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# -*- coding: utf-8 -*-
"""app.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1CbDOX8PDJB6ZyLZiLMXbPyr6k7dvrs20
"""
import gradio as gr
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load the model and tokenizer
model_name = "qarib/bert-base-qarib"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
# Preprocessing function
def light_preprocess(text):
text = text.replace("@USER", "").replace("RT", "").strip()
return text
# Prediction function
def predict_offensive(text):
preprocessed_text = light_preprocess(text)
inputs = tokenizer(preprocessed_text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
return "Offensive" if predicted_class == 1 else "Not Offensive"
# Create the Gradio interface
iface = gr.Interface(
fn=predict_offensive,
inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
outputs="text",
title="Offensive Language Detection",
description="Enter a text to check if it's offensive or not.",
)
# Launch the interface
iface.launch(share=True) |