Update app.py

app.py

@@ -1,15 +1,15 @@
 # Cultural Bias Explorer in Language Models
 # ----------------------------------------
-# This Python project uses LangChain + HuggingFaceEndpoint to explore cultural bias
+# This Python project uses LangChain + HuggingFaceHub to explore cultural bias
 # by retrieving answers to the same prompts using region-specific document bases.
 
 # Install necessary packages before running:
-# pip install langchain huggingface_hub faiss-cpu sentence-transformers unstructured wikipedia
+# pip install langchain langchain-community huggingface_hub faiss-cpu sentence-transformers unstructured wikipedia
 
 from langchain_community.document_loaders import WikipediaLoader
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain_huggingface import HuggingFaceEndpoint
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.llms import HuggingFaceHub
 from langchain.chains import RetrievalQA
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 import os
@@ -37,13 +37,13 @@ def create_vector_store(region_topic):
     return vectorstore
 
 # ------------------ MAIN LOGIC ------------------
-# Set your Hugging Face token in HF settings or as an env variable
-# No need to manually set it here if using Hugging Face Spaces securely
+# If you're using Hugging Face Spaces, the token is already managed securely.
+# Otherwise, you can set it like this:
+# os.environ["HUGGINGFACEHUB_API_TOKEN"] = "your_token_here"
 
-llm = HuggingFaceEndpoint(
-    repo_id="HuggingFaceH4/zephyr-7b-beta",  # free-access model
-    temperature=0.7,
-    max_new_tokens=512
+llm = HuggingFaceHub(
+    repo_id="HuggingFaceH4/zephyr-7b-beta",  # free and public model
+    model_kwargs={"temperature": 0.7, "max_new_tokens": 512}
 )
 
 for region in REGIONS: