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bdac891 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import streamlit as st
# import langchain
import PyPDF2
import os
from transformers import BartTokenizer , BartForConditionalGeneration
tokenizer = BartTokenizer.from_pretrained("facebook/bart-large-cnn")
model = BartForConditionalGeneration.from_pretrained("facebook/bart-large-cnn")
def save_uploaded_file(uploaded_file):
temp_dir = "temp_files"
os.makedirs(temp_dir, exist_ok=True)
file_path = os.path.join(temp_dir, uploaded_file.name)
with open(file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
return file_path
# Function to extract text from PDF
def extract_text_from_pdf(pdf_file):
text = ""
RP_file = save_uploaded_file(pdf_file)
with open(RP_file, "rb") as file:
pdf_reader = PyPDF2.PdfReader(file)
num_pages = len(pdf_reader.pages)
for page_num in range(num_pages):
page = pdf_reader.pages[page_num]
text += page.extract_text()
return text
def generate_summary(text: str):
# Tokenize the text
tokens = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(tokens.input_ids, num_beams = 4, max_length = 200, early_stopping = True)
return summary_ids
# Function to summarize text
def summarize_text(text: str) -> str:
summary_ids = generate_summary(text)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_ip_tokenization_spaces=False)
return summary
# Function to extract key information from the paper
def extract_paper_info(text):
# Logic to extract key information from the paper (e.g., using regex, NLP techniques)
# This part can be expanded based on the specific requirements
pass
# Function to build and fine-tune the chatbot
def build_chatbot():
# Fine-tuning language model for chatbot using Langchain
lang_model = ''
# Additional fine-tuning steps can be added here
return lang_model
# Main function to run the Streamlit app
def main():
st.title("Research Paper Understanding Chatbot")
st.write("As of now supports only summarization.")
# Upload PDF file
uploaded_file = st.file_uploader("Upload a research paper (PDF)", type="pdf")
if uploaded_file is not None:
st.write("Paper uploaded successfully!")
# Extract text from PDF
text = extract_text_from_pdf(uploaded_file)
# Display summary of the paper
st.subheader("Summary of the Paper")
with st.spinner("Brewing a potion for your paper's essence..."):
summary = summarize_text(text)
st.write(summary)
# # Extract key information from the paper
# st.subheader("Key Information")
# paper_info = extract_paper_info(text)
# st.write(paper_info)
# # Build chatbot
# st.subheader("Chatbot")
# chatbot = build_chatbot()
# # Chat interface
# user_input = st.text_input("You: ")
# if user_input:
# response = chatbot.generate_response(user_input)
# st.write("Chatbot:", response)
else:
st.write("Please upload a PDF file")
if __name__ == "__main__":
main()
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