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| import datetime | |
| import gradio as gr | |
| from langdetect import detect, DetectorFactory, detect_langs | |
| from transformers import pipeline | |
| models = {'en': 'Narsil/deberta-large-mnli-zero-cls', # English | |
| 'de': 'Sahajtomar/German_Zeroshot', # German | |
| 'es': 'Recognai/zeroshot_selectra_medium', # Spanish | |
| 'it': 'joeddav/xlm-roberta-large-xnli', # Italian | |
| 'ru': 'DeepPavlov/xlm-roberta-large-en-ru-mnli', # Russian | |
| 'no': 'NbAiLab/nb-bert-base-mnli'} # Norsk | |
| hypothesis_templates = {'en': 'This example is {}.', # English | |
| 'de': 'Dieses beispiel ist {}.', # German | |
| 'es': 'Este ejemplo es {}.', # Spanish | |
| 'it': 'Questo esempio è {}.', # Italian | |
| 'ru': 'Этот пример {}.', # Russian | |
| 'no': 'Dette eksempelet er {}.'} # Norsk | |
| def detect_lang(sequence, labels): | |
| DetectorFactory.seed = 0 | |
| seq_lang = 'en' | |
| try: | |
| seq_lang = detect(sequence) | |
| lbl_lang = detect(labels) | |
| except: | |
| print("Language detection failed!", | |
| "Date:{}, Sequence:{}, Labels:{}".format( | |
| str(datetime.datetime.now()), | |
| labels)) | |
| if seq_lang != lbl_lang: | |
| print("Different languages detected for sequence and labels!", | |
| "Date:{}, Sequence:{}, Labels:{}, Sequence Language:{}, Label Language:{}".format( | |
| str(datetime.datetime.now()), | |
| sequence, | |
| labels, | |
| seq_lang, | |
| lbl_lang)) | |
| if seq_lang in models: | |
| print("Sequence Language detected:", | |
| "Date:{}, Sequence:{}, Sequence Language:{}".format( | |
| str(datetime.datetime.now()), | |
| sequence, | |
| labels)) | |
| else: | |
| print("Language not supported. Defaulting to English!", | |
| "Date:{}, Sequence:{}, Sequence Language:{}".format( | |
| str(datetime.datetime.now()), | |
| sequence, | |
| seq_lang)) | |
| seq_lang = 'en' | |
| return seq_lang | |
| def sequence_to_classify(sequence, labels): | |
| label_clean = str(labels).split(",") | |
| lang = detect_lang(sequence, labels) | |
| classifier = pipeline("zero-shot-classification", | |
| #hypothesis_template=hypothesis_templates[lang], | |
| model=models[lang]) | |
| response = classifier(sequence, label_clean, multi_class=True) | |
| predicted_labels = response['labels'] | |
| predicted_scores = response['scores'] | |
| clean_output = {idx: float(predicted_scores.pop(0)) for idx in predicted_labels} | |
| print("Date:{}, Sequence:{}, Labels: {}".format( | |
| str(datetime.datetime.now()), | |
| sequence, | |
| predicted_labels)) | |
| return clean_output | |
| example_text1="Folkehelseinstituttets mest optimistiske anslag er at alle voksne er ferdigvaksinert innen midten av september." | |
| example_labels1="politikk,helse,sport,religion" | |
| example_text2="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." | |
| example_labels2="helse,sport,religion, mat" | |
| iface = gr.Interface( | |
| title="Multilingual Multi-label Zero-shot Classification", | |
| description="Currently supported languages are English, German, Spanish, Italian, Russian, Norsk.", | |
| fn=sequence_to_classify, | |
| inputs=[gr.inputs.Textbox(lines=20, | |
| label="Please enter the text you would like to classify...", | |
| placeholder="Text here..."), | |
| gr.inputs.Textbox(lines=5, | |
| label="Possible candidate labels (separated by comma)...", | |
| placeholder="laLels here...")], | |
| outputs=gr.outputs.Label(num_top_classes=5), | |
| capture_session=True, | |
| #interpretation="default", | |
| examples=[ | |
| [example_text1, example_labels1], | |
| [example_text2, example_labels2] | |
| ]) | |
| iface.launch() | |