app.py

# UI comes here
import streamlit as st

from langchain_text_splitters import Language
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from transformers import pipeline
from langchain import HuggingFacePipeline



gpt_model = 'gpt-4-1106-preview'
embedding_model = 'text-embedding-3-small'

def init():
    if "conversation" not in st.session_state:
        st.session_state.conversation = None
    if "chat_history" not in st.session_state:
        st.session_state.chat_history = None        

def init_llm_pipeline(openai_key):
    if "llm" not in st.session_state:
        model_id = "bigcode/starcoder2-15b"
        quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float16
        )

        tokenizer = AutoTokenizer.from_pretrained(model_id)
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            quantization_config=quantization_config,
            device_map="auto",
        )
        tokenizer.add_eos_token = True
        tokenizer.pad_token_id = 0
        tokenizer.padding_side = "left"

        text_generation_pipeline = pipeline(
        model=model,
        tokenizer=tokenizer,
        task="text-generation",
        temperature=0.7,
        repetition_penalty=1.1,
        return_full_text=True,
        max_new_tokens=300,
        )
        st.session_state.llm = HuggingFacePipeline(pipeline=text_generation_pipeline)          

def get_text(docs):
    return docs.getvalue().decode("utf-8")

def get_vectorstore(documents):
    python_splitter = RecursiveCharacterTextSplitter.from_language(
        language=Language.PYTHON, chunk_size=2000, chunk_overlap=200
    )
    texts = python_splitter.split_documents(documents)

    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")

    db = FAISS.from_documents(texts, embeddings)
    retriever = db.as_retriever(
        search_type="mmr",  # Also test "similarity"
        search_kwargs={"k": 8},
    )
    return retriever
    
def get_conversation(retriever):
    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
    conversation_chain = ConversationalRetrievalChain.from_llm(
        llm=st.session_state.llm,
        retriever=retriever,
        memory = memory   
    )
    return conversation_chain

def handle_user_input(question):
    response = st.session_state.conversation({'question':question})
    st.session_state.chat_history = response['chat_history']
    for i, message in enumerate(st.session_state.chat_history):
        if i % 2 == 0:
            with st.chat_message("user"):
                st.write(message.content)
        else:
            with st.chat_message("assistant"):
                st.write(message.content)

def main():
    #load_dotenv()
    init()

    st.set_page_config(page_title="Coding-Assistent", page_icon=":books:")

    st.header(":books: Coding-Assistent ")
    user_input = st.chat_input("Stellen Sie Ihre Frage hier")
    if user_input:
        with st.spinner("Führe Anfrage aus ..."):        
            handle_user_input(user_input)


    with st.sidebar:
        st.subheader("Code Upload")
        upload_docs=st.file_uploader("Dokumente hier hochladen", accept_multiple_files=True)
        if st.button("Hochladen"):
            with st.spinner("Analysiere Dokumente ..."):
                init_llm_pipeline()
                raw_text = get_text(upload_docs)
                vectorstore = get_vectorstore(raw_text)
                st.session_state.conversation = get_conversation(vectorstore) 


if __name__ == "__main__":
    main()