from dotenv import load_dotenv
load_dotenv()

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 sumerizer')
    
    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_pipleline(filepath)
                st.success(summary)
    else :
        print("enter a valid pdf file")

    if st.button("text"):
        ans = input("enter your content")
        if st.button("Enter"):
            col1,col2 = st.columns(2)

            with col1:
                st.info("what you have entered")
                print(ans)                   
            with col2:
                st.info("Summarization is below")
                summary1=llm_pipleline1(ans)
                st.success(summary1)



                    
            
if __name__ == '__main__':
    main()            error is ameError: name 'base64_pdf' is not defined