Update app.py

app.py

@@ -1,17 +1,18 @@
 # Cultural Bias Explorer in Language Models
 # ----------------------------------------
-# This Python project uses LangChain + Ollama (or DeepSeek) 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 langchain-community openai faiss-cpu unstructured wikipedia
+# pip install langchain langchain-community 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.llms import Ollama
+from langchain.llms import HuggingFaceHub
 from langchain.chains import RetrievalQA
 from langchain.text_splitter import RecursiveCharacterTextSplitter
+import os
 
 # ------------------ CONFIG ------------------
 REGIONS = ['India', 'United States']
@@ -36,7 +37,13 @@ def create_vector_store(region_topic):
     return vectorstore
 
 # ------------------ MAIN LOGIC ------------------
-llm = Ollama(model="llama3")  # Can also use deepseek-chat or mistral if supported
+# Set your HF token as environment variable or directly in the constructor (not recommended for security)
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = "your_hf_token_here"  # Replace with your actual token
+
+llm = HuggingFaceHub(
+    repo_id="mistralai/Mistral-7B-Instruct-v0.1",
+    model_kwargs={"temperature": 0.7, "max_new_tokens": 512}
+)
 
 for region in REGIONS:
     print(f"\n=== REGION: {region.upper()} ===")