s

app.py

@@ -23,14 +23,14 @@ def askcpv(description):
     encoding = {k: v.to(model.device) for k,v in encoding.items()}
     outputs = model(**encoding)
     sigmoid = torch.nn.Sigmoid()
-    probs = sigmoid(outputs.logits.squeeze().cpu()).detach().numpy()
+    probs = sigmoid(outputs.logits.squeeze().cpu())
     values, indices = torch.topk(probs, k=10)
     # turn predicted id's into actual label names
     # predicted_labels = [id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
     # return predicted_labels
-    print("probs: ", probs)
-    print("values: ", values)
-    print("indices: ", indices)
+    print("probs: ", probs.detach().numpy())
+    print("values: ", values.detach().numpy())
+    print("indices: ", indices.detach().numpy())
     print({i: v.item() for i, v in zip(indices, values)})
     return {cpv[i]: v.item() for i, v in zip(indices, values)}