Update app.py

app.py

@@ -74,10 +74,10 @@ def predict_icd(text_input, model_name, label_count):
     labels = []
     values = []
     if label_count == "50":
-        labels  = ['38.9', '244.9', '250', '272', '272.4', '276.1', '276.2', '285.1', '285.9', '287.5', '305.1', '311', '33.24', '36.15', '37.22', '38.91', '38.93', '39.61', '39.95', '401.9', '403.9', '410.71', '412', '414.01', '424', '427.31', '428', '45.13', '486', '496', '507', '511.9', '518.81', '530.81', '584.9', '585.9', '599', '88.56', '88.72', '93.9', '96.04', '96.6', '96.71', '96.72', '99.04', '99.15', '995.92', 'V15.82', 'V45.81', 'V58.61']
+        labels  = ['38.9', '244.9', '250.00', '272.0', '272.4', '276.1', '276.2', '285.1', '285.9', '287.5', '305.1', '311', '33.24', '36.15', '37.22', '38.91', '38.93', '39.61', '39.95', '401.9', '403.90', '410.71', '412', '414.01', '424.0', '427.31', '428.0', '45.13', '486', '496', '507.0', '511.9', '518.81', '530.81', '584.9', '585.9', '599.0', '88.56', '88.72', '93.9', '96.04', '96.6', '96.71', '96.72', '99.04', '99.15', '995.92', 'V15.82', 'V45.81', 'V58.61']
         values = [0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0]
     if label_count == "5":
-        labels = ['38.93', '401.9', '414.01', '427.31', '428']
+        labels = ['38.93', '401.90', '414.01', '427.31', '428.00']
         values = [1, 0, 0, 0, 0]
     label_map = {i: label for i, label in enumerate(labels)}
     data_dict = {label: [] for label in labels}
@@ -162,10 +162,8 @@ def predict_icd(text_input, model_name, label_count):
     y_pred = "\n".join(y_pred)
     label_dict_df = pd.read_csv("data/mimic3/"+label_count+"/labels_dictionary_"+label_count+"_level_1.csv")
 
-    label_dict_df['icd9_code'] = label_dict_df['icd9_code'].astype(float)
     label_dict = label_dict_df.set_index('icd9_code')['long_title'].to_dict()
-    predicted_labels_float = [float(label) for label in predicted_labels]
-    predicted_labels_with_titles = [(label, label_dict.get(str(label), "Not Found")) for label in predicted_labels_float]
+    predicted_labels_with_titles = [(label, label_dict.get(str(label), "Not Found")) for label in predicted_labels]
 
     html_output ="<h2>ICD Codes</h2>"
     # for label, title in predicted_labels_with_titles: