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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import fitz
import os
# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("REEM-ALRASHIDI/LongFormer-Paper-Citaion-Classifier")
tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-base-4096")
def extract_text_from_pdf(file_path):
text = ''
with fitz.open(file_path) as pdf_document:
for page_number in range(pdf_document.page_count):
page = pdf_document.load_page(page_number)
text += page.get_text()
return text
def predict_class(text):
try:
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
return predicted_class
except Exception as e:
st.error(f"Error during prediction: {e}")
return None
st.title("Paper Citation Classifier")
# Input text boxes for abstract, full text, and affiliations
abstract_input = st.text_area("Enter Abstract:")
full_text_input = st.text_area("Enter Full Text:")
affiliations_input = st.text_area("Enter Affiliations:")
# PDF upload option
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
if uploaded_file is not None:
file_text = extract_text_from_pdf(uploaded_file)
st.text("Extracted Text from PDF:")
st.text(file_text)
# Concatenate inputs with [SEP]
combined_text = f"{abstract_input} [SEP] {full_text_input} [SEP] {affiliations_input} [SEP] {file_text}"
if st.button("Predict"):
predicted_class = predict_class(combined_text)
if predicted_class is not None:
class_labels = ["Level 1", "Level 2", "Level 3", "Level 4"]
st.text(f"Predicted Class: {class_labels[predicted_class]}")
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