Create app.py

app.py

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+import streamlit as st
+from langchain.document_loaders import WikipediaLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import Ollama  # You can change to ChatOpenAI or DeepSeek
+from langchain.chains import RetrievalQA
+
+# Title
+st.set_page_config(page_title="Cultural Bias Explorer")
+st.title("🌍 Cultural Bias Explorer in LLMs (RAG + LangChain)")
+st.markdown("Explore how answers vary across cultures using region-specific knowledge bases.")
+
+# Inputs
+region = st.selectbox("Choose Cultural Region:", ["India", "United States"])
+prompt = st.text_input("Enter your question here:")
+
+# Load LLM
+llm = Ollama(model="llama3")  # You can change to "deepseek-chat" if available
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# Function to build retriever
+@st.cache_resource
+def build_retriever(region_topic):
+    loader = WikipediaLoader(query=region_topic, load_max_docs=3)
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
+    docs = text_splitter.split_documents(documents)
+    db = FAISS.from_documents(docs, embeddings)
+    return db.as_retriever()
+
+# Run RAG if prompt submitted
+if st.button("Generate Answer"):
+    if prompt.strip() == "":
+        st.warning("Please enter a prompt.")
+    else:
+        retriever = build_retriever(region)
+        qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
+        with st.spinner("Thinking..."):
+            answer = qa_chain.run(prompt)
+        st.markdown("#### ✨ Region-Specific Answer:")
+        st.success(answer)