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_icon="395.ico")
st.set_page_config(page_title="Page Title", layout="wide", page_icon="395.ico",)
st.markdown("""
<style>
.reportview-container {
margin-top: -2em;
}
#MainMenu {visibility: hidden;}
.stDeployButton {display:none;}
footer {visibility: hidden;}
#stDecoration {display:none;}
</style>
""",unsafe_allow_html=True)
# 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)
else:
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 = chain.run(q)
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.image('img.png')
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 = 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}')
# 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)
st.divider()
# 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 chat_with_documents.py To Run your script
