fix the error

app.py

@@ -2,18 +2,16 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load model and tokenizer
-model_id = "Rerandaka/Cild_safety_bigbird"
+model_id = "Rerandaka/child-safety-01"
+
 tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
 model = AutoModelForSequenceClassification.from_pretrained(model_id)
 
-# Class mapping (optional — edit as needed)
 label_map = {
     0: "Safe / Normal",
     1: "Inappropriate / Unsafe"
 }
 
-# Inference function
 def classify_text(text: str):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
     with torch.no_grad():
@@ -21,23 +19,15 @@ def classify_text(text: str):
         probs = torch.nn.functional.softmax(outputs.logits, dim=1)
         predicted = torch.argmax(probs, dim=1).item()
         confidence = probs[0][predicted].item()
-    return {
-        "label": label_map.get(predicted, str(predicted)),
-        "confidence": round(confidence, 4)
-    }
+    return f"{label_map.get(predicted, predicted)} (Confidence: {confidence:.2f})"
 
-# Define Gradio Interface
 demo = gr.Interface(
     fn=classify_text,
     inputs=gr.Textbox(label="Enter text to classify"),
-    outputs=[
-        gr.Textbox(label="Predicted Label"),
-        gr.Textbox(label="Confidence")
-    ],
+    outputs=gr.Textbox(label="Prediction"),
     title="Child-Safety Text Classifier",
-    description="This model detects if text content is unsafe or inappropriate for children.",
-    allow_flagging="never"
+    description="This model detects unsafe or inappropriate text for children.",
+    flagging_mode="never"
 )
 
-# Expose API endpoint explicitly
-demo.launch(api_name="predict")
+demo.launch()  # 🚫 DO NOT include api_name