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from __future__ import annotations | |
import os | |
import time | |
from pathlib import Path | |
import openai | |
import streamlit as st | |
from dotenv import load_dotenv | |
from loguru import logger | |
from config import Config | |
from utils import create_vectordb | |
from utils import get_qa_chain | |
from utils import load_file | |
from utils import load_url | |
from utils import save_file_locally | |
load_dotenv() | |
openai.api_key = os.environ['OPENAI_API_KEY'] | |
if 'messages' not in st.session_state: | |
st.session_state.messages = [] | |
for message in st.session_state.messages: | |
with st.chat_message(message['role']): | |
st.markdown(message['content']) | |
uploaded_file = st.sidebar.file_uploader('Upload a file', type=['pdf', 'txt']) | |
doc_url = st.sidebar.text_input('Or enter a URL to a document') | |
if uploaded_file is not None and doc_url != '': | |
st.sidebar.error('Please choose one or the other') | |
st.stop() | |
def set_status(): | |
if uploaded_file is None: | |
# Path(Config.vectorstore_path).unlink(missing_ok=True) | |
st.sidebar.info('Upoad a file to start a conversation') | |
else: | |
st.sidebar.info(f'Let"s talk to {Path(uploaded_file.name)}') | |
def process_data(data, data_type): | |
if 'context' not in st.session_state: | |
if data_type == 'file': | |
upath = f'docs/{uploaded_file.name}' | |
save_file_locally(data, upath) | |
load_file(upath) | |
else: | |
load_url(data) | |
st.session_state['context'] = True | |
def process_uploaded_doc(): | |
if 'context' not in st.session_state: | |
loader = Uns | |
st.session_state['context'] = True | |
set_status() | |
if uploaded_file is not None or doc_url != '': | |
if uploaded_file is not None: | |
process_data(uploaded_file, data_type='file') | |
else: | |
process_data(doc_url, data_type='url') | |
qr_chain = get_qa_chain() | |
if prompt := st.chat_input('Send a message'): | |
st.session_state.messages.append({'role': 'user', 'content': prompt}) | |
with st.chat_message('user'): | |
st.markdown(prompt) | |
with st.chat_message('assistant'): | |
message_placeholder = st.empty() | |
full_response = '' | |
assistant_response = qr_chain({'question': prompt})['answer'] | |
logger.info(f'question {prompt}') | |
logger.info(f'assistant response {assistant_response}') | |
for chunk in assistant_response.split(): | |
full_response += chunk + ' ' | |
time.sleep(0.01) | |
message_placeholder.markdown(full_response + '▌') | |
message_placeholder.markdown(full_response) | |
st.session_state.messages.append( | |
{'role': 'assistant', 'content': full_response}, | |
) | |