Create app.py

app.py

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+from fastai.learner import *
+from fastai.vision.all import *
+import gradio as gr
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+learn = load_learner("export.pkl")
+labels = learn.dls.vocab
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+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 = "Garbage Classifier  [Squeeze Net]"
+description = " Created as a demo for Gradio and HuggingFace Spaces."
+article="<p style='text-align: center'><a href='https://recycleye.com/wastenet/' target='_blank'>Link to ISIC Dataset</a></p>"
+interpretation='default'
+enable_queue=True
+examples = examples=['img1.jpg','img2.jpg','img3.jpg','img4.jpg']
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+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
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+# import gradio as gr
+# from fastai.vision.all import *
+# import skimage
+# #Importing necessary libraries
+# import gradio as gr
+# #import scikit-learn as sklearn
+# from fastai.vision.all import *
+# from sklearn.metrics import roc_auc_score
+
+# 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))}
+
+
+# examples = ['img1.jpg','img2.jpg','img3.jpg']
+
+# #Launching the gradio application
+# gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),
+#              outputs=gr.outputs.Label(num_top_classes=1),
+#              title=title,
+#              description=description,article=article,
+#              examples=examples,
+#              enable_queue=enable_queue).launch(inline=False)
+
+# #gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(224, 224)),outputs=gr.outputs.Label(num_top_classes=3),title=title,description=description,article=article,examples=examples,interpretation=interpretation,enable_queue=enable_queue).launch()
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