Gradio app actualized with examples, title, image recognition and description

app.py

@@ -1,7 +1,20 @@
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+learn = load_learner('sport_car_classifier.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))}
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+gr.Interface(
+    fn=predict, 
+    inputs=gr.inputs.Image(
+        shape=(512, 512)), 
+    outputs=gr.outputs.Label(num_top_classes=3),
+    title="Sport car classifier",
+    description="An app design to classifie the most relateve sportcars",
+    examples=['bmw.jpg', 'lamborghini.jpg', 'koenigsegg.jpg'],
+    interpretation='default',
+    enable_queue=True).launch(share=True)

requirements.txt

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