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app.py

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+import birder
+import numpy as np
+from birder.inference.classification import infer_image
+from huggingface_hub import HfApi
+
+import gradio as gr
+
+
+def get_birder_classification_models():
+    api = HfApi()
+    models = api.list_models(author="birder-project", tags="image-classification")
+    return [model.modelId.split("/")[-1] for model in models]
+
+
+def load_model_and_predict(image, model_name):
+    try:
+        (net, class_to_idx, signature, rgb_stats) = birder.load_pretrained_model(model_name, inference=True)
+        size = birder.get_size_from_signature(signature)
+        transform = birder.classification_transform(size, rgb_stats)
+        (out, _) = infer_image(net, image, transform)
+
+        idx_to_class = {v: k for k, v in class_to_idx.items()}
+        topk_idx = np.argsort(out[0])[-3:][::-1]
+        predictions = [(idx_to_class[idx], float(out[0][idx])) for idx in topk_idx]
+
+        return predictions
+    except Exception as e:
+        return [(f"Error: {str(e)}", 0.0)]
+
+
+def predict(image, model_name):
+    predictions = load_model_and_predict(image, model_name)
+    return {f"{class_name} ({conf:.2%})": conf for class_name, conf in predictions}
+
+
+def create_interface():
+    models = get_birder_classification_models()
+
+    example_images = [
+        "Common myna.jpeg",
+        "Eurasian hoopoe.jpeg",
+        "Grey heron.jpeg",
+    ]
+
+    # Create interface
+    iface = gr.Interface(
+        analytics_enabled=False,
+        fn=predict,
+        inputs=[
+            gr.Image(type="pil", label="Input Image"),
+            gr.Dropdown(
+                choices=models,
+                label="Select Model",
+                value=models[0] if models else None,
+            ),
+        ],
+        outputs=gr.Label(num_top_classes=3),
+        examples=[[path] for path in example_images],
+        title="Birder Image Classification",
+        description="Select a model and upload an image or use one of the examples to get bird species predictions.",
+    )
+
+    return iface
+
+
+# Launch the app
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()

requirements.txt

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+birder
+huggingface_hub