Update app.py

app.py

@@ -371,10 +371,10 @@ def topics(output_file, input_checks):
 
 with gr.Blocks() as demo:
     with gr.Row():
-        with gr.Column(scale=1):
+        with gr.Column(scale=1, min_width=50):
             gr.Markdown("""
             """)
-        with gr.Column(scale=6, min_width=600):
+        with gr.Column(scale=6):
             gr.Markdown("""
                 <div style="text-align: center"><h1>EmotioNL: A framework for Dutch emotion detection</h1></div>
                 
@@ -382,14 +382,14 @@ with gr.Blocks() as demo:
                 
                 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.Column(scale=1):
+        with gr.Column(scale=1, min_width=50):
             gr.Markdown("""
             """)
     with gr.Row():
-        with gr.Column(scale=1):
+        with gr.Column(scale=1, min_width=50):
             gr.Markdown("""
             """)
-        with gr.Column(scale=6, min_width=600):
+        with gr.Column(scale=6):
             with gr.Tab("Sentence"):
                 gr.Markdown("""
                 """)
@@ -469,21 +469,21 @@ with gr.Blocks() as demo:
                 next_button_topics.click(fn=topics, inputs=[output_file,input_checks], outputs=output_topics)
                 send_btn.click(fn=unavailable, inputs=[input_file,input_checks], outputs=[output_markdown,message])
                 demo_btn.click(fn=showcase, inputs=[input_file], outputs=[output_markdown,message,output_file,next_button_freq,next_button_dist,next_button_peaks,next_button_topics])
-        with gr.Column(scale=1):
+        with gr.Column(scale=1, min_width=50):
             gr.Markdown("""
             """)
     
     with gr.Row():
-        with gr.Column(scale=1):
+        with gr.Column(scale=1, min_width=50):
             gr.Markdown("""
             """)
-        with gr.Column(scale=6, min_width=600):
+        with gr.Column(scale=6):
             gr.Markdown("""
                 <font size="2">Both this demo and the dataset have been created by [LT3](https://lt3.ugent.be/), the Language and Translation Technology Team of Ghent University. The EmotioNL project has been carried out with support from the Research Foundation – Flanders (FWO). For any questions, please contact [email protected].</font>
                 
                 <div style="display: flex"><img style="margin-right: 1em" alt="LT3 logo" src="https://lt3.ugent.be/static/images/logo_v2_single.png" width="136" height="58"> <img style="margin-right: 1em" alt="FWO logo" src="https://www.fwo.be/images/logo_desktop.png" height="58"></div>
                 """)
-        with gr.Column(scale=1):
+        with gr.Column(scale=1, min_width=50):
             gr.Markdown("""
             """)
 demo.launch()