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import streamlit as st
from streamlit_pills import pills
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import fitz
import os
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:
max_length = 4096
truncated_text = text[:max_length]
inputs = tokenizer(truncated_text, return_tensors="pt", padding=True, truncation=True, max_length=max_length)
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
uploaded_files_dir = "uploaded_files"
os.makedirs(uploaded_files_dir, exist_ok=True)
class_colors = {
0: "#d62728", # Level 1
1: "#ff7f0e", # Level 2
2: "#2ca02c", # Level 3
3: "#1f77b4" # Level 4
}
st.set_page_config(page_title="Paper Citation Classifier", page_icon="logo2.png")
st.image("logo2.png", width=70)
st.markdown('<div style="position: absolute; top: 5px; left: 5px;"></div>', unsafe_allow_html=True)
# col1, col2 = st.columns([1, 3])
st.title("Paper Citation Classifier")
option = st.radio("Select input type:", ("Text", "PDF"))
if option == "Text":
title_input = st.text_area("Enter Title:")
abstract_input = st.text_area("Enter Abstract:")
full_text_input = st.text_area("Enter Full Text:")
affiliations_input = st.text_area("Enter Affiliations:")
options=["Nursing", "Physics", "Maths", "Chemical", "Nuclear", "Engineering" ,"Other"]
categories = pills("Select WoS category", options)
# categories = st.multiselect("Select WoS categories:", options)
combined_text = f"{title_input} [SEP] {abstract_input} [SEP] {full_text_input} [SEP] {affiliations_input} [SEP] {' [SEP] '.join(categories)}"
if st.button("Predict"):
if not any([title_input, abstract_input, full_text_input, affiliations_input]):
st.warning("Please enter paper text.")
else:
with st.spinner("Predicting..."):
predicted_class = predict_class(combined_text)
if predicted_class is not None:
class_labels = ["Level 1 (Highly Cited Paper)", "Level 2 (Average Cited Paper)", "Level 3 (More Cited Paper)", "Level 4 (Low Cited Paper)"]
st.text("Predicted Class:")
for i, label in enumerate(class_labels):
if i == predicted_class:
st.markdown(
f'<div style="background-color: {class_colors[predicted_class]}; padding: 10px; border-radius: 5px; color: white; font-weight: bold;">{label}</div>',
unsafe_allow_html=True
)
else:
st.text(label)
elif option == "PDF":
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
if uploaded_file is not None:
with st.spinner("Processing PDF..."):
file_path = os.path.join(uploaded_files_dir, uploaded_file.name)
with open(file_path, "wb") as f:
f.write(uploaded_file.getbuffer())
st.success("File uploaded successfully.")
st.text(f"File Path: {file_path}")
file_text = extract_text_from_pdf(file_path)
st.text("Extracted Text:")
st.text(file_text)
if st.button("Predict from PDF Text"):
if not file_text.strip():
st.warning("Please upload a PDF with text content.")
else:
with st.spinner("Predicting..."):
predicted_class = predict_class(file_text)
if predicted_class is not None:
class_labels = ["Level 1 (Highly Cited Paper)", "Level 2 (Average Cited Paper)", "Level 3 (More Cited Paper)", "Level 4 (Low Cited Paper)"]
st.text("Predicted Class:")
for i, label in enumerate(class_labels):
if i == predicted_class:
st.markdown(
f'<div style="background-color: {class_colors[predicted_class]}; padding: 10px; border-radius: 5px; color: white; font-weight: bold;">{label}</div>',
unsafe_allow_html=True
)
else:
st.text(label)
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