Delete app.py

app.py

@@ -1,42 +0,0 @@
-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'))