Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import os
 from langchain_community.document_loaders import PyPDFLoader
 from langchain_community.vectorstores import FAISS
 from langchain.text_splitter import RecursiveCharacterTextSplitter
@@ -16,7 +15,7 @@ warnings.filterwarnings('ignore')
 
 # Set your Hugging Face API token here.
 # For deployment on Hugging Face, you can set this as an environment variable.
-
+import os
 os.environ["HUGGINGFACEHUB_API_TOKEN"] = "hf_YOUR_HUGGINGFACE_TOKEN"
 
 ## LLM - Using an open-source model from Hugging Face
@@ -78,6 +77,11 @@ def retriever(file_path):
     """
     splits = document_loader(file_path)
     chunks = text_splitter(splits)
+
+    # Add a check to ensure chunks are not empty
+    if not chunks:
+        raise ValueError("The uploaded document could not be processed. Please try another file.")
+
     vectordb = vector_database(chunks)
     retriever = vectordb.as_retriever()
     return retriever
@@ -95,8 +99,11 @@ def retriever_qa(file, query):
         return "Please upload a valid PDF file before asking a question."
 
     llm = get_llm()
-    retriever_obj = retriever(file_path)
-    
+    try:
+        retriever_obj = retriever(file_path)
+    except ValueError as e:
+        return str(e)
+
     # Custom prompt to act as a conversational legal advisor
     prompt_template = f"""
     You are a friendly and professional legal advisor. Your goal is to provide concise and contextual legal advice based on the provided document.