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import streamlit as st
import pandas as pd
import numpy as np
import os
import torch
from grobidmonkey import reader
from transformers import pipeline
from transformers import BartTokenizer, BartModel, BartForConditionalGeneration
from transformers import T5Tokenizer, T5ForConditionalGeneration
from document import Document
from BartSE import BARTAutoEncoder
def save_uploaded_file(uploaded_file):
file_path = os.path.join("./uploads", uploaded_file.name)
os.makedirs("./uploads", exist_ok=True) # Create 'uploads' directory if it doesn't exist
with open(file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
return file_path # Return the file path as a string
st.title('Paper2Slides')
st.subheader('Upload paper in pdf format')
col1, col2 = st.columns([3, 1])
with col1:
uploaded_file = st.file_uploader("Choose a file")
with col2:
option = st.selectbox(
'Select parsing method.',
('monkey', 'x2d', 'lxml'))
if uploaded_file is not None:
st.write(uploaded_file.name)
bytes_data = uploaded_file.getvalue()
st.write(len(bytes_data), "bytes")
saved_file_path = save_uploaded_file(uploaded_file)
monkeyReader = reader.MonkeyReader(option)
outline = monkeyReader.readOutline(saved_file_path)
for pre, fill, node in outline:
st.write("%s%s" % (pre, node.name))
# read paper content
essay = monkeyReader.readEssay(saved_file_path)
for key, values in essay.items():
st.write(f"{key}: {', '.join(values)}")
# with st.status("Understanding paper..."):
# Barttokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
# summ_model_path = 'com3dian/Bart-large-paper2slides-summarizer'
# summarizor = BartForConditionalGeneration.from_pretrained(summ_model_path)
# exp_model_path = 'com3dian/Bart-large-paper2slides-expander'
# expandor = BartForConditionalGeneration.from_pretrained(exp_model_path)
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# BartSE = BARTAutoEncoder(summarizor, summarizor, device)
# del summarizor, expandor
# document = Document(essay, Barttokenizer)
# del Barttokenizer
# length = document.merge(25, 30, BartSE, device)
# with st.status("Generating slides..."):
# summarizor = pipeline("summarization", model=summ_model_path, device = device)
# summ_text = summarizor(document.segmentation['text'], max_length=100, min_length=10, do_sample=False)
# summ_text = [text['summary_text'] for text in summ_text]
# for summ in summ_text:
# st.write(summ)
# Function to render HTML content
def render_html(text):
return f"<div>{text}</div>"
# Initialize session state for page index
if 'page_index' not in st.session_state:
st.session_state.page_index = 0
# Function to handle page turn
def turn_page(direction):
if direction == "next" and st.session_state.page_index < len(text_list) - 1:
st.session_state.page_index += 1
elif direction == "prev" and st.session_state.page_index > 0:
st.session_state.page_index -= 1
# Display page turner controls
col1, col2, col3 = st.columns([1, 2, 1])
with col1:
st.button("Previous", on_click=turn_page, args=("prev",))
with col3:
st.button("Next", on_click=turn_page, args=("next",))
with col2:
st.write(f"Page {st.session_state.page_index + 1} of {len(text_list)}")
# Display editable text box
text = st.text_area("Edit Text", summ_text[st.session_state.page_index], height=200)
# Display HTML box
st.markdown(render_html(text), unsafe_allow_html=True)
# Update list with edited text
text_list[st.session_state.page_index] = text
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