"""
Bassma v2 inference endpoint — FastAPI on HuggingFace Spaces.

Pure model server. The Telegram bot lives on Vercel; that side calls
this /verify endpoint with a shared API key.

Endpoints:
  GET  /        — health check (also used by keep-alive cron)
  POST /verify  — JSON API for the Next.js frontend AND Vercel bot route

Env (set as Space Secrets in the HF UI):
  HF_TOKEN        — read token to pull the private model at startup
  BASSMA_API_KEY  — shared secret with anything that calls /verify
                    (frontend, Vercel /api/telegram, etc.)
"""

import os

import numpy as np
from fastapi import FastAPI, Header, HTTPException
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer

MODEL_ID = "ziadabdullah/bassma-v1"

HF_TOKEN = os.environ.get("HF_TOKEN")
API_KEY = os.environ.get("BASSMA_API_KEY")

print("Loading model...", flush=True)
model = SentenceTransformer(MODEL_ID, token=HF_TOKEN)
print("Model loaded.", flush=True)


def normalize(text: str) -> str:
    return (
        text.replace("آ", "ا").replace("أ", "ا").replace("إ", "ا")
        .replace("ٱ", "ا").replace("ـ", "").strip()
    )


class VerifyRequest(BaseModel):
    text_a: str
    text_b: str


class VerifyResponse(BaseModel):
    similarity: float


app = FastAPI(title="Bassma v2")


@app.get("/")
def health():
    return {"status": "ok", "model": MODEL_ID}


@app.post("/verify", response_model=VerifyResponse)
def verify(req: VerifyRequest, x_api_key: str = Header(default="")):
    if API_KEY and x_api_key != API_KEY:
        raise HTTPException(status_code=401, detail="invalid api key")
    if len(req.text_a.strip()) < 50 or len(req.text_b.strip()) < 50:
        raise HTTPException(status_code=400, detail="texts too short")
    embs = model.encode(
        [normalize(req.text_a), normalize(req.text_b)],
        normalize_embeddings=True, convert_to_numpy=True,
    )
    return VerifyResponse(similarity=float(np.dot(embs[0], embs[1])))