update Gradio components

app.py

@@ -6,7 +6,7 @@ from uq import BertForUQSequenceClassification
 def predict(sentence):
     model_path = "tombm/bert-base-uncased-finetuned-cola"
     classifier = pipeline("text-classification", model=model_path, tokenizer=model_path)
-    label = classifier(sentence)
+    label = classifier(sentence)[0]["label"]
     return label
 
 
@@ -19,7 +19,7 @@ def uncertainty(sentence):
     model.return_gp_cov = True
     _, gp_cov = model(**test_input)
 
-    return str(gp_cov.item())
+    return gp_cov.item()
 
 
 with gr.Blocks() as demo:
@@ -32,7 +32,7 @@ with gr.Blocks() as demo:
     gr.Interface(
         fn=predict,
         inputs=gr.Textbox(value="Good morning.", label="Input"),
-        outputs="label",
+        outputs="text",
     )
     gr.Interface(
         fn=predict,
@@ -40,7 +40,7 @@ with gr.Blocks() as demo:
             value="This sentence is sentence, this is a correct sentence!",
             label="Input",
         ),
-        outputs="label",
+        outputs="text",
     )
 
     explain_str = """As we can see, our model correctly classifies the first sentence, but misclassifies the second.
@@ -50,7 +50,7 @@ with gr.Blocks() as demo:
     gr.Interface(
         fn=uncertainty,
         inputs=gr.Textbox(value="Good morning.", label="Input"),
-        outputs="text",
+        outputs=gr.Number(label="Variance from GP head"),
     )  # should have low uncertainty
     gr.Interface(
         fn=uncertainty,
@@ -58,7 +58,7 @@ with gr.Blocks() as demo:
             value="This sentence is sentence, this is a correct sentence!",
             label="Input",
         ),
-        outputs="text",
+        outputs=gr.Number(label="Variance from GP head"),
     )  # should have high uncertainty
 
     final_str = """We can see here that the variance for the misclassified example is much higher than for the correctly