Update app.py

app.py

@@ -1,10 +1,4 @@
-"""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
-"""
+"""Orify Text Detector API – FastAPI (CPU‑only)"""
 
 from __future__ import annotations
 import os, re, html
@@ -18,91 +12,98 @@ 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 jose import jwt, JWTError
 from pydantic import BaseModel, Field
 
-# ─── torch.compile shim (HF CPU runtime) ───────────────────────────────
+# ----------------------------------------------------------------------------
+# Torch compile shim (CPU runtime)
+# ----------------------------------------------------------------------------
 if hasattr(torch, "compile"):
-    torch.compile = (lambda m=None,*a,**kw: m if callable(m) else (lambda f: f))  # type: ignore
+    torch.compile = (lambda m=None, *_, **__: m if callable(m) else (lambda f: f))  # type: ignore
     os.environ["TORCHINDUCTOR_DISABLED"] = "1"
 
-# ─── model / weight config ─────────────────────────────────────────────
+# ----------------------------------------------------------------------------
+# Model 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"}
+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"
+TOKEN_KW    = {"trust_remote_code": True}
+
+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 ───────────────────────────────────────────────────────
+# ----------------------------------------------------------------------------
+# JWT helpers
+# ----------------------------------------------------------------------------
 SECRET_KEY = os.getenv("SECRET_KEY")
 if not SECRET_KEY:
-    raise RuntimeError("Set the SECRET_KEY env-var in Space ➜ Settings ➜ Secrets")
+    raise RuntimeError("SECRET_KEY env‑var not set")
 
-ALGORITHM               = "HS256"
-ACCESS_TOKEN_EXPIRE_HOURS = 24
+ALGORITHM = "HS256"
+EXP_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)
+def _jwt_create(data: dict) -> str:
+    payload = data | {"exp": datetime.utcnow() + timedelta(hours=EXP_HOURS)}
+    return jwt.encode(payload, SECRET_KEY, algorithm=ALGORITHM)
 
-async def _current_user(token: str = Depends(oauth2_scheme)):
+def _jwt_verify(token: str = Depends(oauth2_scheme)) -> str:
     try:
-        payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
-        return payload.get("sub") or "anonymous"
+        return jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])["sub"]
     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()}
+# ----------------------------------------------------------------------------
+# Load ensemble once
+# ----------------------------------------------------------------------------
+print("🔄 Downloading weights…", flush=True)
+local = {k: hf_hub_download(WEIGHT_REPO, v, resume_download=True) for k, v in FILE_MAP.items()}
 
-print("🧩 Initialising models …", flush=True)
-_tok = AutoTokenizer.from_pretrained(BASE_MODEL)
+print("🧩 Initialising models…", flush=True)
+_tok = AutoTokenizer.from_pretrained(BASE_MODEL, **TOKEN_KW)
 _models: List[AutoModelForSequenceClassification] = []
-for p in local_paths.values():
-    m = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL,
-                                                           num_labels=NUM_LABELS)
+for p in local.values():
+    m = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=NUM_LABELS, **TOKEN_KW)
     m.load_state_dict(torch.load(p, map_location=DEVICE))
     m.to(DEVICE).eval()
     _models.append(m)
-print("✅  Ensemble ready")
+print("✅ Ensemble ready")
+
+# ----------------------------------------------------------------------------
+# Helpers
+# ----------------------------------------------------------------------------
 
-# ─── 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 _tidy(t: str) -> str:
+    t = t.replace("\r\n", "\n").replace("\r", "\n")
+    t = re.sub(r"\n\s*\n+", "\n\n", t)
+    t = re.sub(r"[ \t]+", " ", t)
+    t = re.sub(r"(\w+)-\n(\w+)", r"\1\2", t)
+    t = re.sub(r"(?<!\n)\n(?!\n)", " ", t)
+    return t.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
+    ai_probs = probs.clone(); ai_probs[24] = 0
+    ai, human = ai_probs.sum().item() * 100, 0.0
     human = 100 - ai
-    top3  = [LABELS[i] for i in torch.topk(ai_probs, 3).indices.tolist()]
+    top3 = [LABELS[i] for i in torch.topk(ai_probs, 3).indices.tolist()]
     return human, ai, top3
 
-# ─── Pydantic schemas ─────────────────────────────────────────────────
-from pydantic import BaseModel, Field
+# ----------------------------------------------------------------------------
+# Schemas
+# ----------------------------------------------------------------------------
 class Token(BaseModel):
     access_token: str
     token_type: str = "bearer"
@@ -111,52 +112,58 @@ class AnalyseIn(BaseModel):
     text: str = Field(..., min_length=1)
 
 class Line(BaseModel):
-    text: str; ai: float; human: float; top3: List[str]; reason: str
+    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 ───────────────────────────────────────────────────────
+    verdict: str
+    confidence: float
+    ai_avg: float
+    human_avg: float
+    per_line: List[Line]
+    highlight_html: str
+
+# ----------------------------------------------------------------------------
+# FastAPI
+# ----------------------------------------------------------------------------
 app = FastAPI(title="Orify Text Detector API", version="1.0.0")
-app.add_middleware(CORSMiddleware,
-                   allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
+app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
 
-@app.post("/token", response_model=Token, summary="Obtain JWT (demo accepts any creds)")
+@app.post("/token", response_model=Token)
 async def login(form: OAuth2PasswordRequestForm = Depends()):
-    return Token(access_token=_create_token({"sub": form.username}))
+    return Token(access_token=_jwt_create({"sub": form.username}))
 
-@app.post("/analyse", response_model=AnalyseOut, summary="Detect AI-generated text")
-async def analyse(data: AnalyseIn, _user=Depends(_current_user)):
+@app.post("/analyse", response_model=AnalyseOut)
+async def analyse(data: AnalyseIn, _user=Depends(_jwt_verify)):
     lines = _tidy(data.text).split("\n")
-    html_parts, per_line = [], []
+    per_line, html_parts = [], []
     h_sum = ai_sum = n = 0.0
 
     for ln in lines:
         if not ln.strip():
-            html_parts.append("<br>")
-            continue
+            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}%)")
+        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)
+    confidence = max(ai_avg, human_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)
+    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 ───────────────────────────