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import fasttext |
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from huggingface_hub import hf_hub_download |
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import gradio as gr |
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import numpy as np |
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model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin") |
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model = fasttext.load_model(model_path) |
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def predict(text, top): |
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labels, probabilities = model.predict(text, k=top) |
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cleaned_labels = [label.replace('__label__', '') for label in labels] |
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result = dict(zip(cleaned_labels, np.array(probabilities))) |
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return result |
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demo = gr.Interface( |
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fn=predict, |
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inputs=[ |
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gr.Textbox(lines=1, placeholder="Text", label="Content"), |
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gr.Number(value=5, info='number of predictions that should be returned', minimum=1, maximum=100, label="Top"), |
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], |
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title="Language Identification Demo", |
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flagging_mode="never", |
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outputs=gr.Label(label="Result")) |
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demo.launch(share=True, show_api=True) |
