Update app.py

app.py

@@ -406,8 +406,7 @@ with gr.Blocks() as demo:
                 
             with gr.Tab("Dataset"):
                 gr.Markdown("""
-                    _As we are currently updating this demo, submitting your own data is unavailable for the moment._
-                    
+                    _As we are currently updating this demo, submitting your own data is unavailable for the moment._\
                     _Try out the showcase mode._
                     """)
                 with gr.Row():
@@ -432,9 +431,9 @@ with gr.Blocks() as demo:
                             The data should be in tsv-format with two named columns: the first column (id) should contain the sentence IDs, and the second column (text) should contain the actual texts. Optionally, there is a third column named 'date', which specifies the date associated with the text (e.g., tweet date). This column is necessary when the options 'emotion distribution over time' and 'peaks' are selected. For now, we only accept files with maximum 400 sentences and a limit of 300 tokens per sentence.
                             
                             #### Options
-                            **Emotion frequencies** outputs a bar plot with the prediction frequencies of each emotion category (anger, fear, joy, love, sadness or neutral).
-                            **Emotion distribution over time** outputs a line plot that visualises the frequency of predicted emotions over time for each emotion category.
-                            **Peaks** outputs a step graph that only shows the significant fluctuations (upwards and downwards) in emotion frequencies over time.
+                            **Emotion frequencies** outputs a bar plot with the prediction frequencies of each emotion category (anger, fear, joy, love, sadness or neutral).\
+                            **Emotion distribution over time** outputs a line plot that visualises the frequency of predicted emotions over time for each emotion category.\
+                            **Peaks** outputs a step graph that only shows the significant fluctuations (upwards and downwards) in emotion frequencies over time.\
                             **Topics** uses [BERTopic](https://maartengr.github.io/BERTopic/index.html) to find topics in the datasets, and outputs a bar plot that shows the emotion distribution per topic.
                             """)
                 with gr.Row():