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app.py

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+import gradio as gr
+import torch
+from huggingface_hub import from_pretrained_fastai
+from pathlib import Path
+
+examples = ["./examples/image_1.png", 
+            "./examples/image_2.png", 
+            "./examples/image_3.png", 
+            "./examples/image_4.png", 
+            "./examples/image_5.png"]
+            
+repo_id = "hugginglearners/rice_image_classification"
+path = Path("./")
+
+def get_y(r):
+    return r["label"]
+    
+def get_x(r):
+    return path/r["fname"]
+    
+learner = from_pretrained_fastai(repo_id)
+
+def inference(image):
+    label_predict,_,probs = learner.predict(image)
+    return f"This rice image is {label_predict} with {100*probs[torch.argmax(probs)].item():.2f}% probability"
+
+gr.Interface(
+    fn=inference,
+    title="Rice image classification",
+    description = "Predict which type of rice belong to Arborio, Basmati, Ipsala, Jasmine, Karacadag",
+    inputs="image",
+    examples=examples,
+    outputs=gr.Textbox(label='Prediction'),
+    cache_examples=False,
+    article = "Author: <a href=\"https://www.linkedin.com/in/vumichien/\">Vu Minh Chien</a>",
+).launch(debug=True, enable_queue=True)