Update app.py not just test file

app.py

@@ -1,37 +1,47 @@
 from fastai.vision.all import *
-
+import numpy
 import gradio as gr
-
+import PIL
 from PIL import Image, ImageEnhance
 import torchvision.transforms as T
-import pathlib
-plt = platform.system()
-print(str(plt))
-if plt == 'linux': pathlib.WindowsPath = pathlib.PosixPath
+
+# import pathlib
+# plt = platform.system()
+# if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
 
 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= vision_learner().load('zebrafish_age_20220726.pkl')
+#zebrafish_classifier = unet_learner().load('fish_yolk_segmentation_20220726.pkl')
+
 zebrafish_age_predictor = load_learner('zebrafish_age_20220726.pkl')
 zebrafish_classifier = load_learner('fish_yolk_segmentation_20220726.pkl')
-examples = ["5hr.tif", "12hr.tif", "24hr.tif"]
+
+title = "Zebrafish segmenter and age predictor"
+description = "An rgb grayscale zebrafish fluorescence image segmenter and age predictor created with fastai. Created as a demo for Gradio and HuggingFace Spaces. Gradio does not display .tif files - those will only show up in the output. The input will be blank unless the file is .jpg or .png."
+
+examples = ["early1.png","early2.png","early3.png", "late1.png", "late2.png", "late3.png", "mid1.png", "mid2.png","mid3.png"]
 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)))
-    image_out = T.ToPILImage(pred)
-    text_out = dict(zip("Age prediction", map(float,age)))
+    img = img*10
+    img = PILImage.create(img)
+    #img = PILImage.create('24hr.tif')
+    _,axs = plt.subplots(1,3, figsize=(12,3))
+    img.show(ctx=axs[0], title='image')
+    pred.show(alpha=1, ctx=axs[1], vmin=0, vmax=3, title='mask')
+    img.show(ctx=axs[2], title='superimposed')
+    pred.show(ctx=axs[2], vmin=0, vmax=3);
+    fig = plt.gcf()
+    fig.canvas.draw()
+    image_out = PIL.Image.frombytes('RGB', fig.canvas.get_width_height(),fig.canvas.tostring_rgb())
+  
+    text_out = "Age prediction "+ str(age[0])
     return (image_out, text_out )
 
-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)
+intf = gr.Interface(fn=process_zebrafish_image, inputs=gr.inputs.Image(shape=(512, 512)), outputs=['image', 'text'], title = title, description=description, examples= examples).launch(debug=True, share=True)
+