add model and gradio app

app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ModelTester.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'examples', 'intf', 'OrdinalRegressionMetric', 'classify_image']
+
+# %% ModelTester.ipynb 1
+from fastai.vision.all import *
+import gradio as gr
+
+# %% 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
+learn = load_learner("textfile3-2.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)

textfile3-2.pk1

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c86bf454f4bcf7dd0fba8c4628ac89872ce3431fb4d2997ccbdbab5ba70c3dbb
+size 46998504