app.py

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

# Load model
MODEL = "roberta-base-openai-detector"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)

def detect_ai(text):
    sentences = re.split(r'(?<=[.!?]) +', text)
    results = []

    for sent in sentences:
        if not sent.strip():
            continue
        inputs = tokenizer(sent, return_tensors="pt", truncation=True, max_length=512)
        with torch.no_grad():
            outputs = model(**inputs)
            probs = torch.softmax(outputs.logits, dim=1)
        ai_score = float(probs[0][1])   # fix here
        results.append({"sentence": sent, "ai_score": ai_score})

    highlighted = ""
    for r in results:
        color = f"rgba(255,0,0,{r['ai_score']})"
        highlighted += f"<span style='background-color:{color}; padding:2px'>{r['sentence']} </span>"

    return highlighted, results


with gr.Blocks() as demo:
    gr.Markdown("## 🤖 AI Detector (like ZeroGPT)")
    gr.Markdown("Paste your text below. Redder highlights = more AI-like.")
    input_text = gr.Textbox(lines=8, placeholder="Enter text here...")
    output_html = gr.HTML()
    output_json = gr.JSON()
    run_btn = gr.Button("Detect AI")

    run_btn.click(detect_ai, inputs=input_text, outputs=[output_html, output_json])

demo.launch()