Update description in app.py and try examples again

app.py

@@ -21,11 +21,6 @@ description = """
 ## Brain Tumor Segmentation 🧠
 A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data.
 
-The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the "enhancing tumor" (ET), the "tumor core" (TC), the "whole tumor" (WT) based on 4 aligned input MRI scans (T1c, T1, T2, FLAIR).
-- The ET is described by areas that show hyper intensity in T1c when compared to T1, but also when compared to "healthy" white matter in T1c.
-- The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor.
--  The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR.
-
 ## To run 🚀
 
 Upload a image file in the format: 4 channel MRI (4 aligned MRIs T1c, T1, T2, FLAIR at 1x1x1 mm)
@@ -36,9 +31,9 @@ This is an example, not to be used for diagnostic purposes.
 
 ## References 👀
 
-[1] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.
-[2] Menze BH, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694
-[3] Bakas S, et al. "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI:10.1038/sdata.2017.117
+1. Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.
+2. Menze BH, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694
+3. Bakas S, et al. "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI:10.1038/sdata.2017.117
 """
 
 #examples = 'examples/'
@@ -94,19 +89,19 @@ def predict(input_file, z_axis, model=model, device=device):
     #pred_et_image = pred_image[0, 1, :, :, z_axis]
     #pred_wt_image = pred_image[0, 2, :, :, z_axis]
     
-    return input_t1c_image, pred_tc_image, z_axis
+    return input_t1c_image, pred_tc_image, 
 
 
 iface = gr.Interface(
     fn=predict,
     inputs=[
-        gr.File(label='Nifti file'),
+        gr.File(label='Input file'),
         gr.Slider(0, 200, label='z-axis', value=100)
     ], 
     outputs=[
-        gr.Image(label='input image'),
-        gr.Image(label='segmentation'),
-        gr.Slider(0, 200, label='z-axis', value=100)],
+        gr.Image(label='T1C image'),
+        gr.Image(label='Segmentation'),
+    ],
     title=title,
     description=description,
     #examples=examples,

examples/log.csv

@@ -0,0 +1,2 @@
+input_file
+BRATS_485.nii.gz