init

app.py

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+import gradio as gr
+from fastai.vision.all import *
+
+def is_cat(x): return x[0].isupper()
+
+learn = load_learner('model.pkl')
+
+categories = ('kissing', 'not_kissing')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+#examples = ['cat.jpeg', 'dog.jpeg', 'catdog.jpeg']
+
+iface = gr.Interface(fn=classify_image, inputs=image, outputs=label) # examples=examples)
+iface.launch()

environment.yml

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+name: minimal
+channels:
+  - defaults
+dependencies:
+  - ipywidgets
+  - pip:
+    - gradio
+    - fastai
+prefix: ~/miniconda3/envs/minimal

requirements.txt

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+fastai
+torch
+gradio
+numpy
+pandas