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import gradio as gr |
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import torch |
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from transformers import pipeline |
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app_title = "Portuguese Hate Speech Detection" |
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app_description = """ |
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This app is the culmination of the kNOwHATE consortium project, which aimed to tackle Online Hate Speech in the Portuguese comunity. It serves as an user-friendly interface to classify text and identify instances of Hate Speech. |
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This app leverages state-of-the-art Natural Language Processing models developed in the scope of this project to classify harmful text. |
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Select a model from the dropdown menu and input your text to see the classification results. Explore the examples of Hate Speech and Non-Hate Speech offered, and join us in fostering a safer and more respectful online community. |
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For more information about the kNOwHATE project and its initiatives, visit our website [here](https://knowhate.eu) and to explore and use these models visit our Hugging Face page [here](https://huggingface.co/knowhate). |
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""" |
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app_examples = [ |
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["As pessoas tem que perceber que ser 'panasca' não é deixar de ser homem, é deixar de ser humano 😂😂", "knowhate/HateBERTimbau-youtube"], |
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["Vamo-nos unir para criar um mundo mais inclusivo e tolerante.", "knowhate/HateBERTimbau-twitter"], |
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["Isso pulhiticos merdosos, continuem a importar lixo, até Portugal deixar de ser Portugal.", "knowhate/HateBERTimbau-yt-tt"], |
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["Eu admiro muito a coragem e a determinação da minha colega de trabalho.", "knowhate/HateBERTimbau-twitter"], |
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["Vai pá puta que te pariu seu paneleiro do caralho, virgem ofendida", "knowhate/HateBERTimbau-youtube"], |
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["O tempo está ensolarado hoje, perfeito para um passeio no parque.", "knowhate/HateBERTimbau-yt-tt"] |
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] |
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model_list = [ |
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"knowhate/HateBERTimbau-youtube", |
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"knowhate/HateBERTimbau-twitter", |
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"knowhate/HateBERTimbau-yt-tt", |
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] |
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def predict(text, chosen_model): |
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model_pipeline = pipeline("text-classification", model=chosen_model) |
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result = model_pipeline(text) |
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predicted_label = result[0]['label'] |
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predicted_score = result[0]['score'] |
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non_predicted_label = "Hate Speech" if predicted_label == "Non Hate Speech" else "Non Hate Speech" |
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non_predicted_score = 1 - predicted_score |
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scores_dict = { |
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predicted_label: predicted_score, |
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non_predicted_label: non_predicted_score |
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} |
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return scores_dict |
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inputs = [ |
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gr.Textbox(label="Text", value= app_examples[0][0]), |
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gr.Dropdown(label="Model", choices=model_list, value=model_list[2]) |
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] |
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outputs = [ |
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gr.Label(label="Result"), |
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] |
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gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title=app_title, |
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description=app_description, examples=app_examples, theme=gr.themes.Base(primary_hue="red")).launch() |
