Create app.py

app.py

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+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()