app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "Rerandaka/child-safety-01"

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

label_map = {
    0: "Safe / Normal",
    1: "Inappropriate / Unsafe"
}

def classify_text(text: str):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
        predicted = torch.argmax(probs, dim=1).item()
        confidence = probs[0][predicted].item()
    return f"{label_map.get(predicted, predicted)} (Confidence: {confidence:.2f})"

demo = gr.Interface(
    fn=classify_text,
    inputs=gr.Textbox(label="Enter text to classify"),
    outputs=gr.Textbox(label="Prediction"),
    title="Child-Safety Text Classifier",
    description="This model detects unsafe or inappropriate text for children.",
    flagging_mode="never"
)

demo.launch()  # 🚫 DO NOT include api_name