app.py

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

# Load model and tokenizer
model_name = "Hello-SimpleAI/chatgpt-detector-roberta"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def detect_ai_text(text):
    if not text or len(text.strip()) == 0:
        return {"error": "Please provide text to analyze"}
    
    # Tokenize
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True)
    
    # Get prediction
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.softmax(logits, dim=-1)
    
    # Get probabilities
    human_prob = probs[0][0].item()
    ai_prob = probs[0][1].item()
    
    return {
        "human_probability": round(human_prob * 100, 2),
        "ai_probability": round(ai_prob * 100, 2),
        "prediction": "AI Generated" if ai_prob > 0.5 else "Human Written"
    }

# Create Gradio interface
iface = gr.Interface(
    fn=detect_ai_text,
    inputs=gr.Textbox(lines=10, placeholder="Enter text to analyze..."),
    outputs=gr.JSON(label="Detection Results"),
    title="AI Text Detector",
    description="Detects whether text was written by a human or generated by AI"
)

if __name__ == "__main__":
    iface.launch()