app.py

import streamlit as st
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Load pre-trained multilingual model for retrieval and generation with trust_remote_code=True
tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="faiss", trust_remote_code=True)

# Set up FAISS for multilingual document retrieval
def setup_faiss():
    # Load multilingual embeddings for documents (e.g., using LaBSE or multilingual BERT)
    model_embed = SentenceTransformer('sentence-transformers/LaBSE')
    
    # Example multilingual documents
    docs = [
        "How to learn programming?",
        "Comment apprendre la programmation?",
        "پروگرامنگ سیکھنے کا طریقہ کیا ہے؟"
    ]
    
    embeddings = model_embed.encode(docs, convert_to_tensor=True)
    faiss_index = faiss.IndexFlatL2(embeddings.shape[1])
    faiss_index.add(np.array(embeddings))
    
    return faiss_index, docs

# Set up FAISS index
faiss_index, docs = setup_faiss()

# Retrieve documents based on query
def retrieve_docs(query):
    # Embed the query
    query_embedding = SentenceTransformer('sentence-transformers/LaBSE').encode([query], convert_to_tensor=True)
    
    # Perform retrieval using FAISS
    D, I = faiss_index.search(np.array(query_embedding), 1)
    
    # Get the most relevant document
    return docs[I[0][0]]

# Handle question-answering
def answer_question(query):
    # Retrieve relevant document
    retrieved_doc = retrieve_docs(query)
    
    # Tokenize the input
    inputs = tokenizer(query, retrieved_doc, return_tensors="pt", padding=True, truncation=True)
    
    # Generate an answer
    generated = model.generate(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"])
    
    # Decode the answer
    answer = tokenizer.decode(generated[0], skip_special_tokens=True)
    return answer

# Streamlit interface for user input
st.title("Multilingual RAG Translator/Answer Bot")
st.write("Ask a question in your preferred language (Urdu, French, Hindi)")

query = st.text_input("Enter your question:")

if query:
    answer = answer_question(query)
    st.write(f"Answer: {answer}")