app.py

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()