test

app.py

@@ -89,7 +89,7 @@ if option == 'MILESTONE 3':
 
 
 
-    # pred_data = []
+    pred_data = []
 
     # for i in range(10):
     #     X_train = pre_populated_tweets[i]
@@ -112,10 +112,21 @@ if option == 'MILESTONE 3':
     df = df.assign(Score_Overall=SO)
     df = df.assign(Highest_Toxicity_Class_Except_Toxic=HTCET)
     df = df.assign(Score_Except_Toxic=SET)
+
+    X_train = pre_populated_tweets[0]
+    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)
+
             
     st.table(df)
 
-    st.write('test2')
+    st.write(pred_data)
 
 if option == 'Pipeline':