Update app.py

app.py

@@ -8,8 +8,8 @@ from typing import Dict
 
 
 num_labels=14
-#model = BertForSequenceClassification.from_pretrained("owaiskha9654/Multi-Label-Classification-of-PubMed-Articles", num_labels=num_labels)
-#tokenizer = BertTokenizer.from_pretrained('owaiskha9654/Multi-Label-Classification-of-PubMed-Articles', do_lower_case=True) 
+model = BertForSequenceClassification.from_pretrained("owaiskha9654/Multi-Label-Classification-of-PubMed-Articles", num_labels=num_labels)
+tokenizer = BertTokenizer.from_pretrained('owaiskha9654/Multi-Label-Classification-of-PubMed-Articles', do_lower_case=True) 
 
 
 def Multi_Label_Classification_of_Pubmed_Articles(model_input: str) -> Dict[str, float]:
@@ -20,7 +20,6 @@ def Multi_Label_Classification_of_Pubmed_Articles(model_input: str) -> Dict[str,
     b_logit_pred = outs[0]
     pred_label = torch.sigmoid(b_logit_pred)
     
-    #prediction = model.predict(tokenized)[0]
     ret ={
         "Anatomy [A]": float(pred_label[0][0]),
         "Organisms [B]": float(pred_label[0][1]),
@@ -36,11 +35,7 @@ def Multi_Label_Classification_of_Pubmed_Articles(model_input: str) -> Dict[str,
         "Named Groups [M]": float(pred_label[0][11]),
         "Health Care [N]": float(pred_label[0][12]),
         "Geographicals [Z]": float(pred_label[0][13])}
-    #ret = {
-    #    "negative": float(prediction[0]),
-    #    "positive": float(prediction[1])
-    #}
-    return pred_labels
+    return ret
 
 
 model_input = gr.Textbox("Input text here", show_label=False)