Create app.py

app.py

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+import streamlit as st
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+from datasets import load_dataset
+from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
+
+# Load the XNLI dataset (Multilingual NLI dataset) for demonstration
+dataset = load_dataset("xnli", split="validation")
+
+# Initialize tokenizer and retriever for multilingual support (using XLM-Roberta)
+tokenizer = XLMRobertaTokenizer.from_pretrained("xlm-roberta-base")
+retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="compressed", passages_path="./path_to_multilingual_dataset")
+
+# Initialize the RAG model
+model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
+
+# Define Streamlit app
+st.title('Multilingual RAG Translator/Answer Bot')
+
+st.markdown("This app uses a multilingual RAG model to answer your questions in the language of the query. Ask questions in languages like Urdu, Hindi, or French!")
+
+# User input for query
+user_query = st.text_input("Ask a question in Urdu, Hindi, or French:")
+
+if user_query:
+    # Tokenize the input question
+    inputs = tokenizer(user_query, return_tensors="pt", padding=True, truncation=True)
+    input_ids = inputs['input_ids']
+
+    # Use the retriever to get relevant context
+    retrieved_docs = retriever.retrieve(input_ids)
+
+    # Generate an answer using the context
+    generated_ids = model.generate(input_ids, context_input_ids=retrieved_docs)
+    answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+    # Display the answer
+    st.write(f"Answer: {answer}")
+
+    # Display the most relevant documents
+    st.subheader("Relevant Documents:")
+    for doc in retrieved_docs:
+        st.write(doc['text'][:300] + '...')  # Display first 300 characters of each doc