Create app.py

app.py

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+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)
+
+labels = [
+    "toxic",
+    "severe_toxic",
+    "obscene",
+    "threat",
+    "insult",
+    "identity_hate"
+]
+
+def classify(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    scores = torch.sigmoid(outputs.logits)[0].tolist()
+
+    result = {labels[i]: float(scores[i]) for i in range(len(labels))}
+    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()