app.py

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

MODEL = "roberta-base-openai-detector"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)

def detect_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True)
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)
    return {"Human": probs[0][0].item(), "AI": probs[0][1].item()}

demo = gr.Interface(
    fn=detect_text,
    inputs=gr.Textbox(lines=5, placeholder="Paste text here..."),
    outputs="label",
    title="AI Text Detector Chatbot 🤖",
    description="Detect whether text is human-written or AI-generated."
)

demo = gr.Interface(
    fn=detect_text,
    inputs=gr.Textbox(lines=5, placeholder="Paste text here..."),
    outputs=gr.Label(),   # shows classification with confidence
    title="AI Text Detector Chatbot 🤖",
    description="Detect whether text is human-written or AI-generated."
)

demo.launch(share=True)