# app.py
import gradio as gr
import spacy
import language_tool_python
from transformers import pipeline

# --- load lightweight models once ---
nlp = spacy.load("en_core_web_sm")
tool = language_tool_python.LanguageTool('en-US')
para = pipeline("text2text-generation", model="Vamsi/T5_Paraphrase_Paws")

def humanize(text, tone, strength, freeze):
    # detect entities to lock
    locked = [(ent.text, ent.label_) for ent in nlp(text).ents] if freeze else []
    # paraphrase
    paraphrased = para(text, max_length=512, do_sample=True, temperature=0.7 * strength / 10)[0]['generated_text']
    # grammar fix
    paraphrased = tool.correct(paraphrased)
    # restore locked entities
    for ent, label in locked:
        paraphrased = re.sub(re.escape(ent), ent, paraphrased, flags=re.IGNORECASE)
    return paraphrased

# --- Gradio interface ---
import re
iface = gr.Interface(
    fn=humanize,
    inputs=[
        gr.Textbox(label="Paste or type your text here", lines=10, placeholder="Type or paste your AI text…"),
        gr.Dropdown(["Casual", "Academic", "Marketing", "Legal", "Creative"], value="Casual", label="Tone"),
        gr.Slider(1, 10, value=5, label="Humanize Strength (1 = subtle, 10 = aggressive)"),
        gr.Checkbox(label="Lock facts / dates / names")
    ],
    outputs=gr.Textbox(label="Humanized text", lines=10),
    title="AI Humanizer – 100 % Free & Unlimited",
    description="Zero sign-up, zero cost. Paste, choose options, click “Submit”."
)

iface.launch()