try

app.py

@@ -1,16 +1,16 @@
-import numpy as np
 import gradio as gr
+from fastai.vision.all import *
 
-def sepia(input_img):
-    sepia_filter = np.array([
-        [0.393, 0.769, 0.189], 
-        [0.349, 0.686, 0.168], 
-        [0.272, 0.534, 0.131]
-    ])
-    sepia_img = input_img.dot(sepia_filter.T)
-    sepia_img /= sepia_img.max()
-    return sepia_img
+learn = load_learner('export.pkl')
+labels = learn.dls.vocab
 
-demo = gr.Interface(sepia, gr.Image(), "image")
-if __name__ == "__main__":
-    demo.launch()
+def classify_image(img):
+    img = PILImage.create(img)
+    pred, idx, probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+
+examples = ['grizzly.jpg']
+enable_queue=True
+
+gr.Interface(fn=classify_image,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(),enable_queue=enable_queue).launch()