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import gradio as gr
import json
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 Classifier Function
def textclassifier(text):
if not text.strip():
return "Empty input", 0.0
# Check for swear words
if any(word.lower() in swear_words for word in text.split()):
return "swear-word", 1.0
# Use model
try:
result = text_classifier(text)
label = result[0]["label"]
score = result[0]["score"]
# Threshold logic
threshold = 0.994
if label == "nsfw" and score < threshold:
label = "uncertain"
return label, round(score, 4)
except Exception as e:
return f"Error: {str(e)}", 0.0
# Gradio Interface
interface = gr.Interface(
fn=textclassifier,
inputs=gr.Textbox(label="Enter text"),
outputs=[
gr.Label(label="Prediction"),
gr.Number(label="Confidence Score")
],
title="Text Classifier with Swear Word Filter",
# description="First checks for swear words, then uses NSFW text classifier if no swear word is found."
)
interface.launch()
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