# app.py
import gradio as gr
import spacy, re, subprocess, sys, os
from spellchecker import SpellChecker
from transformers import pipeline

subprocess.check_call([sys.executable, "-m", "spacy", "download", "en_core_web_sm"])
nlp = spacy.load("en_core_web_sm")
spell = SpellChecker(language='en')

# 🔧 1) raise max_new_tokens so each chunk can be LONGER
para = pipeline(
    "text2text-generation",
    model="Vamsi/T5_Paraphrase_Paws",
    device=-1,
    max_new_tokens=2000  # 2000 tokens ≈ 1 500 words
)

def simple_correct(text):
    return " ".join(spell.correction(w) or w for w in text.split())

def chunk_text(text, max_words=350):
    words = text.split()
    for i in range(0, len(words), max_words):
        yield " ".join(words[i:i + max_words])

def humanize(text, tone, strength, freeze):
    locked = [(ent.text, ent.label_) for ent in nlp(text).ents] if freeze else []
    out_chunks = []

    for chunk in chunk_text(text, 350):
        # 🔧 2) tell the model *not to summarize*
        prompt = f"Rewrite the following text naturally without shortening it:\n{chunk}"
        paraphrased = para(
            prompt,
            max_new_tokens=2000,
            do_sample=True,
            temperature=0.7 * strength / 10
        )[0]['generated_text']

        paraphrased = simple_correct(paraphrased)
        for ent, label in locked:
            paraphrased = re.sub(re.escape(ent), ent, paraphrased, flags=re.IGNORECASE)
        out_chunks.append(paraphrased)

    full_text = " ".join(out_chunks)

    file_path = "/tmp/full_humanized.txt"
    with open(file_path, "w", encoding="utf-8") as f:
        f.write(full_text)
    return full_text, file_path

with gr.Blocks(title="AI Humanizer – Exact Length") as demo:
    gr.Markdown("## AI Humanizer – Exact Length, No Shortening")
    with gr.Row():
        with gr.Column():
            text_in = gr.Textbox(label="Paste your text (any length)", lines=15, max_lines=None)
            tone_dd = gr.Dropdown(["Casual", "Academic", "Marketing", "Legal", "Creative"], value="Casual", label="Tone")
            strength_sl = gr.Slider(1, 10, value=5, label="Strength 1-10")
            lock_cb = gr.Checkbox(label="Lock facts / dates / names")
            submit_btn = gr.Button("Humanize", variant="primary")

        with gr.Column():
            # 🔧 3) make the output box big enough
            text_out = gr.Textbox(label="Humanized text (same length)", lines=30, interactive=True)
            with gr.Row():
                copy_btn = gr.Button("📋 Copy")
                file_out = gr.File(label="Download .txt")

    submit_btn.click(humanize, inputs=[text_in, tone_dd, strength_sl, lock_cb],
                     outputs=[text_out, file_out])
    copy_btn.click(None, text_out, None, js="(txt) => navigator.clipboard.writeText(txt)")

demo.launch()