Upload app.py

Initial commit of app

app.py

@@ -0,0 +1,47 @@
+#install huggingface_hub["fastai"] gradio timm
+from huggingface_hub import from_pretrained_fastai
+from gradio import Interface, inputs, outputs
+from fastai.learner import Learner
+import fastai
+
+repo_id = "Kieranm/britishmus_plate_material_classifier"
+
+learner = from_pretrained_fastai(repo_id)
+
+mappings = {
+    fastai.torch_core.TensorImage: {
+        "type": inputs.IMage(type='file', label='input'),
+        "process": lambda inp : inp.name
+    },
+    fastai.torch_core.TensorCategory: {
+        "type": outputs.Label(num_top_classes=3, label = 'output'),
+        "process": lambda dls, out: {dls.vocab[i]: float(out[2][i]) for i in range(len(dls.vocab))}
+
+    }
+}
+
+#Taken from fastgradio library
+
+class Demo:
+    def __init__(self, learner):
+
+        self.learner = learner
+        self.types = getattr(self.learner.dls, '_types')[tuple]
+
+    def learner_predict(self, inp):
+        inp = mappings[self.types[0]]["process"](inp)
+        prediction = self.learner.predict(inp)
+        output = mappings[self.types[1]]["process"](self.learner.dls, prediction)
+        return output
+
+    def launch(self, share=True, debug=False, auth=None, **kwargs):
+        inputs = mappings[self.types[0]]["type"]
+
+        outputs = mappings[self.types[1]]["type"]
+
+        Interface(fn=self.learner_predict, inputs=inputs, outputs=outputs, 
+                  examples = ["examples/earthen1.jpg", "examples/earthen2.png", "porcelain1.png", "porcelain2.png"],
+                   **kwargs).launch(share=share, debug=debug, auth=auth)
+
+
+Demo(learner).launch()