testmodel

app.py

@@ -61,7 +61,7 @@ if option == 'MILESTONE 3':
     batch_0 = tokenizer_0(X_train, padding=True, truncation=True, max_length=512, return_tensors="pt")
 
     with torch.no_grad():
-        outputs = model(**batch_0, labels=torch.tensor([1, 0]))
+        outputs = model_0(**batch_0, 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()]
@@ -87,6 +87,18 @@ if option == 'MILESTONE 3':
     HTCET = [0]*10
     SET = [0]*10
 
+    # for i in range(10):
+    #     X_train = pre_populated_tweets[i]
+    #     batch = tokenizer_0(X_train, padding=True, truncation=True, max_length=512, return_tensors="pt")
+
+    # with torch.no_grad():
+    #     outputs = model(**batch_0, 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()]
+
+
+
     df = df.assign(Tweet=pre_populated_tweets)
     df = df.assign(Highest_Toxicity_Class_Overall=HTCO)
     df = df.assign(Score_Overall=SO)