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# %% ModelTester.ipynb 1
from fastai.vision.all import *
import gradio as gr
# needed when model was saved/ported on windows
import platform
import pathlib
# %% ModelTester.ipynb 3
from fastai.metrics import Metric
class OrdinalRegressionMetric(Metric):
def __init__(self):
super().__init__()
self.total = 0
self.count = 0
def accumulate(self, learn):
# Get predictions and targets
preds, targs = learn.pred, learn.y
# Your custom logic to convert predictions and targets to numeric values
preds_numeric = torch.argmax(preds, dim=1)
targs_numeric = targs
#print("preds_numeric: ",preds_numeric)
#print("targs_numeric: ",targs_numeric)
# Calculate the metric (modify this based on your specific needs)
squared_diff = torch.sum(torch.sqrt((preds_numeric - targs_numeric)**2))
# Normalize by the maximum possible difference
max_diff = torch.sqrt((torch.max(targs_numeric) - torch.min(targs_numeric))**2)
#print("squared_diff: ",squared_diff)
#print("max_diff: ",max_diff)
# Update the metric value
self.total += squared_diff
#print("self.total: ",self.total)
self.count += max_diff
#print("self.count: ",self.count)
@property
def value(self):
if self.count == 0:
return 0.0 # or handle this case appropriately
#print("self.total / self.count: ", (self.total / self.count))
# Calculate the normalized metric value
metric_value = 1/(self.total / self.count)
return metric_value
# %% ModelTester.ipynb 4
plt = platform.system()
if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
learn = load_learner("newmodel.pk1")
# %% ModelTester.ipynb 6
categories = ("1","1-2","2","2-3","3","3-4","4","4-5","5")
def classify_image(img):
pred, idx, probs = learn.predict(img)
return dict(zip(categories, map(float, probs)))
# %% ModelTester.ipynb 8
intf = gr.Interface(fn=classify_image, inputs='image', outputs='label')
intf.launch(inline=False)
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