Update app.py

app.py

@@ -23,6 +23,14 @@ from tensorflow.keras.layers import (
     TextVectorization,
 )
 
+MAX_CAPTION_LEN = 64
+MINIMUM_SENTENCE_LENGTH = 5
+IMG_HEIGHT = 299
+IMG_WIDTH = 299
+IMG_CHANNELS = 3
+ATTENTION_DIM = 512  # size of dense layer in Attention
+VOCAB_SIZE = 20000
+
 @st.cache_resource()
 def load_image_model():
     image_model=tf.keras.models.load_model('./image_caption_model.h5')
@@ -37,6 +45,15 @@ def load_decoder_model():
 def load_encoder_model():
     encoder=tf.keras.models.load_model('./encoder_model.h5')
     return encoder
+
+
+FEATURE_EXTRACTOR = tf.keras.applications.inception_resnet_v2.InceptionResNetV2(include_top=False, weights="imagenet")
+FEATURE_EXTRACTOR.trainable = False
+image_input = Input(shape=(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
+image_features = FEATURE_EXTRACTOR(image_input)
+x = Reshape((FEATURES_SHAPE[0] * FEATURES_SHAPE[1], FEATURES_SHAPE[2]))(image_features)
+encoder_output = Dense(ATTENTION_DIM, activation="relu")(x)
+encoder = tf.keras.Model(inputs=image_input, outputs=encoder_output)
     
 st.title(":blue[Nishant Guvvada's] :red[AI Journey]  Image Caption Generation")
 image = Image.open('./title.jpg')
@@ -48,13 +65,7 @@ st.write("""
 
 file = st.file_uploader("Upload any image and the model will try to provide a caption to it!", type= ['png', 'jpg'])
 
-MAX_CAPTION_LEN = 64
-MINIMUM_SENTENCE_LENGTH = 5
-IMG_HEIGHT = 299
-IMG_WIDTH = 299
-IMG_CHANNELS = 3
-ATTENTION_DIM = 512  # size of dense layer in Attention
-VOCAB_SIZE = 20000
+
 
 # We will override the default standardization of TextVectorization to preserve
 # "<>" characters, so we preserve the tokens for the <start> and <end>.