Update app.py

app.py

@@ -4,7 +4,6 @@ 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")
 
@@ -18,7 +17,6 @@ def extract_text_from_pdf(file_path):
 
 def predict_class(text):
     try:
-        # Truncate text to maximum length of 4096 tokens
         max_length = 4096
         truncated_text = text[:max_length]
 
@@ -32,11 +30,9 @@ def predict_class(text):
         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
@@ -44,7 +40,6 @@ class_colors = {
     3: "#d62728"   # Level 4
 }
 
-# Define information for each level
 class_info = {
     0: "Highly cited",
     1: "Average citations",
@@ -57,12 +52,10 @@ 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
     abstract_input = st.text_area("Enter Abstract:")
     full_text_input = st.text_area("Enter Full Text:")
     affiliations_input = st.text_area("Enter Affiliations:")
 
-    # Concatenate inputs with [SEP]
     combined_text = f"{abstract_input} [SEP] {full_text_input} [SEP] {affiliations_input}"
 
     if st.button("Predict"):
@@ -96,12 +89,110 @@ elif option == "PDF":
             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", "Level 2", "Level 3", "Level 4"]
+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)
+
+class_colors = {
+    0: "#1f77b4",  # Level 1
+    1: "#ff7f0e",  # Level 2
+    2: "#2ca02c",  # Level 3
+    3: "#d62728"   # Level 4
+}
+
+st.title("Paper Citation Classifier")
+
+option = st.radio("Select input type:", ("Text", "PDF"))
+
+if option == "Text":
+    abstract_input = st.text_area("Enter Abstract:")
+    full_text_input = st.text_area("Enter Full Text:")
+    affiliations_input = st.text_area("Enter Affiliations:")
+
+    categories = st.multiselect("Select categories:", ["Category 1", "Category 2", "Category 3", "Category 4"])
+
+    combined_text = f"{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", "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"):
+                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)
                         st.text("Predicted Class:")
                         for i, label in enumerate(class_labels):
                             if i == predicted_class: