pet predictor

app.py

@@ -1,7 +1,19 @@
 import gradio as gr
+from fastai.vision.all import *
+import skimage
 
-def greet(name):
-    return "Hello " + name + "!!"
+learn = load_learner('model.pkl')
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+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."
+examples = ['siamese.jpg', 'pomeranian.jpg', 'lion.jpg']
+enable_queue=True
+
+iface = gr.Interface(fn=predict,inputs=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)
 iface.launch()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:533e0c7829751d14e0f347f8416612d288c8b2f55260940c896a0765acf6d75a
+size 47061483