"""Lynote AI Text Detector Lite — bilingual, dependency-light heuristic demo. The detector is transparent and CPU-only: it computes a small set of well-documented statistical signals for AI-generated prose (low burstiness, formulaic phrase density, repeated n-grams, uniform vocabulary) and combines them into a single probabilistic score. It needs no model download, starts instantly, and works on English, Chinese, and other languages. The score is a probabilistic signal, not proof of authorship. """ import math import re from collections import Counter import gradio as gr MAX_CHARS = 5_000 MIN_CHARS = 80 UTM_URL = ( "https://lynote.ai/ai-detector?utm_source=huggingface" "&utm_medium=space&utm_campaign=hf_launch&utm_content=text_detector" ) # High-confidence formulaic phrases (mirrors spaces/humanizer-lite). EN_PHRASES = [ "in today's rapidly evolving world", "in today's fast-paced world", "it is important to note that", "it is worth noting that", "serves as a testament to", "delve into", "leverage", "seamless", "robust", "game-changer", "groundbreaking", "cutting-edge", "state-of-the-art", "plays a crucial role", "in the realm of", "in conclusion", "moreover", "furthermore", ] ZH_PHRASES = [ "值得注意的是", "综上所述", "在当今快速发展的时代", "赋能", "助力", "降本增效", "闭环", "无缝", "总而言之", "由此可见", "发挥着重要作用", ] WEIGHTS = { "burstiness": 0.35, "formulaic_phrases": 0.25, "repetition": 0.20, "vocabulary_uniformity": 0.20, } def _words(text: str): latin = re.findall(r"[A-Za-z0-9]+", text) han = re.findall(r"[\u4e00-\u9fff]", text) return latin + han def _sentences(text: str): return [ s.strip() for s in re.split(r"[。！？!?；;\n]+|\.(?:\s+|$)", text) if s.strip() ] def _burstiness(text: str) -> float: """1.0 = uniform sentence rhythm (AI-like); 0.0 = bursty (human-like).""" lengths = [max(len(_words(s)), 1) for s in _sentences(text)] if len(lengths) < 3: return 0.5 mean = sum(lengths) / len(lengths) variance = sum((length - mean) ** 2 for length in lengths) / len(lengths) cv = math.sqrt(variance) / mean return 1.0 - min(cv / 0.75, 1.0) def _formulaic_phrases(text: str) -> float: """Hits of known AI clichés per 100 tokens, clamped to [0, 1].""" lowered = text.lower() hits = 0 for phrase in EN_PHRASES: hits += len(re.findall(re.escape(phrase), lowered)) for phrase in ZH_PHRASES: hits += text.count(phrase) per_hundred = hits * 100 / max(len(_words(text)), 1) return min(per_hundred / 2.0, 1.0) def _repetition(text: str) -> float: """Repeated 3-grams plus repeated sentence starters, scaled to [0, 1].""" tokens = _words(text) if len(tokens) < 12: return 0.5 grams = [tuple(tokens[i:i + 3]) for i in range(len(tokens) - 2)] counts = Counter(grams) total = len(grams) repeated = sum(count for count in counts.values() if count > 1) ngram_rate = repeated / total sentences = _sentences(text) starters = [tuple(_words(s)[:2]) for s in sentences if _words(s)] starter_counts = Counter(starters) starter_repeats = sum(count - 1 for count in starter_counts.values() if count > 1) starter_rate = starter_repeats / max(len(starters), 1) return min(ngram_rate * 2.5 + starter_rate, 1.0) def _vocabulary_uniformity(text: str) -> float: """1.0 = very repetitive vocabulary; 0.0 = highly diverse.""" tokens = _words(text) if len(tokens) < 8: return 0.5 ttr = len(set(tokens)) / len(tokens) return 1.0 - ttr def detect(text: str): original = (text or "").strip() if not original: return "Paste some text to run the detector.", {} if len(original) > MAX_CHARS: return f"Input is limited to {MAX_CHARS:,} characters in this demo.", {} if len(original) < MIN_CHARS: return ( f"Add at least {MIN_CHARS:,} characters for a meaningful signal " "(short texts are easy to misjudge).", {}, ) signals = { "burstiness": round(_burstiness(original), 3), "formulaic_phrases": round(_formulaic_phrases(original), 3), "repetition": round(_repetition(original), 3), "vocabulary_uniformity": round(_vocabulary_uniformity(original), 3), } probability = sum(signals[name] * weight for name, weight in WEIGHTS.items()) probability = min(max(probability, 0.0), 1.0) if probability < 0.35: band = "Weak AI-generated signal" elif probability < 0.65: band = "Uncertain / mixed signal" else: band = "Strong AI-generated signal" message = f""" ## {band} Estimated AI-generated score: **{probability:.1%}** This is a transparent statistical heuristic, not proof of origin. Human writing can be polished, and AI writing can be varied; very short, translated, or heavily edited texts are especially easy to misjudge. Do not use this demo alone for disciplinary, legal, or moderation decisions. [Try Lynote's full detection experience]({UTM_URL}) """ return message, signals DESCRIPTION = f""" This free, local demo estimates how much a text resembles AI-generated prose using four transparent signals—sentence-rhythm uniformity (burstiness), formulaic phrase density, repeated n-grams, and vocabulary uniformity. It is CPU-only, needs no model download, and works for English, Chinese, and other languages. It is a heuristic demo, not the full [Lynote](https://lynote.ai) production pipeline. """ EXAMPLES = [ [ "In today's rapidly evolving world, it is important to note that this " "robust solution serves as a testament to our commitment to innovation. " "Moreover, we leverage cutting-edge technology to deliver a seamless " "experience to our users. Furthermore, our team has built a " "comprehensive approach that plays a crucial role in improving " "efficiency. It is worth noting that this groundbreaking platform " "fosters collaboration and drives growth across the organization." ], [ "I walked the dog this morning and the sky was grey and low. Halfway " "through the park it started raining, which I hadn't expected, so we " "ran home. The dog seemed to enjoy it more than me. When we got back " "I made coffee and watched the rain run down the window for a while." ], [ "值得注意的是，在当今快速发展的时代，我们通过赋能团队来助力企业实现" "降本增效。综上所述，这一方案形成了完整的业务闭环，无缝连接各个环节，" "发挥着重要作用。由此可见，未来我们需要继续优化流程，从而为企业创造" "更大的价值。" ], [ "今天早上我带狗去公园，出门时天阴阴的。走到一半忽然下起雨，我们只好" "跑回家，狗倒是挺高兴，一路甩着尾巴。回来以后我煮了杯咖啡，坐在窗边" "看了会儿雨，觉得周末就这么过也挺好。" ], ] with gr.Blocks(title="Lynote AI Text Detector") as demo: gr.Markdown("# 🔍 Lynote AI Text Detector") gr.Markdown(DESCRIPTION) source = gr.Textbox( label="Text to analyze", lines=12, max_lines=20, placeholder=f"Paste {MIN_CHARS:,}-{MAX_CHARS:,} characters of text…", ) run = gr.Button("Analyze text", variant="primary") with gr.Row(): result = gr.Markdown() signals = gr.JSON(label="Signal breakdown") run.click(detect, inputs=source, outputs=[result, signals]) gr.Examples(examples=EXAMPLES, inputs=source) gr.Markdown( "**Privacy:** text is analyzed in memory and is not intentionally stored " "by this app. Hugging Face infrastructure remains subject to its platform " "policies." ) if __name__ == "__main__": demo.launch()