Add application file

app.py

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+"""Orify Text Detector API – FastAPI + JWT (CPU-only, HF Zero-GPU)
+
+Endpoints
+---------
+POST /token    → returns JWT  (OAuth2 password-grant, demo-auth)
+POST /analyse  → protected; returns verdict JSON + HTML highlights
+"""
+
+from __future__ import annotations
+import os, re, html
+from datetime import datetime, timedelta
+from typing import List
+
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from huggingface_hub import hf_hub_download
+
+from fastapi import FastAPI, HTTPException, Depends
+from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
+from fastapi.middleware.cors import CORSMiddleware
+from jose import JWTError, jwt
+from pydantic import BaseModel, Field
+
+# ─── torch.compile shim (HF CPU runtime) ───────────────────────────────
+if hasattr(torch, "compile"):
+    torch.compile = (lambda m=None,*a,**kw: m if callable(m) else (lambda f: f))  # type: ignore
+    os.environ["TORCHINDUCTOR_DISABLED"] = "1"
+
+# ─── model / weight config ─────────────────────────────────────────────
+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  = "answerdotai/ModernBERT-base"
+NUM_LABELS  = 41
+
+LABELS = {i:n for i,n in enumerate([
+    "13B","30B","65B","7B","GLM130B","bloom_7b","bloomz","cohere","davinci",
+    "dolly","dolly-v2-12b","flan_t5_base","flan_t5_large","flan_t5_small",
+    "flan_t5_xl","flan_t5_xxl","gemma-7b-it","gemma2-9b-it","gpt-3.5-turbo",
+    "gpt-35","gpt-4","gpt-4o","gpt-j","gpt-neox","human","llama3-70b",
+    "llama3-8b","mixtral-8x7b","opt-1.3b","opt-125m","opt-13b","opt-2.7b",
+    "opt-30b","opt-350m","opt-6.7b","opt-iml-30b","opt-iml-max-1.3b",
+    "t0-11b","t0-3b","text-davinci-002","text-davinci-003"
+])}
+
+# ─── JWT helpers ───────────────────────────────────────────────────────
+SECRET_KEY = os.getenv("SECRET_KEY")
+if not SECRET_KEY:
+    raise RuntimeError("Set the SECRET_KEY env-var in Space ➜ Settings ➜ Secrets")
+
+ALGORITHM               = "HS256"
+ACCESS_TOKEN_EXPIRE_HOURS = 24
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+def _create_token(data: dict, exp_hours: int = ACCESS_TOKEN_EXPIRE_HOURS) -> str:
+    to_encode = data.copy()
+    to_encode["exp"] = datetime.utcnow() + timedelta(hours=exp_hours)
+    return jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
+
+async def _current_user(token: str = Depends(oauth2_scheme)):
+    try:
+        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
+        return payload.get("sub") or "anonymous"
+    except JWTError:
+        raise HTTPException(401, "Invalid or expired token")
+
+# ─── load ensemble once ────────────────────────────────────────────────
+print("🔄 Downloading weights …", flush=True)
+local_paths = {k:hf_hub_download(WEIGHT_REPO,f,resume_download=True)
+               for k,f in FILE_MAP.items()}
+
+print("🧩 Initialising models …", flush=True)
+_tok = AutoTokenizer.from_pretrained(BASE_MODEL)
+_models: List[AutoModelForSequenceClassification] = []
+for p in local_paths.values():
+    m = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL,
+                                                           num_labels=NUM_LABELS)
+    m.load_state_dict(torch.load(p, map_location=DEVICE))
+    m.to(DEVICE).eval()
+    _models.append(m)
+print("✅  Ensemble ready")
+
+# ─── tiny helpers ──────────────────────────────────────────────────────
+def _tidy(text: str) -> str:
+    text = text.replace("\r\n", "\n").replace("\r", "\n")
+    text = re.sub(r"\n\s*\n+", "\n\n", text)
+    text = re.sub(r"[ \t]+", " ", text)
+    text = re.sub(r"(\w+)-\n(\w+)", r"\1\2", text)
+    text = re.sub(r"(?<!\n)\n(?!\n)", " ", text)
+    return text.strip()
+
+def _infer(seg: str):
+    inp = _tok(seg, return_tensors="pt", truncation=True, padding=True).to(DEVICE)
+    with torch.no_grad():
+        probs = torch.stack([torch.softmax(m(**inp).logits, dim=1) for m in _models]).mean(0)[0]
+    ai_probs = probs.clone(); ai_probs[24] = 0    # drop explicit “human” label
+    ai    = ai_probs.sum().item()*100
+    human = 100 - ai
+    top3  = [LABELS[i] for i in torch.topk(ai_probs, 3).indices.tolist()]
+    return human, ai, top3
+
+# ─── Pydantic schemas ─────────────────────────────────────────────────
+from pydantic import BaseModel, Field
+class Token(BaseModel):
+    access_token: str
+    token_type: str = "bearer"
+
+class AnalyseIn(BaseModel):
+    text: str = Field(..., min_length=1)
+
+class Line(BaseModel):
+    text: str; ai: float; human: float; top3: List[str]; reason: str
+
+class AnalyseOut(BaseModel):
+    verdict: str; confidence: float; ai_avg: float; human_avg: float
+    per_line: List[Line]; highlight_html: str
+
+# ─── FastAPI app ───────────────────────────────────────────────────────
+app = FastAPI(title="Orify Text Detector API", version="1.0.0")
+app.add_middleware(CORSMiddleware,
+                   allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
+
+@app.post("/token", response_model=Token, summary="Obtain JWT (demo accepts any creds)")
+async def login(form: OAuth2PasswordRequestForm = Depends()):
+    return Token(access_token=_create_token({"sub": form.username}))
+
+@app.post("/analyse", response_model=AnalyseOut, summary="Detect AI-generated text")
+async def analyse(data: AnalyseIn, _user=Depends(_current_user)):
+    lines = _tidy(data.text).split("\n")
+    html_parts, per_line = [], []
+    h_sum = ai_sum = n = 0.0
+
+    for ln in lines:
+        if not ln.strip():
+            html_parts.append("<br>")
+            continue
+        n += 1
+        human, ai, top3 = _infer(ln)
+        h_sum += human; ai_sum += ai
+        cls = "ai-line" if ai > human else "human-line"
+        tip = f"AI {ai:.2f}% – Top-3: {', '.join(top3)}" if ai > human else f"Human {human:.2f}%"
+        html_parts.append(f"<span class='{cls} prob-tooltip' title='{tip}'>{html.escape(ln)}</span>")
+        reason = (f"High AI likelihood ({ai:.1f}%) – fingerprint ≈ {top3[0]}"
+                  if ai > human else f"Lexical variety suggests human ({human:.1f}%)")
+        per_line.append(Line(text=ln, ai=ai, human=human, top3=top3, reason=reason))
+
+    human_avg = h_sum / n if n else 0
+    ai_avg    = ai_sum / n if n else 0
+    verdict   = "AI-generated" if ai_avg > human_avg else "Human-written"
+    confidence = max(human_avg, ai_avg)
+    badge = (f"<span class='ai-line' style='padding:6px 10px;font-weight:bold'>AI-generated {ai_avg:.2f}%</span>"
+             if verdict == "AI-generated" else
+             f"<span class='human-line' style='padding:6px 10px;font-weight:bold'>Human-written {human_avg:.2f}%</span>")
+    highlight_html = f"<h3>{badge}</h3><hr>" + "<br>".join(html_parts)
+
+    return AnalyseOut(verdict=verdict, confidence=confidence,
+                      ai_avg=ai_avg, human_avg=human_avg,
+                      per_line=per_line, highlight_html=highlight_html)
+
+# ────── local dev:  uvicorn app:app --reload ───────────────────────────

requirements.txt

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+fastapi
+uvicorn[standard]
+torch==2.2.2            # pin to the CPU build you tested with
+transformers>=4.40
+huggingface_hub>=0.23
+python-jose[cryptography]
+pydantic