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from fastai.vision.all import *
import gradio as gr
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
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('zebrafish_age_20220726.pkl')
zebrafish_classifier = load_learner('fish_yolk_segmentation_20220726.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)))
image_out = T.ToPILImage(pred)
text_out = dict(zip("Age prediction", map(float,age)))
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)
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