Update inference.py

inference.py

@@ -11,11 +11,14 @@ text_pipeline = lambda x: vocab(tokenizer(x))
 num_class = 11
 vocab_size = len(vocab)
 embed_size = 300
-lr = 0.4
 
 model = TextClassifierModel(vocab_size, embed_size, num_class)
-optimizer = torch.optim.SGD(model_test.parameters(), lr=0.4)
 
+model = load_state_dict(model, model_trained, vocab)
 
-model, optimizer = load_state_dict(model, optimizer, model_trained, vocab)
+def predict(text, model=model, text_pipeline=text_pipeline):
+    with torch.no_grad()
+    model.eval()
+    text_tensor = torch.tensor(text_pipeline(text))
+    return model(text_tensor, torch.tensor([0])).argmax(1).item()