Update app.py

app.py

@@ -272,14 +272,16 @@ def topics(output_file, input_checks):
     return gr.Plot.update(value=plot, visible=True)  # no next_button becomes available
 
 with gr.Blocks() as demo:
-    with gr.Tab("Sentence"):
-        gr.Markdown("""
+    gr.Markdown("""
                 <div style="text-align: center"><h1>EmotioNL: A framework for Dutch emotion detection</h1></div>
                 
                 <div style="display: block;margin-left: auto;margin-right: auto;width: 60%;"><img alt="EmotioNL logo" src="https://users.ugent.be/~lundbruy/EmotioNL.png" width="100%"></div>
                 
                 This demo was made to demonstrate the EmotioNL model, a transformer-based classification model that analyses emotions in Dutch texts. The model uses [RobBERT](https://github.com/iPieter/RobBERT), which was further fine-tuned on the [EmotioNL dataset](https://lt3.ugent.be/resources/emotionl/). The resulting model is a classifier that, given a sentence, predicts one of the following emotion categories: _anger_, _fear_, _joy_, _love_, _sadness_ or _neutral_. The demo can be used either in **sentence mode**, which allows you to enter a sentence for which an emotion will be predicted; or in **dataset mode**, which allows you to upload a dataset or see the full functuonality of with example data.
                 """)
+    with gr.Tab("Sentence"):
+        gr.Markdown("""
+                    """)
         with gr.Row():
             with gr.Column():
                 input = gr.Textbox(