Update app.py

app.py

@@ -5,7 +5,28 @@ from scipy.spatial.distance import cosine
 import cv2
 import os
 
-print(tf.__version__)
+from tensorflow.keras.applications import resnet
+from tensorflow.keras import layers, Model
+
+def create_embedding_model():
+    base_cnn = resnet.ResNet50(weights="imagenet", input_shape=(200, 200, 3), include_top=False)
+
+    flatten = layers.Flatten()(base_cnn.output)
+    dense1 = layers.Dense(512, activation="relu")(flatten)
+    dense1 = layers.BatchNormalization()(dense1)
+    dense2 = layers.Dense(256, activation="relu")(dense1)
+    dense2 = layers.BatchNormalization()(dense2)
+    output = layers.Dense(256)(dense2)
+
+    embedding_model = Model(base_cnn.input, output, name="Embedding")
+
+    trainable = False
+    for layer in base_cnn.layers:
+        if layer.name == "conv5_block1_out":
+            trainable = True
+        layer.trainable = trainable
+
+    return embedding_model
 
 # K-mean Clustering
 from sklearn.cluster import KMeans
@@ -17,7 +38,9 @@ n_clusters = 5  # You can adjust this based on your data
 kmeans = KMeans(n_clusters=n_clusters)
 
 # Load the embedding model
-embedding_model = tf.keras.models.load_model('base_128.h5')
+# embedding_model = tf.keras.models.load_model('base_128.h5')
+embedding_model = create_embedding_model()
+embedding_model.load_weights('base_128.h5')
 
 # Database to store embeddings and user IDs
 user_embeddings = []