Spaces:
Runtime error
Runtime error
| import datetime | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| from langdetect import detect, DetectorFactory, detect_langs | |
| import fasttext | |
| from transformers import pipeline | |
| models = {'en': 'Narsil/deberta-large-mnli-zero-cls'} #Uzbek | |
| hypothesis_templates = {'en': 'This example is {}.'} # Uzbek | |
| classifiers = {'en': pipeline("zero-shot-classification", hypothesis_template=hypothesis_templates['en'], | |
| model=models['en'])} | |
| fasttext_model = fasttext.load_model(hf_hub_download("julien-c/fasttext-language-id", "lid.176.bin")) | |
| def prep_examples(): | |
| example_text1 = "Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most \ | |
| people who fall sick with COVID-19 will experience mild to moderate symptoms and recover without special treatment. \ | |
| However, some will become seriously ill and require medical attention." | |
| example_labels1 = "business,health related,politics,climate change" | |
| examples = [ | |
| [example_text1, example_labels1] | |
| ] | |
| return examples | |
| def detect_lang(sequence, labels): | |
| DetectorFactory.seed = 0 | |
| seq_lang = 'en' | |
| try: | |
| #seq_lang = detect(sequence) | |
| #lbl_lang = detect(labels) | |
| seq_lang = fasttext_model.predict(sequence, k=1)[0][0].split("__label__")[1] | |
| lbl_lang = fasttext_model.predict(labels, k=1)[0][0].split("__label__")[1] | |
| 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, | |
| seq_lang)) | |
| 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): | |
| classifier = classifiers[detect_lang(sequence, labels)] | |
| label_clean = str(labels).split(",") | |
| response = classifier(sequence, label_clean, multi_label=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 | |
| iface = gr.Interface( | |
| title="En Multi-label Zero-shot Classification", | |
| description="Supported languages are: English", | |
| fn=sequence_to_classify, | |
| inputs=[gr.inputs.Textbox(lines=10, | |
| label="Please enter the text you would like to classify...", | |
| placeholder="Text here..."), | |
| gr.inputs.Textbox(lines=2, | |
| label="Please enter the candidate labels (separated by comma)...", | |
| placeholder="Labels here separated by comma...")], | |
| outputs=gr.outputs.Label(num_top_classes=5), | |
| #interpretation="default", | |
| examples=prep_examples()) | |
| iface.launch() |