task conversion

app.py

@@ -1,9 +1,29 @@
 import gradio as gr
 import subprocess
+import os
+import numpy as np
 
-subprocess.run(["python", "nnunet/dataset_conversion/Task500_Glacier_inference.py"])
-def greet(name):
-    return "Hello " + name + "!!"
+os.environ['data_raw'] = 'data_raw/'
+os.environ['nnUNet_raw_data_base'] = 'nnUNet_raw_data_base/'
+os.environ['nnUNet_preprocessed'] = 'nnUNet_preprocessed/'
+os.environ['RESULTS_FOLDER'] = 'calvingfronts/'
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+
+
+def run_front_detection(input_img):
+    input_img.save('data_raw/test.png')
+    subprocess.run(
+        ['python3', 'nnunet/dataset_conversion/Task500_Glacier_inference.py', '-data_percentage', '100', '-base',
+         os.environ['data_raw']])
+    cmd = [
+        'python3', 'nnunet/inference/predict_simple.py',
+        '-i', os.path.join('$nnUNet_raw_data_base', 'nnUNet_raw_data/Task500_Glacier_zonefronts/imagesTs/'),
+        '-o', os.path.join('$RESULTS_FOLDER', 'fold_0'),
+        '-t', '500','-m','2d','-f','0','-p', 'nnUNetPlansv2.1', '-tr','nnUNetTrainerV2', '-model_folder_name',
+        '$model'
+    ]
+    #subprocess.run(cmd)
+    #TODO remove files
+
+demo = gr.Interface(run_front_detection, gr.Image(type='pil'), "image")
+demo.launch()