export.pkl -> model.pkl

app.py

@@ -1,37 +1,26 @@
 import gradio as gr
-import os
 from fastai.vision.core import PILImage
 from fastai.learner import load_learner
-import pathlib
 
-value = pathlib.Path().resolve()
-value2 = os.listdir()
 
+learn = load_learner('model.pkl')
+labels = learn.dls.vocab
 
-def greet(name):
-    return "Hello " + name + "!"
 
+def predict(img):
+    img = PILImage.create(img)
+    pred, pred_idx, probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
 
-demo = gr.Interface(fn=greet, inputs=gr.Textbox(lines=2, placeholder=str(value2)), outputs="text")
-demo.launch()
-# learn = load_learner('export.pkl')
-# labels = learn.dls.vocab
-#
-#
-# def predict(img):
-#     img = PILImage.create(img)
-#     pred, pred_idx, probs = learn.predict(img)
-#     return {labels[i]: float(probs[i]) for i in range(len(labels))}
-#
-#
-# title = "Bear Classifier"
-# description = ("A bear classifier trained on black/grizzly/teddy bear images downloaded from internet with fastai. "
-#                "Created as a demo for Gradio and HuggingFace Spaces.")
-# examples = ['black.jpg', 'grizzly.jpg', 'teddy.jpg']
-# grif = gr.Interface(fn=predict,
-#                     inputs=gr.inputs.Image(shape=(512, 512)),
-#                     outputs=gr.outputs.Label(num_top_classes=3),
-#                     title=title,
-#                     description=description,
-#                     examples=examples)
-# grif.launch(share=True)
+
+title = "Bear Classifier"
+description = ("A bear classifier trained on black/grizzly/teddy bear images downloaded from internet with fastai. "
+               "Created as a demo for Gradio and HuggingFace Spaces.")
+examples = ['black.jpg', 'grizzly.jpg', 'teddy.jpg']
+grif = gr.Interface(fn=predict,
+                    inputs=gr.inputs.Image(shape=(512, 512)),
+                    outputs=gr.outputs.Label(num_top_classes=3),
+                    title=title,
+                    description=description,
+                    examples=examples)
+grif.launch(share=True)