"""PII detection — server-side CPU inference.

LiquidAI/LFM2.5-Encoder-350M-PII-Detector (LFM2.5 encoder + token-classification head + hybrid regex
decode) runs on the Space's CPU. The browser POSTs {text} to /api/detect and
gets back char-anchored PII spans (type/domain/label) + a redacted string, then
highlights them. Nothing is downloaded to the visitor.

The model repo is private, so an HF_TOKEN Space secret with read access is
required to pull it.
"""

import os
import time

import torch
from flask import Flask, request, send_from_directory

import pii as P

APP_DIR = os.path.dirname(os.path.abspath(__file__))
# Match threads to the container's cgroup CPU quota, not the host core count.
_CPUS = P.effective_cpus()
torch.set_num_threads(_CPUS)
try:
    torch.set_num_interop_threads(1)
except RuntimeError:
    pass

app = Flask(__name__)

print(f"[boot] loading pii-detect on {_CPUS} CPU thread(s), f32…", flush=True)
MODEL = P.PiiDetector()
MODEL.detect("Warm up: email jane@example.com, SSN 123-45-6789.")   # warm the graph
print("[boot] detector ready", flush=True)


@app.route("/")
def index():
    return send_from_directory(APP_DIR, "index.html")


@app.route("/<path:name>")
def static_file(name):
    if name in ("style.css", "lliquid.gif"):
        return send_from_directory(APP_DIR, name)
    return ("not found", 404)


@app.route("/api/ready")
def ready():
    return {"ready": MODEL is not None}


@app.route("/api/detect", methods=["POST"])
def api_detect():
    data = request.get_json(silent=True) or {}
    text = (data.get("text") or "").replace("\r", "")
    if not text.strip():
        return {"spans": [], "redacted": "", "count": 0, "ms": 0}
    t0 = time.perf_counter()
    out = MODEL.detect(text)
    out["ms"] = round((time.perf_counter() - t0) * 1000)
    return out


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=int(os.environ.get("PORT", 7860)), threaded=True)