'Submit'
);

streamlitDoc.addEventListener('keydown', function(e) {
    switch (e.key) {
        case 'Enter':
            submitButton.click();
            break;
    }
});
</script>
""", 
    height=0,
    width=0,
)