Update app.py

app.py

@@ -1,61 +1,65 @@
-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
-
-
-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()
+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()