Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("Sk1306/student_chat_toxicity_classifier_model")
+model = AutoModelForSequenceClassification.from_pretrained("Sk1306/student_chat_toxicity_classifier_model")
+def predict_toxicity(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    outputs = model(**inputs)
+    logits = outputs.logits
+
+    # Apply softmax to get probabilities
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+
+    # Get the predicted class (index 0 for non-toxic, index 1 for toxic)
+    predicted_class = torch.argmax(probabilities, dim=-1).item()
+
+    # Map the prediction to the label (0 = Non-toxic, 1 = Toxic)
+    if predicted_class == 0:
+        return "Non-toxic"
+    else:
+        return "Toxic"
+
+interface = gr.Interface(
+    fn=predict_toxicity,
+    inputs="text",  # Text input from the user
+    outputs="text",  # Text output for the prediction
+    title="Student Chat Toxicity Classifier",
+    description="Enter a message",
+    theme="dark",
+    examples=[
+        "You can copy in exam to pass!",
+        "Study well.Hardwork pays off!",
+        "Take these drugs.It will boost your memory",
+    ],
+    
+)
+interface.launch(inline=False)