changes to app

app.py

@@ -7,7 +7,6 @@ import uuid
 current_dir = os.path.dirname(os.path.abspath(__file__))
 
 from src.document_retrieval import DocumentRetrieval
-from utils.visual.env_utils import env_input_fields, initialize_env_variables, are_credentials_set, save_credentials
 from utils.parsing.sambaparse import parse_doc_universal # added
 from utils.vectordb.vector_db import VectorDb
 
@@ -36,14 +35,13 @@ def process_documents(files, collection_name, document_retrieval, vectorstore, c
         document_retrieval = DocumentRetrieval()
         _, _, text_chunks = parse_doc_universal(doc=files)
         print(len(text_chunks))
-        print(text_chunks)
+        print(text_chunks[0])
         embeddings = document_retrieval.load_embedding_model()
         collection_id = str(uuid.uuid4())
         collection_name = f"collection_{collection_id}"
         vectorstore = document_retrieval.create_vector_store(text_chunks, embeddings, output_db=save_location, collection_name=collection_name)
         document_retrieval.init_retriever(vectorstore)
         conversation_chain = document_retrieval.get_qa_retrieval_chain()
-        #input_disabled = False
         return conversation_chain, vectorstore, document_retrieval, collection_name, "Complete! You can now ask questions."
     except Exception as e:
         return conversation_chain, vectorstore, document_retrieval, collection_name, f"An error occurred while processing: {str(e)}"
@@ -54,9 +52,6 @@ with open(CONFIG_PATH, 'r') as yaml_file:
     
 prod_mode = config.get('prod_mode', False)
 
-# Load env variables
-initialize_env_variables(prod_mode)
-
 caution_text = """⚠️ Note: depending on the size of your document, this could take several minutes.
 """