app.py

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)