Update app.py

app.py

@@ -1,5 +1,4 @@
 import streamlit as st
-#from streamlit_pills import pills
 import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import fitz
@@ -31,11 +30,9 @@ def predict_class(text):
         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
@@ -45,7 +42,6 @@ class_colors = {
 
 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)
@@ -77,16 +73,12 @@ if option == "Text":
     keywords_input = st.text_area("Enter Keywords:")
     options=["Nursing", "Physics", "Maths", "Chemical", "Nuclear", "Engineering" ,"Other"]
     
-    #categories = pills("Select WoS category", options)
-    
-    selected_categories = st.selectbox("Select WoS categories:", options)
+    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(categories)}"
+    combined_text = f"{title_input} [SEP] {keywords_input} [SEP] {abstract_input} [SEP] {full_text_input} [SEP] {affiliations_input} [SEP] {' [SEP] '.join(selected_categories)}"
 
     if st.button("Predict"):
         if not any([title_input, abstract_input,keywords_input, full_text_input, affiliations_input]):