Update app.py

app.py

@@ -10,16 +10,15 @@ description = """
 
 <!-- Provide a quick summary of the dataset. -->
 
-M-SYNTH is a synthetic digital mammography (DM) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available [Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE)](https://github.com/DIDSR/VICTRE) toolkit.
+Neuro-Synth is a synthetic neuroimaging dataset ...
 
 ## Dataset Details
 
 The dataset has the following characteristics:
 
-* Breast density: dense, heterogeneously dense, scattered, fatty
-* Mass radius (mm): 5.00, 7.00, 9.00
-* Mass density: 1.0, 1.06, 1.1 (ratio of radiodensity of the mass to that of fibroglandular tissue)
-* Relative dose: 20%, 40%, 60%, 80%, 100% of the clinically recommended dose for each density
+* 
+* 
+* 
 
 ## Dataset Download
 
@@ -35,13 +34,13 @@ wget https://john6688-neuro-synth.hf.space/file=dataset/synth_kde_white_female.c
 citation = """
 ## Citation
 ```
-@article{sizikova2023knowledge,
-  title={Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses},
-  author={Sizikova, Elena and Saharkhiz, Niloufar and Sharma, Diksha and Lago, Miguel and Sahiner, Berkman and Delfino, Jana G. and Badano, Aldo},
-  journal={Advances in Neural Information Processing Systems},
+@article{,
+  title={},
+  author={},
+  journal={},
   volume={},
   pages={},
-  year={2023}
+  year={2024}
 }
 ```
 """
@@ -53,8 +52,7 @@ example_df = pd.read_csv(file_name)
 
 def infer(num_sample):
     num_sample = int(num_sample)
-    #input = np.load("model/kde_white_female.npz", allow_pickle=True)['model'].item()
-    input = np.load("model/kde_white_female.npz")['model'].item()
+    input = np.load("model/kde_white_female.npz", allow_pickle=True)['model'].item()
     kde, scaler, cols_names = input['model'], input['scaler'], input['columns']
     
     sample = kde.sample(num_sample, random_state=0)