add probs

interfaces/cap_minor.py

@@ -32,7 +32,7 @@ domains = {
     "local government agenda": "localgovernment"
 }
 
-def convert_minor_to_major(results):
+def convert_minor_to_major(results, probs):
     results_as_text = dict()
     for i in results:
         prob = probs[i]
@@ -73,7 +73,7 @@ def predict(text, model_id, tokenizer_id):
 
     probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
     output_pred_minor = {f"[{CAP_MIN_NUM_DICT[i]}] {CAP_MIN_LABEL_NAMES[CAP_MIN_NUM_DICT[i]]}": probs[i] for i in np.argsort(probs)[::-1]}
-    output_pred_major = convert_minor_to_major(np.argsort(probs)[::-1])
+    output_pred_major = convert_minor_to_major(np.argsort(probs)[::-1], probs)
     output_info = f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.</p>'
     return output_pred_minor, output_pred_major, output_info