app.py

import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
import spaces

# Model configuration
MODEL_ID = "Qwen/Qwen3-8B-Instruct" # Replace with specific adversarial repo if different
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.float16,
    device_map="auto",
)

@spaces.GPU(duration=60)
def humanize_text(text, intensity):
    # Constructing the adversarial prompt
    prompt = f"Rewrite the following text to be highly human-like, varying sentence structure and avoiding common AI patterns. Maintain the original meaning perfectly.\n\nIntensity: {intensity}/10\nText: {text}\n\nHumanized Output:"
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    
    generation_kwargs = dict(
        inputs,
        streamer=streamer,
        max_new_tokens=1024,
        do_sample=True,
        temperature=0.7 + (intensity * 0.05), # Higher intensity = more randomness
        top_p=0.95,
    )
    
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    
    output_text = ""
    for new_text in streamer:
        output_text += new_text
        yield output_text

# Gradio Interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🤖 Adversarial Humanizer (Qwen3-8B)")
    gr.Markdown("Transform AI-generated text into human-like prose to bypass detection.")
    
    with gr.Row():
        with gr.Column():
            input_box = gr.Textbox(label="Input Text", lines=8, placeholder="Paste AI content here...")
            intensity_slider = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Humanization Intensity")
            submit_btn = gr.Button("Humanize", variant="primary")
        
        with gr.Column():
            output_box = gr.Textbox(label="Humanized Result", lines=10, interactive=False)
            
    submit_btn.click(
        fn=humanize_text,
        inputs=[input_box, intensity_slider],
        outputs=output_box
    )

demo.launch()