predictor update

app.py

@@ -1,7 +1,27 @@
 import gradio as gr
+from fastai.vision.all import *
+import scikit-image
 
 def greet(name):
     return "Hello " + name + "!!"
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+iface.launch()
+
+pet_learner = load_learner('pets.pkl')
+im = PILImage.create('corgi.jpg')
+im.thumbnail((192,192))
+
+labels = pet_learner.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = pet_learner.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."
+article = "<p style = 'text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
+examples = ['corgi.jpeg']
+interpretation = 'default'
+enable_queue=True
+
+gr.Interface(fn=predict,inpute = 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()

requirements.txt

@@ -0,0 +1,2 @@
+fastai
+scikit-image