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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 = ["llama.jpg"]
+repo_id = "osanseviero/is_it_a_llama"
+path = Path("./")
+
+def get_y(r):
+    return r["label"]
+    
+def get_x(r):
+    return path/r["fname"]
+    
+learner = from_pretrained_fastai(repo_id)
+labels = learner.dls.vocab
+
+def inference(image):
+    label_predict,_,probs = learner.predict(image)
+    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
+    return labels_probs
+
+gr.Interface(
+    fn=inference,
+    title="Llama image classification",
+    description = "Predict if this image has a llama (or a forest)",
+    inputs="image",
+    outputs="label",
+    examples=examples,
+).launch(debug=True, enable_queue=True)