offensive

app.py

@@ -37,6 +37,11 @@ This box presents a detailed breakdown of the evaluation for each model.
 (Esta caixa apresenta um detalhamento da avaliação para cada modelo.)
 """ }
 
+short_score_descriptions = {
+   0: "Not offensive",
+   1: "Offensive"
+}
+
 score_descriptions = {
     0: "This text is not offensive.",
     1: "This text is offensive.",
@@ -96,14 +101,16 @@ def predict(s1, chosen_model):
               logits = softmax(logits).tolist()
               break
     def get_description(idx):
-      description = score_descriptions[idx]
+      description = short_score_descriptions[idx]
       description_pt = score_descriptions_pt[idx]
       final_description = description + "\n \n" + description_pt
       return final_description
     
-    scores = { get_description(k):v for k,v in enumerate(logits) }
+    max_pos = logits.index(max(logits))
+    markdown_description = f"# **{short_score_descriptions[max_pos]}** \n \n" + get_description(max_pos)
+    scores = { short_score_descriptions[k]:v for k,v in enumerate(logits) }
 
-    return scores
+    return markdown_description, scores
 
 
 inputs = [
@@ -112,6 +119,7 @@ inputs = [
 ]
 
 outputs = [
+ gr.Markdown(),
  gr.Label(label="Result")
 ]