remove old code

app.py

@@ -1,10 +1,10 @@
-import gradio as gr
+# import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# def greet(name):
+#     return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
 
 import fastbook
 from fastbook import *
@@ -13,8 +13,31 @@ import skimage
 from skimage import io as skio
 import numpy
 from PIL import Image, ImageEnhance
+import torchvision.transforms as T
+
 def name_to_hrs (r): return float(round(float(os.path.basename(r)[0:-4].split("_")[1][1:])*(minutes/60)+5,2))
 def validation_split (r):  return os.path.basename(r)[0:-4].split("_")[3] == "R0003" or os.path.basename(r)[0:-4].split("_")[3] == "R0006"
 def get_label_filename(name): return path/'labels'/f'{name.stem}_annotationLabels.tif'
-zebrafish_age_predictor = load_learner(path/'FishAge.pkl')
-zebrafish_classifier = load_learner(path/'FishSegmentation.pkl')
+zebrafish_age_predictor = load_learner('FishAge.pkl')
+zebrafish_classifier = load_learner('FishSegmentation.pkl')
+examples = ["5hr.tif", "12hr.tif", "24hr.tif"]
+def process_zebrafish_image(img):
+
+    # out_pl.clear_output()
+    # enhancer = ImageEnhance.Brightness(img)
+    # factor = 10 
+    # im_output = enhancer.enhance(factor)
+    # with out_pl: display(im_output.to_thumb(256,256))
+    age,tensor, tensor=zebrafish_age_predictor.predict(img)
+    
+    pred,pred_idx,probs=zebrafish_classifier.predict(img)
+
+    # with out_pl_mask: pred.show(alpha=1, vmin=0, vmax=3, title='mask')
+    # lbl_pred.value = f'Predicted age: {age[0]};'
+    #return dict(zip(pred, map(float,age)))
+    return dict(zip(T.ToPILImage(pred), map(float,age)))
+
+import gradio as gr
+
+intf = gr.Interface(fn=process_zebrafish_image, inputs=gr.inputs.Image(shape=(512, 512)), outputs=[gr.outputs.Image(), gr.outputs.Label()]).launch(share=True)
+intf.launch(inline=False)