Update app/app_savta.py

app/app_savta.py

@@ -60,10 +60,15 @@ data = create_data(Path('src/data/processed'))
 learner = unet_learner(data,resnet34, metrics=rmse, wd=1e-2, n_out=3, loss_func=MSELossFlat(), path='src/')
 learner.load('model')
 
+def gen(input_img):
+   return PILImageBW.create((learner.predict(input_img))[0]).convert('L')
+
 ################### Gradio Web APP ################################
 
 title = "SavtaDepth WebApp"
+
 description = "Savta Depth is a collaborative Open Source Data Science project for monocular depth estimation - Turn 2d photos into 3d photos. To test the model and code please check out the link bellow."
+
 article = "<p style='text-align: center'><a href='https://dagshub.com/OperationSavta/SavtaDepth' target='_blank'>SavtaDepth Project from OperationSavta</a></p><p style='text-align: center'><a href='https://colab.research.google.com/drive/1XU4DgQ217_hUMU1dllppeQNw3pTRlHy1?usp=sharing' target='_blank'>Google Colab Demo</a></p></center></p>"
 
 examples = [
@@ -73,10 +78,6 @@ examples = [
 favicon = "examples/favicon.ico"
 thumbnail = "examples/SavtaDepth.png"
 
-def gen(input_img):
-   
-    return PILImageBW.create((learner.predict(input_img))[0]).convert('L')
-
 
 def main():
 	iface = gr.Interface(
@@ -91,7 +92,6 @@ def main():
 	                     article = article, 
 	                     examples = examples, 
 	                     theme ="peach",
-	                     thumbnail=thumbnail, 
 	                     allow_screenshot=True
 	                     )