put custom ordinalRegressionMetric function in space

app.py

@@ -1,3 +1,43 @@
 import gradio as gr
 
+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
+
 gr.load("models/beelzeebuub/FJModel").launch()