arunks/RAG-Retrieval.Augmented.Generation

1 2import streamlit as st3from transformers import pipeline4import io5 6# Load the model7generator = pipeline("text-generation", model="EleutherAI/gpt-neo-1.3B")8 9# Streamlit app10def main():11    st.title("Text Generation with GPT-Neo")12 13    # Input text area14    input_text = st.text_area("Input Text", "")15 16    # File uploader for PDF files17    uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])18 19    # Generate button20    if st.button("Generate"):21        if input_text:22            # Generate text based on the input text23            output_text = generator(input_text, max_length=50, do_sample=True, temperature=0.7)[0]['generated_text']24            st.write(output_text)25        elif uploaded_file is not None:26            # Read the uploaded PDF file27            with io.BytesIO(uploaded_file.read()) as f:28                # Process the PDF file and generate text29                # (Add your PDF processing and text generation code here)30                # For demonstration purposes, we'll just echo the content of the PDF file31                pdf_content = f.read().decode("utf-8")32                st.write("Content of the uploaded PDF file:")33                st.write(pdf_content)34        else:35            st.error("Please enter some input text or upload a PDF file.")36 37if __name__ == "__main__":38    main()39