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import gradio as gr |
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from transformers import pipeline |
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classifier = pipeline("zero-shot-classification", model="NbAiLab/nb-bert-base-mnli") |
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def sequence_to_classify(sequence, labels): |
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hypothesis_template = 'Dette eksempelet er {}.' |
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label_clean = str(labels).split(",") |
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response = classifier(sequence, label_clean, hypothesis_template=hypothesis_template, multi_class=True) |
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predicted_labels = response['labels'] |
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predicted_scores = response['scores'] |
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clean_output = {idx: float(predicted_scores.pop(0)) for idx in predicted_labels} |
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print("response is:{}".format(response)) |
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print("clean_output: {}".format(clean_output)) |
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print(predicted_labels) |
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print(predicted_scores) |
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return clean_output |
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example_text=["Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september.","Kutt smør i terninger, og la det temperere seg litt mens deigen elter. Ha hvetemel, sukker, gjær, salt og kardemomme i en bakebolle til kjøkkenmaskin. Bruker du fersk gjær kan du smuldre gjæren i bollen, eller røre den ut i melken. Alt vil ettehvert blande seg godt, så begge deler er like bra."] |
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example_labels=["politikk,helse,sport,religion", "helse,sport,religion, mat"] |
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def greet(name): |
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return "Hello " + name + "!!" |
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iface = gr.Interface( |
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title = "Zero-shot Classification of Norwegian Text", |
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description = "Demo of zero-shot classification using NB-Bert base model (Norwegian).", |
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fn=sequence_to_classify, |
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inputs=[gr.inputs.Textbox(lines=2, |
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label="Write a norwegian text you would like to classify...", |
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placeholder="Text here..."), |
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gr.inputs.Textbox(lines=10, |
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label="Possible candidate labels", |
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placeholder="labels here...")], |
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outputs=gr.outputs.Label(num_top_classes=3), |
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capture_session=True, |
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interpretation="default" |
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,examples=[ |
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[example_text, example_labels] |
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]) |
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iface.launch() |
