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import streamlit as st
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)
st.title("Paper Citation Classifier")
option = st.radio("Select input type:", ("Text", "PDF"))
if option == "Text":
text_input = st.text_area("Enter your text here:")
if st.button("Predict") and text_input.strip():
predicted_class = predict_class(text_input)
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]}")
elif option == "PDF":
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
if uploaded_file is not None:
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"):
predicted_class = predict_class(file_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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