Need some direction in hiding or removing a widget

I am new to Streamlit and still learning Python 3.5. I am working on a project for myself and am stuck on removing, I believe, a widget. I put in a screenshot, but in the upper left corner, there is a Textbox to enter the OpenAI API Key. I would like to hide or remove this as I put my API key in a separate file. I have uploaded the full code in case it is needed. I have been spending a week on my downtime trying to figure this out. I appreciate any assistance. Thanks.

import streamlit as st
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from dotenv import load_dotenv, find_dotenv

st.set_page_config(page_title="Page Title", layout="wide", page_icon="395.ico",)

        .reportview-container {
            margin-top: -2em;
        #MainMenu {visibility: hidden;}
        .stDeployButton {display:none;}
        footer {visibility: hidden;}
        #stDecoration {display:none;}

# 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.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)
        print('Document format is not supported!')
        return None

    data = loader.load()
    return data

# splitting data in chunks
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

# create embeddings using OpenAIEmbeddings() and save them in a Chroma vector store
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.chat_models 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 =
    return answer

# calculate embedding cost using tiktoken
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 * 0.0004

# clear the chat history from streamlit session state
def clear_history():
    if 'history' in st.session_state:
        del st.session_state['history']

if __name__ == "__main__":
    import os

    # loading the OpenAI api key from .env
    from dotenv import load_dotenv, find_dotenv
    load_dotenv(find_dotenv(), override=True)

    st.subheader('Question-Answering Application')
    with st.sidebar:
        # text_input for the OpenAI API key (alternative to python-dotenv and .env)
        api_key = st.text_input('OpenAI API Key:', type='password')
        if api_key:
            os.environ['OPENAI_API_KEY'] = api_key

        # file uploader widget
        uploaded_file = st.file_uploader('Upload a file:', type=['pdf', 'docx', 'txt'])

        # chunk size number widget
        chunk_size = st.number_input('Chunk size:', min_value=100, max_value=2048, value=512, on_change=clear_history)

        # k number input widget
        k = st.number_input('k', min_value=1, max_value=20, value=3, on_change=clear_history)

        # add data button widget
        add_data = st.button('Add Data', on_click=clear_history)

        if uploaded_file and add_data: # if the user browsed a file
            with st.spinner('Reading, chunking and embedding file ...'):

                # writing the file from RAM to the current directory on disk
                bytes_data =
                file_name = os.path.join('./',
                with open(file_name, 'wb') as f:

                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}')

                # creating the embeddings and returning the Chroma vector store
                vector_store = create_embeddings(chunks)

                # saving the vector store in the streamlit session state (to be persistent between reruns)
                st.session_state.vs = vector_store
                st.success('File uploaded, chunked and embedded successfully.')

    # user's question text input widget
    q = st.text_input('Ask a question about the content of your file:')
    if q: # if the user entered a question and hit enter
        if 'vs' in st.session_state: # if there's the vector store (user uploaded, split and embedded a file)
            vector_store = st.session_state.vs
            st.write(f'k: {k}')
            answer = ask_and_get_answer(vector_store, q, k)

            # text area widget for the LLM answer
            st.text_area('LLM Answer: ', value=answer)


            # if there's no chat history in the session state, create it
            if 'history' not in st.session_state:
                st.session_state.history = ''

            # the current question and answer
            value = f'Q: {q} \nA: {answer}'

            st.session_state.history = f'{value} \n {"-" * 100} \n {st.session_state.history}'
            h = st.session_state.history

            # text area widget for the chat history
            st.text_area(label='Chat History', value=h, key='history', height=400)

#streamlit run  To Run your script

That is the widget.

Thank You, this was the answer I was looking for!