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| """Application file.""" | |
| from pathlib import Path | |
| import evaluate | |
| import gradio as gr | |
| module = evaluate.load("Natooz/levenshtein") | |
| # Code taken and adapted from: https://github.com/huggingface/evaluate/blob/main/src/evaluate/utils/gradio.py | |
| local_path = Path(__file__).parent | |
| # if there are several input types, use first as default. | |
| if isinstance(module.features, list): | |
| (feature_names, feature_types) = zip(*module.features[0].items()) | |
| else: | |
| (feature_names, feature_types) = zip(*module.features.items()) | |
| gradio_input_types = evaluate.utils.infer_gradio_input_types(feature_types) | |
| def compute(data): | |
| return module.compute(**evaluate.utils.parse_gradio_data(data, gradio_input_types)) | |
| gradio_app = gr.Interface( | |
| fn=compute, | |
| inputs=gr.Dataframe( | |
| headers=feature_names, | |
| col_count=len(feature_names), | |
| row_count=1, | |
| datatype=evaluate.utils.json_to_string_type(gradio_input_types), | |
| ), | |
| outputs=gr.Textbox(label=module.name), | |
| description=module.info.description, | |
| title=f"Metric: {module.name}", | |
| article=evaluate.utils.parse_readme(local_path / "README.md"), | |
| ) | |
| if __name__ == "__main__": | |
| gradio_app.launch() | |