a

app.py

@@ -1,19 +1,21 @@
 import gradio as gr
-from fastai.vision.widgets import *
+# from fastai.vision.widgets import *
 from fastai.vision.all import *
-import skimage
+# import skimage
 
 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 = "Pet Breed Classifier"
-# description = "A pet breed classifier trained on the Oxford Pets dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
-# article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+
+# Cell
+categories = ('Tank', 'No-Tank')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+# Cell
+image = gr.inputs.Image(shape=(256, 256))
+label = gr.outputs.Label()
 examples = ['tank_1.jpg', 'tank_2.jfif', 'photos.jfif']
-# interpretation='default'
-# enable_queue=True
 
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(256, 256)), outputs=gr.outputs.Label(), example=examples).launch(share=True)
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

requirements.txt

@@ -1,4 +1,3 @@
 fastai==2.7.5
 fastcore==1.4.5
-scikit-image
 ipywidgets