Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import os
-
 from langchain_community.document_loaders import PyPDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import Chroma
@@ -19,6 +18,27 @@ import torch
 import tqdm 
 import accelerate
 import re
+from huggingface_hub import login, HfApi
+
+
+
+# Authenticate using the Hugging Face token stored as a secret
+def authenticate_hf():
+    token = os.getenv("HUGGINGFACE_TOKEN")
+    if not token:
+        raise ValueError("Hugging Face token not found in environment variables.")
+    
+    try:
+        login(token=token)
+        api = HfApi()
+        user_info = api.whoami(token=token)
+        print(f"Login successful. User info: {user_info}")
+    except Exception as e:
+        raise ValueError(f"Error during login: {e}")
+
+# Authenticate at the start of the script
+authenticate_hf()
+
 
 
 
@@ -282,15 +302,6 @@ def demo():
         qa_chain = gr.State()
         collection_name = gr.State()
         
-        gr.Markdown(
-        """<center><h2>PDF-based chatbot</center></h2>
-        <h3>Ask any questions about your PDF documents</h3>""")
-        gr.Markdown(
-        """<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \
-        The user interface explicitely shows multiple steps to help understand the RAG workflow. 
-        This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br>
-        <br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply.
-        """)
         
         with gr.Tab("Step 1 - Upload PDF"):
             with gr.Row():