Update app.py

app.py

@@ -8,28 +8,20 @@ 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
-
+    # Load the model architecture
+    facenet_model = load_model('facenet_keras.h5', compile=False)
+    
+    # Define the embedding model using FaceNet
+    embedding_model = Model(inputs=facenet_model.input, 
+                            outputs=facenet_model.layers[-2].output, 
+                            name="Embedding")
+    
+    # Here, you don't need to set layers as trainable since it's already done in your training script
     return embedding_model
 
 # Load the embedding model
 embedding_model = create_embedding_model()
-embedding_model.load_weights('v1_embedding.h5')
+embedding_model.load_weights('facenet_siamese_embedding.h5')
 
 # Database to store embeddings and user IDs
 user_embeddings = []
@@ -76,7 +68,7 @@ def recognize_user(image):
         print(f"Min distance: {closest_distance}")  # Debug: Print minimum distance
 
         if closest_distance <= RECOGNITION_THRESHOLD:
-            return f"Recognized User: {closest_user_id}}"
+            return f"Recognized User: {closest_user_id}"
         else:
             return f"User not recognized. Closest Distance: {closest_distance}"
     except Exception as e:
@@ -104,4 +96,4 @@ def main():
     demo.launch(share=True)
 
 if __name__ == "__main__":
-    main()
+    main()