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import gradio as gr
from transformers import pipeline
# Load the toxicity classification pipeline
print("Loading toxicity classifier pipeline...")
toxicity_pipeline = pipeline(
"text-classification",
model="s-nlp/roberta_toxicity_classifier",
tokenizer="s-nlp/roberta_toxicity_classifier",
# return_all_scores=True # This ensures we get scores for both classes
)
print("Pipeline loaded successfully!")
def toxicity_classification(text: str) -> dict:
"""
Classify the toxicity of the given text.
Args:
text (str): The text to analyze
Returns:
dict: A dictionary containing toxicity scores and classification
"""
if not text.strip():
return {
"error": "Please enter some text to analyze"
}
try:
# Get predictions using the pipeline
result = toxicity_pipeline(text)[0]
# The pipeline returns a list like: [{'label': 'LABEL_0', 'score': 0.8}]
# LABEL_0 is non-toxic, LABEL_1 is toxic (based on the model description)
# Convert to a more readable format
label = result['label']
confidence = result['score']
classification = "non-toxic" if label == "LABEL_0" else "toxic"
return {
"classification": classification,
"confidence": round(confidence, 4)
}
except Exception as e:
return {
"error": f"Error processing text: {str(e)}"
}
# Create the Gradio interface
demo = gr.Interface(
fn=toxicity_classification,
inputs=gr.Textbox(
placeholder="Enter text to analyze for toxicity...",
lines=3,
label="Input Text"
),
outputs=gr.JSON(label="Toxicity Analysis Results"),
title="Text Toxicity Classification",
description="Analyze text toxicity using RoBERTa transformer model (s-nlp/roberta_toxicity_classifier)",
examples=[
["You are amazing!"],
["This is a wonderful day."],
["I disagree with your opinion."],
["The weather is nice today."]
]
)
# Launch the interface and MCP server
if __name__ == "__main__":
# Add debugging section for local testing
import sys
# Check if running in debug mode (pass 'debug' as command line argument)
if len(sys.argv) > 1 and sys.argv[1] == "debug":
print("=" * 50)
print("DEBUG MODE - Testing toxicity classification")
print("=" * 50)
# Test cases for debugging
test_cases = [
"You are amazing!",
"This is a wonderful day.",
"I hate you so much!",
"The weather is nice today.",
"You're such an idiot!",
"I disagree with your opinion.",
"" # Empty string test
]
for i, test_text in enumerate(test_cases):
print(f"\n--- Test Case {i} ---")
print(f"Input: '{test_text}'")
# Set breakpoint here for debugging
import pdb; pdb.set_trace()
# Call the function
result = toxicity_classification(test_text)
print(f"Output: {result}")
print("-" * 30)
print("\nDebug testing completed!")
else:
# Normal Gradio mode
demo.launch(mcp_server=True) |