Group input and output fields into containers

app.py

@@ -52,34 +52,38 @@ def predict_and_decode(model, title='', abstract=''):
 st.header("Paper Category Classifier")
 st.text("Input a title and/or an abstract of a scientific paper, and get classification according to arxiv.org categories")
 
-title_default = "Attention Is All You Need"
-abstract_default = (
-    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks "
-    "in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through "
-    "an attention mechanism. We propose a new simple network architecture, the Transformer..."
-)
-
-line_height = 34
-n_lines = 10
-title = st.text_input("Paper title", value=title_default, help="Type in paper's title")
-abstract = st.text_area("Paper abstract", value=abstract_default, height=line_height*n_lines, help="Type in paper's abstract")
+input_container = st.container(border=True)
+with input_container:
+    title_default = "Attention Is All You Need"
+    abstract_default = (
+        "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks "
+        "in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through "
+        "an attention mechanism. We propose a new simple network architecture, the Transformer..."
+    )
+
+    line_height = 34
+    n_lines = 10
+    title = st.text_input("Paper title", value=title_default, help="Type in paper's title")
+    abstract = st.text_area("Paper abstract", value=abstract_default, height=line_height*n_lines, help="Type in paper's abstract")
 
 if title or abstract:
     result = predict_and_decode(model, title=title, abstract=abstract)
 
-    cnt = st.container(border=True)
-    with cnt:
+    main_cnt = st.container(border=True)
+    with main_cnt:
         st.markdown("#### Top category")
         st.markdown(f"**{result.tag[0]}** -- {result.name[0]}")
         st.markdown(f"Probability: {result.probability[0]*100:.2f}%")
 
-    threshold = 0.55
-    st.text("Other top categories:")
-    max_len = min(max(1, sum(result.iloc[1:].probability > threshold)), 5)
+    rest_cnt = st.container(border=True)
+    with rest_cnt:
+        threshold = 0.55
+        st.text("Other top categories:")
+        max_len = min(max(1, sum(result.iloc[1:].probability > threshold)), 5)
 
-    def format_p(example):
-        example.probability = f"{example.probability * 100 :.2f}%"
-        return example
-    st.table(result.iloc[1:1 + max_len].apply(format_p, axis=1))
+        def format_p(example):
+            example.probability = f"{example.probability * 100 :.2f}%"
+            return example
+        st.table(result.iloc[1:1 + max_len].apply(format_p, axis=1))
 else:
     st.warning("Type a title and/or an abstract to get started!")