app.py

import streamlit as st
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration, pipeline

# Function to generate response using RAG (Retrieval-Augmented Generation)
def generate_response_with_rag(txt):
    try:
        # Initialize the RAG model and tokenizer
        tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
        retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="exact", use_dummy_dataset=True)
        model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")

        # Tokenize the input text
        inputs = tokenizer(txt, return_tensors="pt")

        # Retrieve relevant documents using the retriever
        retrieved_docs = retriever.retrieve(inputs["input_ids"])

        # Generate the output using RAG
        generated = model.generate(input_ids=inputs["input_ids"], context_input_ids=retrieved_docs['context_input_ids'])

        # Decode the generated text
        summary = tokenizer.decode(generated[0], skip_special_tokens=True)

        return summary
    except Exception as e:
        st.error(f"An error occurred during summarization: {str(e)}")
        return None

# Page title and layout
st.set_page_config(page_title='🦜🔗 RAG Text Summarization App')
st.title('🦜🔗 RAG Text Summarization App')

# Text input area for user to input text
txt_input = st.text_area('Enter your text', '', height=200)

# Form to accept the user's text input for summarization
response = None
with st.form('summarize_form', clear_on_submit=True):
    submitted = st.form_submit_button('Submit')
    if submitted and txt_input:
        with st.spinner('Summarizing with RAG...'):
            response = generate_response_with_rag(txt_input)

# Display the response if available
if response:
    st.info(response)

# Instructions for getting started with Hugging Face models
st.subheader("Hugging Face RAG Summarization")
st.write("""
This app uses Hugging Face's RAG model (Retrieval-Augmented Generation) to generate summaries with relevant external context.
RAG retrieves information from a set of documents and combines that with a generative model to produce more accurate summaries.
""")