# signature_verification/signature_siamese.py
import cv2
import numpy as np
import tensorflow as tf
from config import SIA_MODEL_PATH, SIG_THRESH

class SignatureSiamese:
    def __init__(self, model_path: str = SIA_MODEL_PATH):
        try:
            self.interp = tf.lite.Interpreter(model_path=model_path)
            self.interp.allocate_tensors()
            ids = self.interp.get_input_details()
            self.in1, self.in2 = ids[0]['index'], ids[1]['index']
            self.out_idx = self.interp.get_output_details()[0]['index']
            print(f"✅ Loaded TFLite Siamese model from {model_path}")
        except Exception as e:
            self.interp = None
            print(f"❌ Failed to load Siamese model from {model_path}: {e}")

    def _prep(self, img: np.ndarray) -> np.ndarray:
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) if img.ndim == 3 else img
        r = cv2.resize(gray, (224, 224))
        return r.reshape(1, 224, 224, 1).astype(np.float32) / 255.0

    def verify(self, img1: np.ndarray, img2: np.ndarray) -> tuple[bool, float]:
        if not self.interp:
            return False, float('inf')
        inp1, inp2 = self._prep(img1), self._prep(img2)
        self.interp.set_tensor(self.in1, inp1)
        self.interp.set_tensor(self.in2, inp2)
        self.interp.invoke()
        diff = self.interp.get_tensor(self.out_idx)
        dist = np.linalg.norm(diff)
        return dist < SIG_THRESH, dist