Upload app.py

app.py

@@ -0,0 +1,133 @@
+import streamlit as st
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import fitz
+import os
+
+model = AutoModelForSequenceClassification.from_pretrained("Reem333/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="logo.png")
+
+with st.sidebar:
+    st.image("logo.png", width=70)
+    st.markdown('<div style="position: absolute; left: 5px;"></div>', unsafe_allow_html=True)
+    
+    st.markdown("# Paper Citation Classifier")
+    st.markdown("---")
+    st.markdown("## About")
+    st.markdown('''
+    This is a tool to classify paper citations into different levels based on their number of citations.
+    Powered by Fine-Tuned [Longformer model](https://huggingface.co/REEM-ALRASHIDI/LongFormer-Paper-Citaion-Classifier) with custom data.
+    ''')
+    st.markdown("### Class Levels:")
+    st.markdown("- Level 1: Highly cited papers")
+    st.markdown("- Level 2: Average cited papers")
+    st.markdown("- Level 3: More cited papers")
+    st.markdown("- Level 4: Low cited papers")
+    st.markdown("---")
+    st.markdown('Tabuk University')
+
+st.title("Check Your Paper Now!")
+
+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:")
+    keywords_input = st.text_area("Enter Keywords:")
+    options=["Nursing", "Physics", "Maths", "Chemical", "Nuclear", "Engineering" ,"Other"]
+    
+    selected_category = st.selectbox("Select WoS categories:", options)
+    if selected_category == "Other":
+        custom_category = st.text_input("Enter custom category:")
+        selected_category = custom_category if custom_category else "Other"
+
+    combined_text = f"{title_input} [SEP] {keywords_input} [SEP] {abstract_input} [SEP] {full_text_input} [SEP] {affiliations_input} [SEP] {' [SEP] '.join(selected_category)}"
+
+    if st.button("Predict"):
+        if not any([title_input, abstract_input,keywords_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", "Level 2", "Level 3", "Level 4"]
+
+                    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", "Level 2", "Level 3", "Level 4"]
+                            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)