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| import pkg_resources | |
| from transformers import pipeline | |
| import gradio as gr | |
| translator = pipeline("translation", model="Helsinki-NLP/opus-mt-is-en") | |
| sentiment_classifier = pipeline("text-classification", model="Birkir/electra-base-igc-is-sentiment-analysis") | |
| formality_classifier = pipeline("text-classification", model="svanhvit/formality-classification-icebert") | |
| detoxify_pipeline = pipeline('text-classification', model='unitary/toxic-bert', tokenizer='bert-base-uncased', function_to_apply='sigmoid', top_k=None) | |
| politeness_classifier = pipeline("text-classification", model="Genius1237/xlm-roberta-large-tydip") | |
| def translate_text(text): | |
| translation = translator(text, max_length=512) | |
| return translation[0]['translation_text'] | |
| def analyze_toxicity(text): | |
| toxicity_results = detoxify_pipeline(text) | |
| return toxicity_results[0] | |
| def analyze_politeness(text): | |
| politeness_result = politeness_classifier(text) | |
| return politeness_result[0]['label'], politeness_result[0]['score'] | |
| def analyze_formality(text): | |
| formality_result = formality_classifier(text) | |
| formality_label = formality_result[0]['label'] | |
| formality_score = formality_result[0]['score'] | |
| return formality_label, formality_score | |
| def analyze_sentiment(text): | |
| sentiment_result = sentiment_classifier(text) | |
| sentiment_label = sentiment_result[0]['label'] | |
| sentiment_score = sentiment_result[0]['score'] | |
| return sentiment_label, sentiment_score | |
| def analyze_text(icelandic_text): | |
| formality_label, formality_score = analyze_formality(icelandic_text) | |
| sentiment_label, sentiment_score = analyze_sentiment(icelandic_text) | |
| # Convert sentiment label | |
| sentiment_label = sentiment_label.replace("LABEL_", "") | |
| translated_text = translate_text(icelandic_text) | |
| toxicity_results = analyze_toxicity(translated_text) | |
| if isinstance(toxicity_results, list): | |
| toxicity_results = toxicity_results[0] | |
| # Determine toxicity label based on score | |
| toxicity_label = '1' if toxicity_results['score'] >= 0.5 else '0' | |
| politeness_label, politeness_score = analyze_politeness(translated_text) | |
| # Convert politeness label to binary | |
| politeness_label = '1' if politeness_label.lower() == 'polite' else '0' | |
| analysis_results = ( | |
| f"Translated Text: {translated_text}\n\n" | |
| f"Sentiment: Label: {sentiment_label}, Score: {round(sentiment_score, 2)}\n" | |
| f"Formality: Label: {formality_label}, Score: {round(formality_score, 2)}\n" | |
| f"Toxicity: Label: {toxicity_label}, Score: {round(toxicity_results['score'], 2)}\n" | |
| f"Politeness: Label: {politeness_label}, Score: {round(politeness_score, 2)}" | |
| ) | |
| return analysis_results | |
| demo = gr.Interface(fn=analyze_text, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter Icelandic Text Here..."), | |
| outputs=gr.Textbox(label="Analysis Results"), | |
| title="Icelandic Text Analysis", | |
| description="This app translates Icelandic text to English and performs sentiment, formality, toxicity, and politeness analysis.") | |
| if __name__ == "__main__": | |
| demo.launch() |