Update app.py

app.py

@@ -21,20 +21,20 @@ def Multi_Label_Classification_of_Pubmed_Articles(model_input: str) -> Dict[str,
     pred_label = torch.sigmoid(b_logit_pred)
     
     ret ={
-        "Anatomy [A]": float(pred_label[0][0]),
-        "Organisms [B]": float(pred_label[0][1]),
-        "Diseases [C]": float(pred_label[0][2]),
-        "Chemicals and Drugs [D]": float(pred_label[0][3]),
+        "Anatomy [A]": float(pred_label[10][0]),
+        "Organisms [B]": float(pred_label[10][1]),
+        "Diseases [C]": float(pred_label[10][2]),
+        "Chemicals and Drugs [D]": float(pred_label[10][3]),
         "Analytical, Diagnostic and Therapeutic Techniques, and Equipment [E]": float(pred_label[0][4]),
-        "Psychiatry and Psychology [F]": float(pred_label[0][5]),
-        "Phenomena and Processes [G]": float(pred_label[0][6]),
-        "Disciplines and Occupations [H]": float(pred_label[0][7]),
+        "Psychiatry and Psychology [F]": float(pred_label[10][5]),
+        "Phenomena and Processes [G]": float(pred_label[10][6]),
+        "Disciplines and Occupations [H]": float(pred_label[10][7]),
         "Anthropology, Education, Sociology, and Social Phenomena [I]": float(pred_label[0][8]),
-        "Technology, Industry, and Agriculture [J]": float(pred_label[0][9]),
-        "Information Science [L]": float(pred_label[0][10]),
-        "Named Groups [M]": float(pred_label[0][11]),
-        "Health Care [N]": float(pred_label[0][12]),
-        "Geographicals [Z]": float(pred_label[0][13])}
+        "Technology, Industry, and Agriculture [J]": float(pred_label[10][9]),
+        "Information Science [L]": float(pred_label[10][10]),
+        "Named Groups [M]": float(pred_label[10][11]),
+        "Health Care [N]": float(pred_label[10][12]),
+        "Geographicals [Z]": float(pred_label[10][13])}
     return ret