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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
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import base64
#model and tokenizer
# load the model & tokenizer
#checkpoint = "LaMini-Flan-T5-248M"
#tokenizer = T5Tokenizer.from_pretrained(checkpoint)
#base_model = T5ForConditionalGeneration.from_pretrained(checkpoint, device_map='auto', torch_dtype=torch.float32)
# Load model directly
tokenizer = AutoTokenizer.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
base_model = AutoModelForSeq2SeqLM.from_pretrained("MBZUAI/LaMini-Flan-T5-248M")
#file loader and 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:
final_texts = final_texts + text.page_content
return final_texts, len(final_texts)
# LLM pipeline- using summarization pipleine
def llm_pipeline(filepath):
input_text, input_length = file_preprocessing(filepath)
pipe_sum = pipeline(
'summarization',
model = base_model,
tokenizer = tokenizer,
max_length = input_length//5,
min_length = 25)
result = pipe_sum(input_text)
result = result[0]['summary_text']
return result
@st.cache_data #to improve performance by caching
def displayPDF(file):
# Opening file from file path as read binary
with open(file, "rb") as f:
base64_pdf = base64.b64encode(f.read()).decode('utf-8')
# Embedding PDF file in the web browser
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(page_title='pdf insight',layout="wide",page_icon="📃",initial_sidebar_state="expanded")
def main():
st.title("PDF Insight")
uploaded_file = st.file_uploader("Upload the PDF", type=['pdf'])
if uploaded_file is not None:
if st.button("Summarize"):
col1, col2 = st.columns([0.4,0.6])
filepath = "uploaded_pdfs/"+uploaded_file.name
with open(filepath, "wb") as temp_file:
temp_file.write(uploaded_file.read())
with col1:
st.info("Uploaded PDF")
pdf_view = displayPDF(filepath)
with col2:
summary = llm_pipeline(filepath)
st.info("Summarization")
st.success(summary)
#initializing the app
if __name__ == "__main__":
main()
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