app.py

@@ -75,7 +75,8 @@ def classification(text, k):
     else:
         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} (see image below)."
+    text += f"\nThe most likely habitat is '{habitat_name}'."
+    text += f"\n See image of the most likely habitat, {habitat_labels[0]}, below)."
     image_output = return_habitat_image(habitat_labels[0])
     return text, image_output
 
@@ -85,7 +86,6 @@ def masking(text, k):
     
     best_predictions = []
     best_positions = []
-    best_sentences = []
     
     for _ in range(k):
         max_score = 0
@@ -116,14 +116,14 @@ def masking(text, k):
         
         best_predictions.append(best_prediction)
         best_positions.append(best_position)
-        best_sentences.append(best_sentence)
         text_split.insert(best_position, best_prediction)
     if k == 1:
         text = f"The most likely missing species is {best_predictions[0]} (position {best_positions[0]})."
     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_sentences[-1]}. (see image of the most likely species below)."
-    image = return_species_image(best_prediction)
+    text += f"\nThe new vegetation plot is {best_sentence}."
+    text += f"\n See image of the most likely species, {best_predictions[0]}, below)."
+    image = return_species_image(best_prediction[0])
     return text, image
 
 with gr.Blocks() as demo:
@@ -155,7 +155,7 @@ with gr.Blocks() as demo:
         gr.Markdown("""<h5 style="text-align: center;">An example of input</h5>""")
         gr.Examples([["vaccinium myrtillus, dryopteris dilatata, molinia caerulea", 1]], [species_masking, k_masking], [text_masking, image_masking], masking, True)
 
-    button_classification.click(classification, inputs=[species_classification, k_classification], outputs=[textclassification, image_classification])
+    button_classification.click(classification, inputs=[species_classification, k_classification], outputs=[text_classification, image_classification])
     button_masking.click(masking, inputs=[species_masking, k_masking], outputs=[text_masking, image_masking])
 
 demo.launch()