Create app.py

app.py

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+# FastAPI Space: username + image -> decision, score, threshold, jwt
+import os, io, json, time
+import numpy as np
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+import jwt
+
+# ---- config ----
+MODELS_DIR = os.environ.get('MODELS_DIR', 'models')           # put gallery/labels/threshold here
+JWT_SECRET = os.environ.get('PORTAL_SECRET', 'change-me')     # set a real secret in Space settings
+JWT_EXP_SECS = int(os.environ.get('JWT_EXP_SECS', '600'))     # 10 min
+
+# ---- load artifacts ----
+import json as _json
+labels = _json.load(open(f"{MODELS_DIR}/labels.json","r",encoding="utf-8"))
+gallery = np.load(f"{MODELS_DIR}/gallery_mean.npy")
+thr = _json.load(open(f"{MODELS_DIR}/threshold.json","r"))["cosine_threshold"]
+
+# ---- embedder ----
+class FaceEmbedder:
+    def __init__(self, provider='CPUExecutionProvider'):
+        import insightface
+        from insightface.app import FaceAnalysis
+        self.fa = FaceAnalysis(name='buffalo_l', providers=[provider])
+        self.fa.prepare(ctx_id=-1, det_size=(640,640))
+    def __call__(self, img_rgb: np.ndarray):
+        faces = self.fa.get(img_rgb)
+        if not faces: return None
+        f = max(faces, key=lambda z: (z.bbox[2]-z.bbox[0])*(z.bbox[3]-z.bbox[1]))
+        return f.normed_embedding.astype('float32')
+
+EMB = FaceEmbedder()
+
+# ---- api ----
+app = FastAPI(title="Access Gate")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"], allow_credentials=True,
+    allow_methods=["*"], allow_headers=["*"]
+)
+
+def issue_token(username: str):
+    payload = {"sub": username, "iat": int(time.time()), "exp": int(time.time()) + JWT_EXP_SECS}
+    return jwt.encode(payload, JWT_SECRET, algorithm="HS256")
+
+@app.get("/labels")
+def get_labels():
+    return {"labels": labels}
+
+@app.post("/verify")
+async def verify(username: str = Form(...), image: UploadFile = File(...)):
+    # read image
+    arr = np.array(Image.open(io.BytesIO(await image.read())).convert("RGB"))
+    # checks
+    if username not in labels:
+        return {"decision": "Not Found", "score": 0.0, "threshold": thr, "reason": "Unknown user"}
+
+    emb = EMB(arr)
+    if emb is None:
+        return {"decision": "Invalid", "score": 0.0, "threshold": thr, "reason": "No face detected"}
+
+    idx = labels.index(username)
+    score = float(emb @ gallery[idx])
+    decision = "Allow" if score >= thr else "Deny"
+    token = issue_token(username) if decision == "Allow" else ""
+
+    return {
+        "decision": decision,
+        "score": score,
+        "threshold": thr,
+        "reason": "",
+        "token": token
+    }