Initial Gradio app

README.md

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+---
+title: Fastai_pet_classifier
+emoji: 🤢
+colorFrom: red
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: false
+---

app.py

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+import gradio as gr
+from fastai.vision.all import *
+import skimage
+
+learn = load_learner('panda-model-1')
+
+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 = "Prostate cANcer graDe Assessment model"
+description = "A model to predict the ISUP grade for prostate cancer based on whole-slide images of digitized H&E-stained biopsies."
+# article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ['test.jpg']
+interpretation='default'
+enable_queue=True
+
+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()

requirements.txt

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+fastai
+scikit-image
+imageio
+pandas
+imagecodecs
+tifffile