gradio why you do

app.py

@@ -59,14 +59,14 @@ def get_scores(text1, text2):
     return simcse_out, mpnet_out, fuzz_out, tfidf_out
 
 inputs = [
-          gr.inputs.Textbox(lines=5, label="Input Text One"),
-          gr.inputs.Textbox(lines=5, label="Input Text Two")
+          gr.Textbox(lines=5, label="Input Text One"),
+          gr.Textbox(lines=5, label="Input Text Two")
 ]
 outputs = [
-            gr.outputs.Label(type="confidences",label="Cosine similarity based on SimCSE embeddings"),
-            gr.outputs.Label(type="confidences",label="Cosine similarity based on stsb-mpnet embeddings"),
-            gr.outputs.Label(type="confidences",label="Token sort ratio using Levenshtein distance"),
-            gr.outputs.Label(type="confidences",label="Cosine similarity based on tf-idf vectors"),
+            gr.Label(label="Cosine similarity based on SimCSE embeddings"),
+            gr.Label(label="Cosine similarity based on stsb-mpnet embeddings"),
+            gr.Label(label="Token sort ratio using Levenshtein distance"),
+            gr.Label(label="Cosine similarity based on tf-idf vectors"),
 ]
 title = "SimCSE vs MPNet vs thefuzz vs TF-IDF"
 description = "Demo for comparing semantic text similarity methods. Princeton-NLP SimCSE, stsb-mpnet-base-v2 from sentence-transformers (MPnet from Microsoft as the backbone), thefuzz from SeatGeek, and TF-IDF. Interface by Troy Yang."