Spaces:
Runtime error
Runtime error
import gradio as gr | |
import numpy as np | |
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification | |
tokenizer = AutoTokenizer.from_pretrained("risingodegua/hate-speech-detector") | |
model = TFAutoModelForSequenceClassification.from_pretrained("risingodegua/hate-speech-detector") | |
def make_prediction(text): | |
''' | |
This function takes a string as input and returns a prediction for the hate speech class. | |
Hate speech class labels are: Normal(0), Offensive(1), and Hate speech(2). | |
Parameters: | |
text (str): The text to be classified. | |
Returns: | |
str: The predicted class label. | |
''' | |
input_ids = tokenizer.encode(text) | |
input_ids = np.array(input_ids) | |
input_ids = np.expand_dims(input_ids, axis=0) | |
prediction_arr = model.predict(input_ids)[0][0] | |
labels = ["Normal", "Offensive", "Hate Speech"] | |
prediction = labels[np.argmax(prediction_arr)] | |
return prediction | |
iface = gr.Interface( | |
fn=make_prediction, | |
inputs=gr.inputs.Textbox(lines=3, placeholder="Enter your text here..."), | |
outputs="text", | |
title="Hate Speech Detector", | |
description="A model for detecting if a given text is an hate speech.", | |
) | |
iface.launch() |