import os 
import PyPDF2
from PIL import Image
import streamlit as st 
from transformers import T5ForConditionalGeneration,T5TokenizerFast

model = T5ForConditionalGeneration.from_pretrained("t5-base")
tokenizer = T5TokenizerFast.from_pretrained("t5-base")

def read_pdf(pdf):
    reader=PyPDF2.PdfReader(pdf)
    text=''
    for page in reader.pages:
        text+=page.extract_text()
    # text_file_name = 'text.txt'
    # text_file_path = '/content/text.txt'
    # with open(text_file_path, 'w') as text_file:
    #     text_file.write(text)
    return text


def summarizer(text):
    inputs = tokenizer.encode("summarize: " + text,return_tensors="pt", max_length=1000,truncation=True)
    outputs = model.generate(inputs,max_length=1000, min_length=100,length_penalty=2.0, num_beams=4,early_stopping=True)
    summary = tokenizer.decode(outputs[0])
    return summary


st.title(':blue[Abstractive Summarizer]')
st.header('by: _Team_ _Rare_ _species_')

uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
if uploaded_file is not None:
    if st.button('Summarize Document'):
        with st.spinner("📚    Please wait while we produce a summary..."):
            text=read_pdf(uploaded_file)
            summary=summarizer(text)
            st.divider()
            st.markdown(summary, unsafe_allow_html=True)
            st.divider()