Update app.py

app.py

@@ -1,17 +1,21 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
-from huggingface_hub import login
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, AutoModelForSequenceClassification
 from PyPDF2 import PdfReader
 from docx import Document
 import csv
 import json
 import os
 import torch
-from langchain.document_loaders import JSONLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
+from huggingface_hub import login
+
+huggingface_token = os.getenv('HUGGINGFACE_TOKEN')
+
+# Realizar el inicio de sesión de Hugging Face solo si el token está disponible
+if huggingface_token:
+    login(token=huggingface_token)
 
 # Cargar el modelo y el pipeline de Hugging Face
 @st.cache_resource
@@ -54,7 +58,7 @@ def create_vector_store():
     vector_stores = {}
     for category, docs in json_documents.items():
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
-        split_docs = [doc for doc in text_splitter.split_text(docs)]
+        split_docs = text_splitter.split_text(docs)
         vector_stores[category] = FAISS.from_texts(split_docs, embeddings)
     return vector_stores