import os
import base64
import tempfile
import streamlit as st
from transformers import pipeline
from PyPDF2 import PdfReader

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load the summarization model
tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
base_model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")

# Function to extract text from a PDF using PyPDF2
def extract_text_from_pdf(pdf_path):
      reader = PdfReader(pdf_path)
      text = ""
      for page in reader.pages:
          text += page.extract_text()  # Extract text from each page
      if not text.strip():
          raise ValueError("The PDF file contains no extractable text.")
      return text


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

@st.cache_data
# Function to display the PDF
def displayPDF(file_path):
    
      with open(file_path, "rb") as f:
          base64_pdf = base64.b64encode(f.read()).decode('utf-8')
      pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
      st.markdown(pdf_display, unsafe_allow_html=True)


# Streamlit App
def main():
    st.title('Content Summarizer')

    # PDF Upload Section
    uploaded_file = st.file_uploader("Upload your PDF file", type=['pdf'])
    if uploaded_file is not None:
        if st.button("Summarize PDF"):
            col1, col2 = st.columns(2)

            # Save the uploaded file to a temporary location
            with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf", dir="/tmp/") as temp_file:
                temp_file.write(uploaded_file.read())
                temp_filepath = temp_file.name

            with col1:
                st.info("Uploaded PDF File")
                displayPDF(temp_filepath)

            with col2:
                st.info("Summarization")
                input_text = extract_text_from_pdf(temp_filepath)
                if input_text:  # Proceed only if text extraction was successful
                    summary = llm_pipeline(input_text)
                    st.success(summary)

    # Text Input Section
    st.header("Summarize Your Text")
    user_input = st.text_area("Enter your content here:", height=200)
    if st.button("Summarize Text"):
        if user_input.strip():
            col1, col2 = st.columns(2)

            with col1:
                st.info("Original Content")
                st.write(user_input)

            with col2:
                st.info("Summarization")
                summary = llm_pipeline(user_input)
                st.success(summary)
        else:
            st.warning("Please enter some content to summarize.")

if __name__ == '__main__':
    main()