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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:
# Truncate text to maximum length of 4096 tokens
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
# Create a directory to store uploaded files
uploaded_files_dir = "uploaded_files"
os.makedirs(uploaded_files_dir, exist_ok=True)
# Define colors for different classes
class_colors = {
0: "#1f77b4", # Level 1
1: "#ff7f0e", # Level 2
2: "#2ca02c", # Level 3
3: "#d62728" # Level 4
}
st.set_page_config("Paper Citation Calssifier")
st.title("Paper Citation Classifier")
option = st.radio("Select input type:", ("Text", "PDF"))
if option == "Text":
# Input text boxes for abstract, full text, and affiliations
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" ,"Other"]
categories = pills("Select a category", options)
# categories = st.multiselect("Select WoS categories:", ["Nursing", "Physics", "Maths", "Chemical", "Nuclear" ,"Other"])
# Combine selected categories with [SEP]
combined_text = f"{title_input} [SEP] {abstract_input} [SEP] {full_text_input} [SEP] {affiliations_input} [SEP] {' [SEP] '.join(categories)}"
if st.button("Predict"):
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)
# Provide an option to predict from PDF text
if st.button("Predict from PDF Text"):
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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