Update app.py

app.py

@@ -1,17 +1,27 @@
 
-from gradio.components import Image, Label
+__all__ = ['learner', 'labels', 'interface', 'classify']
+
+from fastai.vision.all import *
 import gradio as gr
 
-def predict(img):
-    # Replace this with your actual prediction logic
-    return "Your image is: " + img.filename
+learner = load_learner('ConvNext_RmsProps.pkl')
+
+labels = learner.dls.vocab
+
+
+def classify(img):
+    img = PILImage.create(img)
+    pred, pred_idx, probs = learner.predict(img)
+    return {labels[i]: float(probs[i]) for i in range (len(labels))}
 
-title = "Minimal Gradio Example"
-description = "This example demonstrates using Gradio with an image input and label output."
 
-gr.Interface(fn=predict,
-             inputs=Image(),  # No shape specified, Gradio will handle different sizes
-             outputs=Label(),
-             title=title,
-             description=description
-             ).launch()
+interface = gr.Interface(
+    fn=classify,
+    inputs=gr.Image(),
+    outputs=gr.Label(),
+    examples=['Amanita.jpg'],
+    allow_flagging='never',
+    title='Bear classifier',
+    description='Model trained to classify four types of bears — black, giant panda, red panda and polar.'
+)
+interface.launch(share=False, inline=False)