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eef9db5 df66169 eef9db5 df66169 eef9db5 df66169 eef9db5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
MODEL_NAME = "s-nlp/roberta_toxicity_classifier"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
# FIX 1: Dynamically pull the exact labels the model was trained on
labels = model.config.id2label
def classify(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True)
with torch.no_grad():
outputs = model(**inputs)
# FIX 2: Use softmax for binary/multi-class classification to get probabilities
scores = torch.softmax(outputs.logits, dim=-1)[0].tolist()
# FIX 3: Safely map the scores to the dynamic labels
result = {labels[i]: float(scores[i]) for i in range(len(scores))}
return result
demo = gr.Interface(
fn=classify,
inputs=gr.Textbox(label="Enter English text"),
outputs=gr.JSON(label="Toxicity scores"),
title="English Toxicity Detection",
description="Model: s-nlp/roberta_toxicity_classifier"
)
demo.launch() |