Delete test.py

test.py

@@ -1,48 +0,0 @@
-import pickle
-import tensorflow
-import numpy as np
-from numpy.linalg import norm
-from tensorflow.keras.preprocessing import image
-from tensorflow.keras.layers import GlobalMaxPooling2D
-from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
-from sklearn.neighbors import NearestNeighbors
-import cv2
-
-# Load the precomputed feature vectors and filenames from pickle files
-feature_list = np.array(pickle.load(open('embeddings.pkl', 'rb')))
-filenames = pickle.load(open('filenames.pkl', 'rb'))
-
-# Load ResNet50 model without the top layer for feature extraction
-model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-model.trainable = False
-
-# Create a Sequential model with ResNet50 and GlobalMaxPooling2D layers
-model = tensorflow.keras.Sequential([
-    model,
-    GlobalMaxPooling2D()
-])
-
-# Load and preprocess the query image
-img = image.load_img('sample/khade.jpg', target_size=(224, 224))
-img_array = image.img_to_array(img)
-expanded_img_array = np.expand_dims(img_array, axis=0)
-preprocessed_img = preprocess_input(expanded_img_array)
-
-# Extract features from the query image and normalize
-result = model.predict(preprocessed_img).flatten()
-normalized_result = result / norm(result)
-
-# Initialize NearestNeighbors model and fit with the feature vectors
-neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
-neighbors.fit(feature_list)
-
-# Find the nearest neighbors (excluding itself)
-distances, indices = neighbors.kneighbors([normalized_result])
-
-print(indices)  # Print the indices of nearest neighbors
-
-# Display the nearest neighbor images
-for file in indices[0][0:5]:
-    temp_img = cv2.imread(filenames[file])
-    cv2.imshow('output', cv2.resize(temp_img, (312, 312)))
-    cv2.waitKey(0)