Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 import cv2
 import os
 from scipy.spatial.distance import cosine
-from tensorflow.keras.applications import resnet
+from tensorflow.keras.models import load_model
 from tensorflow.keras import layers, Model
 
 def create_embedding_model():
@@ -32,8 +32,15 @@ RECOGNITION_THRESHOLD = 0.1 # Adjust as needed
 
 # Preprocess the image
 def preprocess_image(image):
-    image = cv2.resize(image, (200, 200))  # Resize image to 200x200
-    image = tf.keras.applications.resnet50.preprocess_input(image)
+    # Resize image to match FaceNet input size (160x160)
+    image = cv2.resize(image, (160, 160))
+
+    # Normalize the image to a range of [-1, 1] as FaceNet might expect
+    image = image.astype('float32')
+    mean, std = image.mean(), image.std()
+    image = (image - mean) / std
+
+    # Expand dimensions to add batch size of 1
     return np.expand_dims(image, axis=0)
 
 # Generate embedding