turning on dogs vs cats classifier

app.py

@@ -1,7 +1,36 @@
+# import gradio as gr
+
+# def greet(name):
+#     return "Hello " + name + "!!"
+
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
+
+
+
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+def is_cat(x): 
+    return x[0].isupper() 
+
+def main():
+    learn = load_learner('02_trained_model_cats_vs_dogs.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>"
+    interpretation='default'
+    enable_queue=True
+    # gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=2)).launch(share=False)
+    gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=2),title=title,description=description,article=article,examples=None,interpretation=interpretation,enable_queue=enable_queue).launch(share=True)
+
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+if __name__ == '__main__':
+    main()