Create app.py

app.py

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+import re
+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from deep_translator import GoogleTranslator
+
+# -------------------- Load Kannada Model & Tokenizer --------------------
+MODEL_PATH = "Thilak118/indic-bert-toxicity-classifier_kannada"
+
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# -------------------- Translator (English → Kannada) --------------------
+translator = GoogleTranslator(source="en", target="kn")
+
+# -------------------- Clean Kannada Text --------------------
+def clean_text(text):
+    text = re.sub(r"[^\u0C80-\u0CFF\s.,!?]", "", str(text))
+    text = re.sub(r"\s+", " ", text).strip()
+    return text
+
+# -------------------- Transliterate / Translate to Kannada --------------------
+def transliterate_to_kannada(text):
+    if text and text.strip():
+        try:
+            return translator.translate(text)
+        except Exception:
+            return "Translation failed"
+    return ""
+
+# -------------------- Toxicity Prediction --------------------
+def predict_toxicity(english_text):
+    kannada_text = transliterate_to_kannada(english_text)
+
+    if "failed" in kannada_text.lower():
+        return f"Transliterated Kannada Text: {kannada_text}\nPrediction: Failed"
+
+    cleaned_text = clean_text(kannada_text)
+
+    inputs = tokenizer(
+        cleaned_text,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=128
+    ).to(device)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    prediction = torch.argmax(outputs.logits, dim=1).item()
+    probs = torch.softmax(outputs.logits, dim=1)[0]
+    confidence = probs[prediction].item() * 100
+
+    label = "Toxic" if prediction == 0 else "Non-Toxic"
+
+    return (
+        f"Transliterated Kannada Text: {cleaned_text}\n"
+        f"Prediction: {label}\n"
+        f"Confidence: {confidence:.2f}%"
+    )
+
+# -------------------- Gradio UI --------------------
+with gr.Blocks(title="Kannada Text Toxicity Classifier (IndicBERT)") as demo:
+    gr.Markdown(
+        """
+        # Kannada Text Toxicity Classifier (IndicBERT + Transliteration)
+
+        Enter Kannada text written in **English letters**  
+        Example:  
+        **`ninage ashtondu seen illa le`**
+
+        The app converts it into **Kannada script** and predicts toxicity.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Enter Kannada Text (English Transliteration)",
+                placeholder="e.g., ninage ashtondu seen illa le",
+                lines=2
+            )
+        with gr.Column():
+            transliterated_text = gr.Textbox(
+                label="Kannada Script (Preview)",
+                interactive=False,
+                lines=2
+            )
+
+    with gr.Row():
+        preview_btn = gr.Button("Preview Transliteration")
+        predict_btn = gr.Button("Predict Toxicity")
+
+    output_text = gr.Textbox(
+        label="Prediction Output",
+        interactive=False,
+        lines=5
+    )
+
+    preview_btn.click(
+        fn=transliterate_to_kannada,
+        inputs=input_text,
+        outputs=transliterated_text
+    )
+
+    predict_btn.click(
+        fn=predict_toxicity,
+        inputs=input_text,
+        outputs=output_text
+    )
+
+# -------------------- Launch App --------------------
+demo.launch()