app.py

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

# Load model and tokenizer
model_id = "Rerandaka/Child_safty_bigbird_1"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

# Inference function
def classify(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
    with torch.no_grad():
        logits = model(**inputs).logits
        predicted_class = torch.argmax(logits, dim=1).item()
    return str(predicted_class)

# Use Blocks to define interface and API
with gr.Blocks() as demo:
    gr.Markdown("## Child-Safety Text Classifier\nThis model detects unsafe or inappropriate text for children.")

    with gr.Row():
        input_box = gr.Textbox(label="Enter text to classify")
        output_box = gr.Textbox(label="Prediction")

    btn = gr.Button("Submit")
    btn.click(fn=classify, inputs=input_box, outputs=output_box)

    # ✅ Named API endpoint
    demo.load(fn=classify, inputs=input_box, outputs=output_box, api_name="predict")

# Launch space
demo.launch()