app.py

from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader
from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores.faiss import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
import panel as pn
import os
import tempfile
from langchain.chains import RetrievalQA

file_input = pn.widgets.FileInput(width=300)

openaikey = pn.widgets.PasswordInput(
    value="", placeholder="Enter your OpenAI API Key here...", width=300
)
prompt = pn.widgets.TextEditor(
    value="", placeholder="Enter your questions here...", height=160, toolbar=False
)
run_button = pn.widgets.Button(name="Run!")

select_k = pn.widgets.IntSlider(
    name="Number of relevant chunks", start=1, end=5, step=1, value=2
)
select_chain_type = pn.widgets.RadioButtonGroup(
    name='Chain type',
    options=['stuff', 'map_reduce', "refine", "map_rerank"]
)

widgets = pn.Row(
    pn.Column(prompt, run_button, margin=5),
    pn.Card(
        "Chain type:",
        pn.Column(select_chain_type, select_k),
        title="Advanced settings", margin=10
    ), width=600
)


def qa(file, query, chain_type, k):
    loader = PyPDFLoader(file)
    documents = loader.load()
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
    texts = text_splitter.split_documents(documents)
    embeddings = OpenAIEmbeddings()
    vector_store = FAISS.from_documents(texts, embeddings)
    retriever = vector_store.as_retriever(
        search_type="similarity", search_kwargs={"k": k})
    model = ChatOpenAI(model='gpt-3.5-turbo')
    # qa = ConversationalRetrievalChain.from_llm(
    #    model, retriever=retriever, chain_type=chain_type)
    qa = RetrievalQA.from_chain_type(
        model, chain_type=chain_type, retriever=retriever, return_source_documents=False)

    result = qa({"query": query})
    # print(result['result'])
    # return result['answer']
    return result['result']


convos = []  # store all panel objects in a list


def temfile_create(file_input):
    with tempfile.NamedTemporaryFile(suffix='.pdf', delete=False) as temp_file:
        temp_file.write(file_input.value)
        temp_file.flush()
        temp_file.seek(0)
        # Do something with the temporary file here, such as passing the file path to another function
        return temp_file.name


def qa_result(_):
    os.environ["OPENAI_API_KEY"] = openaikey.value

    if file_input.value is not None:
        # file_input.save("/.cache/temp.pdf")
        pdf_file = temfile_create(file_input)
        prompt_text = prompt.value

        if prompt_text:
            result = qa(file=pdf_file, query=prompt_text,
                        chain_type=select_chain_type.value, k=select_k.value)
            convos.extend([
                pn.Row(
                    pn.panel("\U0001F60A", width=10),
                    prompt_text,
                    width=600
                ),
                pn.Row(
                    pn.panel("\U0001F916", width=10),
                    # result['answer'],
                    result,
                    width=600
                )
            ])
    return pn.Column(*convos, margin=15, width=575, min_height=400)


qa_interactive = pn.panel(
    pn.bind(qa_result, run_button),
    loading_indicator=True,
)

output = pn.WidgetBox('*Output will show up here:*',
                      qa_interactive, width=630, scroll=True)

# layout

# try:
pn.Column(
    pn.pane.Markdown("""
    ## \U0001F60A! Question Answering with your PDF file
    
    Step 1: Upload a PDF file \n
    Step 2: Enter your OpenAI API key. This costs $$. You will need to set up billing info at [OpenAI](https://platform.openai.com/account). \n
    Step 3: Type your question at the bottom and click "Run" \n
    
    """),
    pn.Row(file_input, openaikey),
    output,
    widgets

).servable()
# except Exception as ex:
#    pass