app.py

"""
  Orify Text Detector  – Space edition (Zero-GPU ready)

  • Three ModernBERT-base checkpoints (soft-vote)
  • Per-line colour coding, probability tool-tips, top-3 AI model hints
  • Everything fetched automatically from the weight repo and cached
"""

# ── Imports ──────────────────────────────────────────────────────────────
from pathlib import Path
import re, torch, gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from huggingface_hub import hf_hub_download
import spaces
import os, types                 # add `types`

# ──────────────────  robust torch.compile shim  ─────────────────────────
if hasattr(torch, "compile"):
    def _no_compile(model: types.Any = None, *args, **kwargs):
        """
        1. If called as torch.compile(model, …)  → just return the model.
        2. If called as torch.compile(**kw)      → return a decorator that
           immediately gives back the class / fn it decorates.
        """
        if callable(model):      # pattern 1
            return model
        # pattern 2  (used by ModernBERT via @torch.compile(...))
        def decorator(fn):
            return fn
        return decorator

    torch.compile = _no_compile          # monkey-patch
    os.environ["TORCHINDUCTOR_DISABLED"] = "1"

# (everything below is unchanged)
DEVICE        = torch.device("cuda" if torch.cuda.is_available() else "cpu")
WEIGHT_REPO   = "Sleepyriizi/Orify-Text-Detection-Weights"
FILE_MAP = {"ensamble_1":"ensamble_1",
            "ensamble_2.bin":"ensamble_2.bin",
            "ensamble_3":"ensamble_3"}
BASE_MODEL_NAME = "answerdotai/ModernBERT-base"
NUM_LABELS      = 41

LABELS = {            # id → friendly label (unchanged)
    0: "13B", 1: "30B",  2: "65B",  3: "7B",           4: "GLM130B",
    5: "bloom_7b", 6: "bloomz", 7: "cohere", 8: "davinci",
    9: "dolly",   10: "dolly-v2-12b", 11: "flan_t5_base",
    12: "flan_t5_large", 13: "flan_t5_small", 14: "flan_t5_xl",
    15: "flan_t5_xxl", 16: "gemma-7b-it", 17: "gemma2-9b-it",
    18: "gpt-3.5-turbo", 19: "gpt-35", 20: "gpt-4",
    21: "gpt-4o", 22: "gpt-j", 23: "gpt-neox", 24: "human",
    25: "llama3-70b", 26: "llama3-8b", 27: "mixtral-8x7b",
    28: "opt-1.3b", 29: "opt-125m", 30: "opt-13b",
    31: "opt-2.7b", 32: "opt-30b", 33: "opt-350m",
    34: "opt-6.7b", 35: "opt-iml-30b", 36: "opt-iml-max-1.3b",
    37: "t0-11b", 38: "t0-3b", 39: "text-davinci-002", 40: "text-davinci-003"
}

# ── CSS (kept identical) ────────────────────────────────────────────────
CSS = Path(__file__).with_name("style.css").read_text() if Path(__file__).with_name("style.css").exists() else """
:root{--clr-ai:#ff4d4f;--clr-human:#52c41a;--border:2px solid var(--clr-ai);--radius:10px}
body{font-family:'Roboto Mono',monospace;margin:0 auto;max-width:900px;padding:32px}
textarea,.output-box{width:100%;box-sizing:border-box;padding:16px;font-size:1rem;border:var(--border);border-radius:var(--radius)}
.output-box{min-height:160px}.ai-line{background:rgba(255,77,79,.12);padding:2px 4px;border-radius:4px}
.human-line{background:rgba(82,196,26,.12);padding:2px 4px;border-radius:4px}
.prob-tooltip{cursor:help;border-bottom:1px dotted currentColor}
"""

# ── Model loading (download once, then cached) ───────────────────────────
print("🔄 Downloading weights …")
local_paths = {alias: hf_hub_download(WEIGHT_REPO, fname, resume_download=True)
               for alias, fname in FILE_MAP.items()}

print("🧩 Loading tokenizer & models …")
tokeniser = AutoTokenizer.from_pretrained(BASE_MODEL_NAME)

models = []
for alias, path in local_paths.items():
    net = AutoModelForSequenceClassification.from_pretrained(
              BASE_MODEL_NAME, num_labels=NUM_LABELS)
    net.load_state_dict(torch.load(path, map_location=DEVICE))
    net.to(DEVICE).eval()
    models.append(net)

# ── Helpers ──────────────────────────────────────────────────────────────
def tidy(txt: str) -> str:
    txt = txt.replace("\r\n", "\n").replace("\r", "\n")
    txt = re.sub(r"\n\s*\n+", "\n\n", txt)
    txt = re.sub(r"[ \t]+", " ", txt)
    txt = re.sub(r"(\w+)-\n(\w+)", r"\1\2", txt)
    txt = re.sub(r"(?<!\n)\n(?!\n)", " ", txt)
    return txt.strip()

def infer(segment: str):
    """Return (human%, ai%, [top-3 ai model names])."""
    inputs = tokeniser(segment, return_tensors="pt", truncation=True,
                       padding=True).to(DEVICE)
    with torch.no_grad():
        probs = torch.stack([
            torch.softmax(m(**inputs).logits, dim=1) for m in models
        ]).mean(dim=0)[0]

    ai_probs = probs.clone(); ai_probs[24] = 0        # null out human idx
    ai_score   = ai_probs.sum().item() * 100
    human_score = 100 - ai_score
    top3 = torch.topk(ai_probs, 3).indices.tolist()
    top3_names = [LABELS[i] for i in top3]
    return human_score, ai_score, top3_names

# ── Inference + explanation ──────────────────────────────────────────────
@spaces.GPU
def analyse(text: str):
    if not text.strip():
        return "✏️ Please paste or type some text to analyse…"

    lines = tidy(text).split("\n")
    highlighted, h_tot, ai_tot, n = [], 0.0, 0.0, 0

    for ln in lines:
        if not ln.strip():
            highlighted.append("<br>")
            continue
        n += 1
        h, ai, top3 = infer(ln)
        h_tot += h; ai_tot += ai
        tooltip = (f"AI {ai:.2f}% • Top-3: {', '.join(top3)}"
                   if ai > h else f"Human {h:.2f}%")
        cls = "ai-line" if ai > h else "human-line"
        span = (f"<span class='{cls} prob-tooltip' title='{tooltip}'>"
                f"{gr.utils.sanitize_html(ln)}</span>")
        highlighted.append(span)

    verdict = (f"<p><strong>Overall verdict:</strong> "
               f"<span class='human-line' style='padding:4px 8px;'>"
               f"Human-written {h_tot/n:.2f}%</span>"
               if h_tot >= ai_tot else
               f"<p><strong>Overall verdict:</strong> "
               f"<span class='ai-line' style='padding:4px 8px;'>"
               f"AI-generated {ai_tot/n:.2f}%</span>")
    return verdict + "<hr>" + "<br>".join(highlighted)

# ── Interface ────────────────────────────────────────────────────────────
with gr.Blocks(css=CSS, title="Orify Text Detector") as demo:
    gr.Markdown("""
    ### Orify Text Detector  
    Paste any English text and press **Analyse**.  
    <span class='human-line'>Green</span> = human | <span class='ai-line'>Red</span> = AI.  
    Hover a line to see confidence and the top-3 AI models it resembles.
    """)
    inp = gr.Textbox(lines=8, placeholder="Paste text here …",
                     elem_classes=["input-area"])
    out = gr.HTML("", elem_classes=["output-box"])
    gr.Button("Analyse").click(analyse, inp, out)
    gr.Markdown("<sub>Powered by ModernBERT + Orify Ensemble © 2025</sub>")

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