Update Model/MultimodelNER/VLSP2016/MNER_2016.py

Model/MultimodelNER/VLSP2016/MNER_2016.py

@@ -23,7 +23,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
 net = getattr(resnet, 'resnet152')()
-net.load_state_dict(torch.load(os.path.join('/Model/Resnet/', 'resnet152.pth')))
+net.load_state_dict(torch.load(os.path.join('Model/Resnet/', 'resnet152.pth')))
 encoder = myResnet(net, True, device)
 def process_text(text):
     # Loại bỏ dấu cách thừa và dấu cách ở đầu và cuối văn bản
@@ -37,8 +37,8 @@ def show_mner_2016():
     multimodal_text = process_text(multimodal_text)  # Xử lý văn bản
     image = st.file_uploader("Upload an image (only jpg):", type=["jpg"])
     if st.button("Process Multimodal NER"):
-            save_image = '/Model/MultimodelNER/VLSP2016/Image'
-            save_txt = '/Model/MultimodelNER/VLSP2016/Filetxt/test.txt'
+            save_image = 'Model/MultimodelNER/VLSP2016/Image'
+            save_txt = 'Model/MultimodelNER/VLSP2016/Filetxt/test.txt'
             image_name = image.name
             save_uploaded_image(image, save_image)
             convert_text_to_txt(multimodal_text, save_txt)
@@ -46,7 +46,7 @@ def show_mner_2016():
             st.image(image, caption="Uploaded Image", use_column_width=True)
 
             bert_model='vinai/phobert-base-v2'
-            output_dir='/Model/MultimodelNER/VLSP2016/best_model'
+            output_dir='Model/MultimodelNER/VLSP2016/best_model'
             output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
             output_encoder_file = os.path.join(output_dir, "pytorch_encoder.bin")
             processor = MNERProcessor_2016()
@@ -70,7 +70,7 @@ def show_mner_2016():
             trans_matrix[6, 12] = 1
             tokenizer = AutoTokenizer.from_pretrained(bert_model, do_lower_case=False)
             model_umt, encoder_umt = load_model(output_model_file, output_encoder_file, encoder,num_labels,auxnum_labels)
-            eval_examples = get_test_examples_predict('/Model/MultimodelNER/VLSP2016/Filetxt/')
+            eval_examples = get_test_examples_predict('Model/MultimodelNER/VLSP2016/Filetxt/')
 
             y_pred, a = predict(model_umt, encoder_umt, eval_examples, tokenizer, device,save_image,trans_matrix)
             formatted_output = format_predictions(a, y_pred[0])