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# -*- coding: utf-8 -*-
"""app.py
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1mHMq54a9glSyzg_CVIP0wshX9T8NUtmx
"""
import gradio as gr
from transformers import pipeline
pipe = pipeline(model="delarosajav95/HateSpeech-BETO-cased-v2")
#function that Gradio will use to classify
def classify_text(inputs):
result = pipe(inputs, return_all_scores=True)
output = []
label_mapping = {
'LABEL_0': 'Non-Hate Speech',
'LABEL_1': 'Hate Speech'
}
for i, predictions in enumerate(result):
for pred in predictions:
label = label_mapping.get(pred['label'], pred['label'])
score = pred['score']
output.append(f"{label}: {score:.2%}")
return "\n".join(output)
#defining Gradio interface
textbox = gr.Textbox(lines=3, placeholder="Enter a Spanish user review, comment, or opinion to evaluate (e.g., 'Esas personas no merecen respeto.')",
label="User Comment/Post:")
output_box = gr.Textbox(label="Results:")
iface = gr.Interface(
fn=classify_text,
inputs=textbox,
outputs=output_box,
live=True,
title="Spanish Hate Speech Classifier for User Content",
allow_flagging="never",
)
# Launch the interface
iface.launch() |