Files
BriefGPT/local_app.py

97 lines
3.2 KiB
Python

import os
import streamlit as st
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from streamlit_chat import message as st_message
import pandas as pd
from local_chat_utils import load_db_from_file_and_create_if_not_exists_local
from streamlit_app_utils import generate_answer
from langchain.llms import GPT4All, LlamaCpp
from dotenv import load_dotenv
load_dotenv('test.env')
model_type = os.getenv('MODEL_TYPE')
model_path = os.getenv('MODEL_PATH')
accepted_filetypes = ['.txt', '.pdf', '.epub']
#Model is initialized here. Configure it with your parameters and the path to your model.
loading = st.spinner('Initializing LLM')
with st.spinner('Initializing LLM...'):
if 'llm' not in st.session_state:
with st.spinner('Loading LLM...'):
if model_type.lower() == 'LlamaCpp'.lower():
llm = LlamaCpp(model_path=model_path, n_ctx=1000)
st.session_state.llm = llm
elif model_type.lower() == 'GPT4All'.lower():
llm = GPT4All(model=model_path, backend='gptj', n_ctx=1000)
st.session_state.llm = llm
else:
st.warning('Invalid model type. GPT4ALL or LlamaCpp supported - make sure you specify in your env file.')
def chat():
st.title('Chat')
if 'text_input' not in st.session_state:
st.session_state.text_input = ''
directory = 'documents'
files = os.listdir(directory)
files = [file for file in files if file.endswith(tuple(accepted_filetypes))]
selected_file = st.selectbox('Select a file', files)
st.write('You selected: ' + selected_file)
selected_file_path = os.path.join(directory, selected_file)
if st.button('Load file (first time might take a second...) pressing this button will reset the chat history'):
db = load_db_from_file_and_create_if_not_exists_local(selected_file_path, 'hkunlp/instructor-base')
st.session_state.db = db
st.session_state.history = []
user_input = st.text_input('Enter your question', key='text_input')
if st.button('Ask') and 'db' in st.session_state:
answer = generate_answer(st.session_state.db, st.session_state.llm)
if 'history' not in st.session_state:
st.session_state.history = []
for i, chat in enumerate(st.session_state.history):
st_message(**chat, key=str(i))
def documents():
st.title('Documents')
st.markdown('Documents are stored in the documents folder in the project directory.')
directory = 'documents'
files = os.listdir(directory)
files = [file for file in files if file.endswith('.txt') or file.endswith('.pdf')]
if files:
files_df = pd.DataFrame(files, columns=['File Name'], index=range(1, len(files) + 1))
st.dataframe(files_df, width=1000)
else:
st.write('No documents found in documents folder. Add some documents first!')
PAGES = {
"Chat": chat,
"Documents": documents,
}
st.sidebar.title("Navigation")
selection = st.sidebar.radio("Go to", list(PAGES.keys()))
st.sidebar.markdown(' [Contact author](mailto:ethanujohnston@gmail.com)')
st.sidebar.markdown(' [Github](https://github.com/e-johnstonn/docsummarizer)')
page = PAGES[selection]
page()