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app.py

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+import numpy as np
+import tensorflow
+
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.layers import GlobalMaxPooling2D
+from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input
+from numpy.linalg import norm
+import os
+from tqdm import tqdm
+import pickle
+
+model = ResNet50(weights="imagenet", include_top=False,input_shape=(224,224,3))
+model.trainable=False
+
+model1 = tensorflow.keras.Sequential([
+    model,
+    GlobalMaxPooling2D()
+])
+
+def  extract_features(img_path,model):
+    img=image.load_img(img_path,target_size = (224,224))
+    image_array = image.img_to_array(img)
+    expanded_image_array = np.expand_dims(image_array,axis=0)
+    processed_image = preprocess_input(expanded_image_array)
+    result = model.predict(processed_image).flatten()
+    normalized_result=result/norm(result)
+    return  normalized_result
+
+filenames =[]
+
+for file in os.listdir('images'):
+    filenames.append(os.path.join('images',file))
+
+feature_list = []
+
+for i in tqdm(filenames):
+    feature_list.append(extract_features(i,model1))
+
+print(np.array(feature_list).shape)
+
+pickle.dump(feature_list,open('embeddings.pkl','wb'))
+pickle.dump(filenames,open('filenames.pkl','wb'))