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import gradio as gr
import torch
from transformers import pipeline
app_title = "Portuguese Hate Speech Detection"
app_description = """
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.
This app leverages state-of-the-art Natural Language Processing (NLP) models developed in the scope of this project to classify harmful text.
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.
For more information about the kNOwHATE project and its initiatives, visit our website [here](https://knowhate.eu).
"""
app_examples = [
["As pessoas tem que perceber que ser 'panasca' não é deixar de ser homem, é deixar de ser humano 😂😂", "knowhate/HateBERTimbau-youtube"],
["Vamo-nos unir para criar um mundo mais inclusivo e tolerante.", "knowhate/HateBERTimbau-twitter"],
["Isso pulhiticos merdosos, continuem a importar lixo, até Portugal deixar de ser Portugal.", "knowhate/HateBERTimbau-yt-tt"],
["Eu admiro muito a coragem e a determinação da minha colega de trabalho.", "knowhate/HateBERTimbau"],
["Vai pá puta que te pariu seu paneleiro do caralho, virgem ofendida", "knowhate/HateBERTimbau-youtube"],
["O tempo está ensolarado hoje, perfeito para um passeio no parque.", "knowhate/HateBERTimbau-twitter"]
]
model_list = [
"knowhate/HateBERTimbau",
"knowhate/HateBERTimbau-youtube",
"knowhate/HateBERTimbau-twitter",
"knowhate/HateBERTimbau-yt-tt",
]
def predict(text, chosen_model):
# Initialize the pipeline with the chosen model
model_pipeline = pipeline("text-classification", model=chosen_model)
result = model_pipeline(text)
predicted_label = result[0]['label']
predicted_score = result[0]['score']
non_predicted_label = "Hate Speech" if predicted_label == "Non Hate Speech" else "Non Hate Speech"
non_predicted_score = 1 - predicted_score
scores_dict = {
predicted_label: predicted_score,
non_predicted_label: non_predicted_score
}
return scores_dict#, predicted_label
inputs = [
gr.Textbox(label="Text", value= app_examples[0][0]),
gr.Dropdown(label="Model", choices=model_list, value=model_list[2])
]
outputs = [
gr.Label(label="Result"),
]
gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title=app_title,
description=app_description, examples=app_examples).launch() |