Update app.py

app.py

@@ -44,6 +44,14 @@ class_colors = {
     3: "#d62728"   # Level 4
 }
 
+# Define information for each level
+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"))
@@ -58,36 +66,49 @@ if option == "Text":
     combined_text = f"{abstract_input} [SEP] {full_text_input} [SEP] {affiliations_input}"
 
     if st.button("Predict"):
-        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:")
-            st.markdown(
-                f'<div style="background-color: {class_colors[predicted_class]}; padding: 10px; border-radius: 5px; color: white; font-weight: bold;">{class_labels[predicted_class]}</div>',
-                unsafe_allow_html=True
-            )
+        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:
-        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)
+        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)
 
-        # Provide an option to predict from PDF text
-        if st.button("Predict from PDF Text"):
-            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:")
-                st.markdown(
-                    f'<div style="background-color: {class_colors[predicted_class]}; padding: 10px; border-radius: 5px; color: white; font-weight: bold;">{class_labels[predicted_class]}</div>',
-                    unsafe_allow_html=True
-                )
+            # 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"]
+                        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)