test

app.py

@@ -113,20 +113,20 @@ if option == 'MILESTONE 3':
     df = df.assign(Highest_Toxicity_Class_Except_Toxic=HTCET)
     df = df.assign(Score_Except_Toxic=SET)
 
-    X_train = 'I dont anonymously edit articles at all.'
-    batch = tokenizers[0](X_train, padding=True, truncation=True, max_length=512, return_tensors="pt")
+    # X_train = 'I dont anonymously edit articles at all.'
+    # batch = tokenizers[0](X_train, padding=True, truncation=True, max_length=512, return_tensors="pt")
 
-    with torch.no_grad():
-        outputs = models[0](**batch, labels=torch.tensor([1, 0]))
-        predictions = F.softmax(outputs.logits, dim=1)
-        labels = torch.argmax(predictions, dim=1)
-        labels = [model.config.id2label[label_id] for label_id in labels.tolist()]
-        pred_data.append(predictions)
+    # with torch.no_grad():
+    #     outputs = models[0](**batch, labels=torch.tensor([1, 0]))
+    #     predictions = F.softmax(outputs.logits, dim=1)
+    #     labels = torch.argmax(predictions, dim=1)
+    #     labels = [model.config.id2label[label_id] for label_id in labels.tolist()]
+    #     pred_data.append(predictions)
 
             
     st.table(df)
 
-    st.write(pred_data)
+    # st.write(pred_data)
 
 if option == 'Pipeline':