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import gradio as gr
import json
import re
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
# Load Swear Words
try:
with open("swearWord.json", "r") as f:
swear_words = set(json.load(f))
print("Swear words loaded successfully.")
except Exception as e:
print(f"Failed to load swearWord.json: {e}")
swear_words = set()
# Load Model and Tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained("eliasalbouzidi/distilbert-nsfw-text-classifier")
model = AutoModelForSequenceClassification.from_pretrained("eliasalbouzidi/distilbert-nsfw-text-classifier")
text_classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print("Model loaded successfully.")
except Exception as e:
print(f"Error loading model: {e}")
exit(1)
# Text Classification and Censorship Function
def textclassifier(text):
if not text.strip():
return "Empty input", "unknown", 0.0
# Censor known swear words
def censor_word(word):
return "***" if word.lower() in swear_words else word
words = re.findall(r"\w+|[^\w\s]", text, re.UNICODE)
censored_words = [censor_word(word) if re.match(r"\w+", word) else word for word in words]
censored_text = " ".join(censored_words)
# Run model on original input
try:
result = text_classifier(text)
label = result[0]["label"]
score = result[0]["score"]
# Apply threshold for uncertainty
threshold = 0.994
if label == "nsfw" and score < threshold:
label = "uncertain"
return censored_text, label, round(score, 4)
except Exception as e:
return censored_text, f"Error: {str(e)}", 0.0
# Gradio Interface
interface = gr.Interface(
fn=textclassifier,
inputs=gr.Textbox(label="Enter text"),
outputs=[
gr.Textbox(label="Censored Text"),
gr.Label(label="NSFW Prediction"),
gr.Number(label="Confidence Score")
],
title="Text Censorship + NSFW Classifier",
description="Censors known swear words using *** and classifies the original text as NSFW, Safe, or Uncertain."
)
if __name__ == "__main__":
interface.launch()
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