Upload 2 files

app.py

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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]}")

requirements.txt

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+streamlit
+torch
+transformers
+PyMuPDF
+PyPDFium2