Create app.py

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the model
+MODEL_PATH = "unitary/toxic-bert"
+classifier = pipeline("text-classification", model=MODEL_PATH, tokenizer=MODEL_PATH)
+
+def predict_toxicity(text):
+    # Get predictions
+    predictions = classifier(text, return_all_scores=True)[0]
+    
+    # Format results
+    results = {}
+    for pred in predictions:
+        results[pred['label']] = f"{pred['score']:.4f}"
+    
+    return results
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_toxicity,
+    inputs=gr.Textbox(lines=5, label="Enter text to analyze"),
+    outputs=gr.Label(num_top_classes=6, label="Toxicity Scores"),
+    title="Toxicity Prediction",
+    description="This POC uses the model based onm toxic-bert to predict toxicity in text. Multi-class response.",
+    examples=[
+        ["Great game everyone!"],
+        ["You're such a noob, uninstall please."],
+        ["I hope you die in real life, loser."],
+        ["Nice move! How did you do that?"],
+        ["Go back to the kitchen where you belong."],
+    ]
+)
+
+# Launch the app
+iface.launch()