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import streamlit as st
import os
from pathlib import Path
from sentence_transformers import SentenceTransformer
from streamlit_utils import local_css, remote_css, load_pdf_files
from config import load_app_config, set_global_api_key
from indexing_utils import ServiceContextLoader, create_multi_index
from query_executers import QueryExecuter
#multi_index_demo_app.py (revoir ici )
#https://www.youtube.com/watch?v=xOOIVwH3g68
def main():
"""
This is the main method of the streamlit application
"""
# Set the OpenAI Api key as an environment variable
set_global_api_key()
dirname = Path(os.path.dirname(__file__))
local_css((dirname /"style.css").as_posix())
remote_css('https://fonts.googleapis.com/icon?family=Material+Icons')
# Load a Configuration object for the application
app_config = load_app_config()
# Initialize a ServiceContext for the QueryEngine
service_context = ServiceContextLoader(app_config=app_config).load()
# Initialize a simple SentenceTransformer model for clustering the final responses
sbert_model = SentenceTransformer(app_config.ClusteringConfig.SentenceTransformerModel)
st.title("Parallel Multi-Document Question Answering")
# Provide a file_uploader with drag and drop functionality
multiple_files = st.file_uploader(
"Drop multiple files:", accept_multiple_files=True
)
if multiple_files is None:
st.text("No upload")
else:
files = [file for file in multiple_files if str(file.name).endswith(".pdf")]
# Load the pdf files based on the file objects
file_content_list = load_pdf_files(files=files)
if file_content_list:
top_k = app_config.QueryEngineConfig.similarity_top_k
# Create a multi-index query engine based on the pdf file content
multi_index_query_engine = create_multi_index(file_content_list=file_content_list,
_service_context=service_context,
top_k=top_k)
# Execute the query and display the results in the streamlit app
query_executer = QueryExecuter(query_engine=multi_index_query_engine,
sbert_model=sbert_model,
config=app_config)
query_executer.run()
if __name__ == "__main__":
main()
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