app (1).py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import PeftModel

# --- Load base model + your LoRA adapter ---
BASE_MODEL = "EleutherAI/gpt-neo-125M"
ADAPTER_MODEL = "khaliqabdull/humanizer3.0-lora"   

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(ADAPTER_MODEL)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    device_map="auto",
    load_in_8bit=True
)

# Attach LoRA adapter
model = PeftModel.from_pretrained(model, ADAPTER_MODEL)

# Create pipeline
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer
)

# --- Humanizer function ---
def humanize_text(text):
    prompt = f"Input:\n{text}\n\nHuman-like rewrite:\n"
    result = pipe(
        prompt,
        max_new_tokens=120,
        do_sample=True,
        temperature=0.7,
        top_p=0.9
    )
    return result[0]["generated_text"]

# --- Gradio UI ---
iface = gr.Interface(
    fn=humanize_text,
    inputs=gr.Textbox(lines=6, placeholder="Paste AI-like text here..."),
    outputs="text",
    title="🤖 Humanizer 3.0",
    description="Enter AI-like text and get a human-like rewrite."
)

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