app.py

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

# Load model
model_id = "Rerandaka/Cild_safety_bigbird"
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)

# Create Gradio interface
with gr.Blocks() as demo:
    txt_in = gr.Textbox(label="Enter text")
    txt_out = gr.Textbox(label="Prediction")
    btn = gr.Button("Classify")
    btn.click(classify, txt_in, txt_out)

# Launch with API enabled
demo.launch()