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c462d23
1
Parent(s):
173c796
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Browse files
app.py
CHANGED
@@ -1,43 +1,3 @@
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import gradio as gr
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class OrdinalRegressionMetric(Metric):
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def __init__(self):
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super().__init__()
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self.total = 0
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self.count = 0
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def accumulate(self, learn):
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# Get predictions and targets
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preds, targs = learn.pred, learn.y
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# Your custom logic to convert predictions and targets to numeric values
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preds_numeric = torch.argmax(preds, dim=1)
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targs_numeric = targs
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#print("preds_numeric: ",preds_numeric)
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#print("targs_numeric: ",targs_numeric)
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# Calculate the metric (modify this based on your specific needs)
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squared_diff = torch.sum(torch.sqrt((preds_numeric - targs_numeric)**2))
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# Normalize by the maximum possible difference
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max_diff = torch.sqrt((torch.max(targs_numeric) - torch.min(targs_numeric))**2)
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#print("squared_diff: ",squared_diff)
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#print("max_diff: ",max_diff)
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# Update the metric value
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self.total += squared_diff
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#print("self.total: ",self.total)
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self.count += max_diff
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#print("self.count: ",self.count)
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@property
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def value(self):
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if self.count == 0:
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return 0.0 # or handle this case appropriately
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#print("self.total / self.count: ", (self.total / self.count))
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# Calculate the normalized metric value
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metric_value = 1/(self.total / self.count)
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return metric_value
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gr.load("models/beelzeebuub/FJModel").launch()
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import gradio as gr
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gr.load("models/beelzeebuub/FJModel").launch()
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