import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Load the AI Humaniser model
print("Loading model...")
model_name = "NoaiGPT/777"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
print("Model loaded successfully.")

def humanise_text(original_text, temperature=0.7):
    if not original_text.strip():
        return "Please enter some text to humanise."
    
    input_text = f"paraphraser: {original_text}"
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512)
    
    outputs = model.generate(
        **inputs,
        num_beams=5,
        num_return_sequences=1,
        temperature=temperature,
        max_length=512,
        no_repeat_ngram_size=2,
        early_stopping=True
    )
    
    humanised = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return humanised

# Create the Gradio interface
demo = gr.Interface(
    fn=humanise_text,
    inputs=[
        gr.Textbox(label="Original (AI-generated) Text", lines=6, placeholder="Paste your AI text here..."),
        gr.Slider(label="Creativity (Temperature)", minimum=0.3, maximum=1.2, value=0.7, step=0.05)
    ],
    outputs=gr.Textbox(label="Humanised Text", lines=6),
    title="AI Humaniser",
    description="Paste AI-generated text. This tool rewrites it to sound more human and avoid AI detection.",
    theme="soft"
)

demo.launch()