from dotenv import load_dotenv
load_dotenv()
from tempfile import NamedTemporaryFile
import os
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 pipeline
import torch
import base64


# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
base_model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")

#file loader and processing 
def file_preprocessing(file):
    loader = PyPDFLoader(file)
    pages = loader.load_and_split()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, 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_pipleline(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
def llm_pipleline1(ans):
    pipe_sum = pipeline(
        'summarization',
        model = base_model,
        tokenizer = tokenizer,
        max_length = 500,
        min_length = 50      
    )
    input_text =""+ ans
    result = pipe_sum(input_text)
    result = result[0]['summary_text']
    return result

@st.cache_data
# Function to display the PDF file
def displayPDF(file):
    # Opening file from file path
    with open(file, "rb") as f:
        base_pdf = base64.b64encode(f.read()).decode('utf-8')  # Corrected function name and variable

    # Embedding PDF in HTML
    pdf_display = f'<iframe src="data:application/pdf;base64,{base_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
    
    # Displaying the file
    st.markdown(pdf_display, unsafe_allow_html=True)


#streamlit code
st.set_page_config(layout='wide')

def main():
    st.title('Content Summarizer')

    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)

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

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

            with col2:
                st.info("Summarization is below")
                summary = llm_pipleline(temp_filepath)
                st.success(summary)

    # New Section for Text Input Summarization
    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 is below")
                summary = llm_pipleline1(user_input)
                st.success(summary)
        else:
            st.warning("Please enter some content to summarize.")

if __name__ == '__main__':
    main()