Update the app layout with Blocks

app.py

@@ -6,22 +6,30 @@ from main_idea_with_pipeline import predict_mainidea_sent
 config = AutoConfig.from_pretrained("yutingg/custom-distill-bert-for-sentence-label", trust_remote_code=True)
 model = AutoModel.from_pretrained("yutingg/custom-distill-bert-for-sentence-label", trust_remote_code=True, config=config)
 
-def greet(essay):
+def predict_main_idea(essay):
     ret = predict_mainidea_sent(essay, model), predict_mainidea_sent_old(essay, model)
     return ret
 
-iface = gr.Interface(fn=greet, inputs="text", outputs=[
-    gr.Dataframe(
-        label="pipeline output",
-        headers=['label: is main idea', 'sentence'],
-        datatype=["str", "str"],
-        col_count=(2, "fixed"),
-    ),
-    gr.Dataframe(
-        label="torch output with Triage",
-        headers=['label: is main idea', 'sentence'],
-        datatype=["str", "str"],
-        col_count=(2, "fixed"),
-    )
-])
-iface.launch()
+with gr.Blocks() as main_idea_demo:
+    with gr.Row():
+        essay_input = gr.Textbox(label="essay", lines=10)
+    with gr.Row():
+        predict_button = gr.Button("Predict Main Idea Sentence")
+    with gr.Row():
+        with gr.Column(scale=1, min_width=600):
+            output_1 = gr.Dataframe(
+                label="pipeline output",
+                headers=['label: is main idea', 'sentence'],
+                datatype=["str", "str"],
+                col_count=(2, "fixed"),
+            )
+        with gr.Column(scale=1, min_width=600):
+            output_2 = gr.Dataframe(
+                label="torch output with Triage",
+                headers=['label: is main idea', 'sentence'],
+                datatype=["str", "str"],
+                col_count=(2, "fixed"),
+            )
+    predict_button.click(predict_main_idea, inputs=essay_input, outputs=[output_1, output_2])
+
+main_idea_demo.launch()