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import gradio as gr |
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from transformers import TFBertModel, TFXLMRobertaModel |
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import numpy as np |
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import tensorflow as tf |
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from transformers import AutoTokenizer |
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app_title = "Portuguese Counter Hate Speech Detection (NFAA)" |
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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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["Essa gente tem é de deixar de ser apaparicada pelo Estado e começar a cumprir os seus deveres como cidadãos", |
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"Nepia o que faz com que as pessoas generalizem é o ódio intrínseco que têm contra uma etnia, ng é responsável pela sua xenofobia", |
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"knowhate/twt-bertimbau/twt-bb-b16e5-avg767.keras"], |
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["Nem vou comentar o hate e misoginia que tenho visto aqui no tt em relação à Anitta", |
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"E xenofobia também. Tugas no seu melhor", |
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"knowhate/twt-bertimbau/twt-bb-b16e5-avg767.keras"], |
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["A Festa tá no Climax, chama o zuca pra Dançar.", |
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"Já reparaste no contador da luz? Vai trabalhar malandro", |
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"knowhate/twt-bertimbau/twt-bb-b16e5-avg767.keras"] |
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] |
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model_list = [ |
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"knowhate/twt-bertimbau/twt-bb-b16e5-avg767.keras" |
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] |
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def predict(text, target, chosen_model): |
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model1 = tf.keras.models.load_model(chosen_model, custom_objects={"TFBertModel": TFBertModel}) |
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checkpoint = "neuralmind/bert-base-portuguese-cased" |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_fast=True) |
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tokpair = tokenizer(text, target, truncation=True, padding='max_length', return_tensors='np') |
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outp = model1(tokpair) |
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proto_tensor = tf.make_tensor_proto(outp) |
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allscores = tf.make_ndarray(proto_tensor)[0] |
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scores_dict = { |
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'Neutral': allscores[0], |
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'Counter Speech': allscores[1], |
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'Hate Speech': allscores[2] |
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} |
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return scores_dict |
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inputs = [ |
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gr.Textbox(label="Context", value= app_examples[0][0]), |
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gr.Textbox(label="Target", value= app_examples[0][1]), |
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gr.Dropdown(label="Model", choices=model_list, value=model_list[0]) |
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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() |
