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| # 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() |