Update app.py

app.py

@@ -96,7 +96,7 @@ def predict_icd(text_input, model_name, label_count):
     
     model_path = "meghanaraok/"+model_name+"_"+label_count+"/"
     #change
-    label_dict = pd.read_csv("data/mimic3"+label_count+"/labels_dictionary_"+label_count+"_level_1.csv")
+    label_dict = pd.read_csv("../data/mimic3"+label_count+"/labels_dictionary_"+label_count+"_level_1.csv")
     num_labels = label_dict.shape[0]
     
     if model_name != "ClinicalLongformer":
@@ -104,17 +104,13 @@ def predict_icd(text_input, model_name, label_count):
         parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
         model_args, data_args, training_args = parser.parse_json_file(json_file=config_json)
         coding_model_config, model_class = get_coding_model_config(model_name, model_args, data_args, label_dict, label_dict.shape[0])
-        #2
         kwargs = {
             "coding_model_config": coding_model_config,
             "args": training_args
         }
-        #3
         model = model_class.from_pretrained(model_path, **kwargs)
         model.to(torch.device("cuda:0"))
-        #4
         tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name,  padding_side="right")
-        #4
         results = segment_tokenize_dataset(tokenizer, text, labels,
                                            data_args.max_seq_length,
                                            model_args.num_chunks_per_document)