Update app.py

app.py

@@ -4,6 +4,7 @@ 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")
 
@@ -17,6 +18,7 @@ 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]
 
@@ -30,105 +32,11 @@ 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)
 
-class_colors = {
-    0: "#1f77b4",  # Level 1
-    1: "#ff7f0e",  # Level 2
-    2: "#2ca02c",  # Level 3
-    3: "#d62728"   # Level 4
-}
-
-class_info = {
-    0: "Highly cited",
-    1: "Average citations",
-    2: "More citations",
-    3: "Low citations"
-}
-
-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:")
-
-    combined_text = f"{abstract_input} [SEP] {full_text_input} [SEP] {affiliations_input}"
-
-    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
-                        )
-                        st.text(class_info[predicted_class])
-                    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:
-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)
-
+# Define colors for different classes
 class_colors = {
     0: "#1f77b4",  # Level 1
     1: "#ff7f0e",  # Level 2
@@ -141,12 +49,15 @@ 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:")
 
+    # Select categories using pills
     categories = st.multiselect("Select categories:", ["Category 1", "Category 2", "Category 3", "Category 4"])
 
+    # Combine selected categories with [SEP]
     combined_text = f"{abstract_input} [SEP] {full_text_input} [SEP] {affiliations_input} [SEP] {' [SEP] '.join(categories)}"
 
     if st.button("Predict"):
@@ -179,6 +90,7 @@ 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)
@@ -193,13 +105,7 @@ elif option == "PDF":
                                 )
                             else:
                                 st.text(label)
-                        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
-                                )
-                                st.text(class_info[predicted_class])
-                            else:
-                                st.text(label)
+
+
+
+