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app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+MODEL_ID = "gorkem371/toxicity-classifier-xlmr-base-v3"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+model.eval()
+
+LABELS = ["Not Toxic", "Toxic"]
+
+
+def classify(text: str) -> dict:
+    """Return probability distribution over Not Toxic / Toxic."""
+    if not text or not text.strip():
+        return {label: 0.0 for label in LABELS}
+
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=256,
+        padding=True,
+    )
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    probs = torch.softmax(logits, dim=-1)[0]
+    return {LABELS[i]: float(probs[i]) for i in range(len(LABELS))}
+
+
+examples = [
+    # English
+    ["You are such a wonderful person, keep up the great work!"],
+    ["I hope you suffer and never find happiness in your life."],
+    ["The weather is beautiful today, perfect for a walk."],
+    # Turkish
+    ["Harika bir insansınız, paylaşımlarınız çok değerli."],
+    ["Seni gördüğüm yerde döverim, haddini bil."],
+    ["Bugün hava çok güzel, yürüyüşe çıkalım."],
+    # Arabic
+    ["أنت إنسان رائع ومشاركاتك مفيدة جداً."],
+    ["اخرس يا حيوان، لا أحد يهتم برأيك."],
+    ["اليوم طقس جميل، فلنخرج للتنزه."],
+]
+
+demo = gr.Interface(
+    fn=classify,
+    inputs=gr.Textbox(
+        label="Enter text",
+        placeholder="Type or paste text in Turkish, Arabic, or English...",
+        lines=3,
+    ),
+    outputs=gr.Label(label="Prediction"),
+    title="Multilingual Toxicity Classifier",
+    description=(
+        "Binary toxicity classifier for Turkish, Arabic, and English. "
+        "Built with XLM-RoBERTa-base, fine-tuned on 120K balanced samples."
+    ),
+    examples=examples,
+    theme=gr.themes.Soft(),
+    allow_flagging="never",
+)
+
+if __name__ == "__main__":
+    demo.launch()