init

app.py

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+"""
+  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
+
+# ── Configuration ────────────────────────────────────────────────────────
+DEVICE        = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+WEIGHT_REPO   = "Sleepyriizi/Orify-Text-Detection-Weights"
+
+FILE_MAP = {                   # local alias   -> file in repo
+    "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()

requirements.txt

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+gradio
+torch
+git+https://github.com/huggingface/transformers