app.py

import streamlit as st
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.chains.summarize import load_summarize_chain
from transformers import T5Tokenizer, T5ForConditionalGeneration
from transformers import pipeline
import torch
import base64


# Model & Tokenizer
checkpoint = "LaMini-Flan-T5-248M"
tokenizer = T5Tokenizer.from_pretrained(checkpoint)
base_model = T5ForConditionalGeneration.from_pretrained('MBZUAI/LaMini-Flan-T5-248M', device_map='auto', torch_dtype=torch.float32)

#File Loader & Preprocessing
def file_preprocessing(file):
    loader = PyPDFLoader(file)
    pages = loader.load_and_split()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
    texts= text_splitter.split_documents(pages)
    final_texts = ""
    for text in texts:
        print(text)
        final_texts = final_texts + text.page_content
    return final_texts

# LM Pipeline
def llm_pipeline(filepath):
    pipe_sum = pipeline(
        'summarization',
        model = base_model,
        tokenizer = tokenizer,
        max_length = 500,
        min_length = 50    
    )
    input_text = file_preprocessing(filepath)
    result = pipe_sum(input_text)
    result = result[0]['summary_text']
    return result

@st.cache_data
#functionto display the PDF of a given file 
def displayPDF(file):
    #Opening file from file path
    with open(file, "rb") as f:
        base64_pdf =base64.b64encode(f.read()).decode('utf-8')
    
    #Embeding PDF to HTML
    pdf_display= F'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
    
    #Displaying file
    st.markdown(pdf_display, unsafe_allow_html=True)
    
# streamlit code
st.set_page_config(layout='wide', page_title="Summarizatin App")

def main():
    st.title("PDF Summarization App using LLM")
    st.write("This is a simple app to summarize PDF files.")
    
    uploaded_file = st.file_uploader("Upload your PDF file", type=["pdf"])
    
    if uploaded_file is not None:
        if st.button("Summarize"):
            col1, col2 = st.columns(2)
            filePath = "data/"+uploaded_file.name
            with open(filePath, "wb") as temp_file:
                temp_file.write(uploaded_file.read())
            with col1:
                st.info("Uploaded PDF file")
                pdf_viewer = displayPDF(filePath)
                
            with col2:
                st.info("Summarization is below")
                summary = llm_pipeline(filePath)
                st.success(summary)
                

if __name__ == '__main__':
    main()