frontend.py

import streamlit as st
import os
# from dotenv import load_dotenv

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma


# load_dotenv()
#
# openai_api_key = os.getenv('OPENAI_API_KEY')

# load private local file
# loading PDF, DOCX and TXT files as LangChain Documents
def load_document(file):
    import os
    name, extension = os.path.splitext(file)

    if extension == '.pdf':
        from langchain_community.document_loaders import PyPDFLoader
        print(f'Loading {file}')
        loader = PyPDFLoader(file)
    elif extension == '.docx':
        from langchain.document_loaders import Docx2txtLoader
        print(f'Loading {file}')
        loader = Docx2txtLoader(file)
    elif extension == '.txt':
        from langchain.document_loaders import TextLoader
        loader = TextLoader(file)
    else:
        print('Document format is not supported!')
        return None

    data = loader.load()
    return data


def chunk_data(data, chunk_size=256, chunk_overlap=20):
    from langchain.text_splitter import RecursiveCharacterTextSplitter
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
    chunks = text_splitter.split_documents(data)
    return chunks


def create_embeddings(chunks):
    embeddings = OpenAIEmbeddings()
    vector_store = Chroma.from_documents(chunks, embeddings)
    return vector_store


def ask_and_get_answer(vector_store, q, k=3):
    from langchain.chains import RetrievalQA
    from langchain_openai import ChatOpenAI

    llm = ChatOpenAI(model='gpt-3.5-turbo', temperature=1)
    retriever = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': k})
    chain = RetrievalQA.from_chain_type(llm=llm, chain_type='stuff', retriever=retriever)

    answer = chain.invoke(q)
    return answer


# embedding cost
def calculate_embedding_cost(texts):
    import tiktoken
    enc = tiktoken.encoding_for_model('text-embedding-ada-002')
    total_tokens = sum([len(enc.encode(page.page_content)) for page in texts])
    return total_tokens, total_tokens / 1000 * .0004


def clear_history():
    if 'history' in st.session_state:
        del st.session_state['history']


if __name__ == '__main__':
    import os
    from dotenv import load_dotenv, find_dotenv
    load_dotenv(find_dotenv(), override=True)

    with st.sidebar:
        api_key = st.text_input('OpenAI API key:', type='password')
        if api_key:
            os.environ['OPENAI_API_KEY'] = api_key

        # widgets
        uploaded_file = st.file_uploader('Upload a file of type pdf, txt, or docx:', type=['pdf', 'docx', 'txt'])
        chunk_size = st.number_input('Chunk size:', min_value=100, max_value=2048, value=512, on_change=clear_history)
        k = st.number_input('k', min_value=1, max_value=20, value=3, on_change=clear_history)
        add_data = st.button('Add Data', on_click=clear_history)

        if uploaded_file and add_data:
            with st.spinner('Reading, chunking and embedding file'):
                bytes_data = uploaded_file.read()
                file_name = os.path.join('./', uploaded_file.name)
                with open(file_name, 'wb') as f:
                    f.write(bytes_data)

                data = load_document(file_name)
                chunks = chunk_data(data, chunk_size=chunk_size)
                st.write(f'Chunk size: {chunk_size}, Chunks: {len(chunks)}')

                tokens, embedding_cost = calculate_embedding_cost(chunks)
                st.write(f'Embedding cost: {embedding_cost:.4f}')

                vector_store = create_embeddings(chunks)

                st.session_state.vs = vector_store
                st.success('File uploaded, chunked, and embedded successfully')

    question = st.text_input('Ask a question regarding the content of your file:')
    if question:
        if 'vs' in st.session_state:
            vector_store = st.session_state.vs

            answer = ask_and_get_answer(vector_store, question, k)
            st.text_area('Question:', value=question)
            st.text_area('LLM Answer:', value=answer['result'])

        st.divider()
        if 'history' not in st.session_state:
            st.session_state.history = ''

        value = f'Question: {question} \nAnswer: {answer['result']}'
        st.session_state.history = f'{value} \n {"-" * 100} \n {st.session_state.history}'
        h = st.session_state.history
        st.text_area(label='Chat history', value=h, key='history', height=400)