Update app.py

app.py

@@ -46,10 +46,16 @@ def Page():
       top_10 = torch.topk(scores, 10)
       bottom_10 = torch.topk(scores, 10, largest=False)
       df = pd.DataFrame()
-      df["probs"] = top_10.values[0]+bottom_10.values[0]
+      d2 = pd.DataFrame
+      df["probs"] = top_10.values[0]
+      d2["probs"] = bottom_10.values[0]
       df["probs"] = [f"{value:.2%}" for value in df["probs"].values]
-      df["next token ID"] = [top_10.indices[0][i].numpy() for i in range(10)]+[bottom_10.indices[0][i].numpy() for i in range(10)]
-      df["predicted next token"] = [tokenizer.decode(top_10.indices[0][i]) for i in range(10)]+[tokenizer.decode(bottom_10.indices[0][i])+10 for i in range(10)]
+      d2["probs"] = [f"{value:.2%}" for value in d2["probs"].values]
+      df["next token ID"] = [top_10.indices[0][i].numpy() for i in range(10)]
+      d2["next token ID"] = [bottom_10.indices[0][i].numpy() for i in range(10)]
+      df["predicted next token"] = [tokenizer.decode(top_10.indices[0][i]) for i in range(10)]
+      d2["predicted next token"] = [tokenizer.decode(bottom_10.indices[0][i]) for i in range(10)]
       solara.Markdown("###Prediction")
-      solara.DataFrame(df, items_per_page=20, cell_actions=cell_actions)
+      solara.DataFrame(df, items_per_page=10, cell_actions=cell_actions)
+      solara.DataFrame(d2, items_per_page=10, cell_actions=cell_actions)
 Page()