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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() |