app.py

@@ -76,7 +76,7 @@ def classification(text, k):
         text = f"This vegetation plot belongs to the habitats {', '.join(habitat_labels[:-1])} and {habitat_labels[-1]}."
     habitat_name = eunis_habitats[eunis_habitats['EUNIS 2020 code'] == habitat_labels[0]]['EUNIS-2021 habitat name'].values[0]
     text += f"\nThe most likely habitat is '{habitat_name}'."
-    text += f"\n See image of the most likely habitat, {habitat_labels[0]}, below)."
+    text += f"\nSee an image of this habitat ({habitat_labels[0]}) below."
     image_output = return_habitat_image(habitat_labels[0])
     return text, image_output
 
@@ -122,7 +122,7 @@ def masking(text, k):
     else:
         text = f"The most likely missing species are {', '.join(best_predictions[:-1])} and {best_predictions[-1]} (positions {', '.join(map(str, best_positions[:-1]))} and {best_positions[-1]})."
     text += f"\nThe new vegetation plot is {best_sentence}."
-    text += f"\n See image of the most likely species, {best_predictions[0]}, below)."
+    text += f"\nSee an image of the most likely species ({best_predictions[0]}) below."
     image = return_species_image(best_prediction[0])
     return text, image
 
@@ -146,8 +146,9 @@ with gr.Blocks() as demo:
     with gr.Tab("Missing species finding"):
         gr.Markdown("""<h3 style="text-align: center;">Finding the missing species!</h3>""")
         with gr.Row():
-            species_masking = gr.Textbox(lines=2, label="Species", placeholder="Enter a list of comma-separated binomial names here.")
-            k_masking = gr.Slider(1, 5, value=1, step=1, label="Top-k", info="Choose the number of top missing species to find.")
+            with gr.Column():
+                species_masking = gr.Textbox(lines=2, label="Species", placeholder="Enter a list of comma-separated binomial names here.")
+                k_masking = gr.Slider(1, 5, value=1, step=1, label="Top-k", info="Choose the number of top missing species to find.")
             with gr.Column():
                 text_masking = gr.Textbox()
                 image_masking = gr.Image()