# import numpy as np
# import csv

# class Searcher:
#     def __init__(self, indexPath):
#         # store our index path
#         self.indexPath = indexPath


#     def chi2_distance(self, histA, histB, eps = 1e-10):
#         # compute the chi-squared distance
#         d = 0.5 * np.sum([((a - b) ** 2) / (a + b + eps)
#             for (a, b) in zip(histA, histB)])
#         # return the chi-squared distance
#         return d

#     def search(self, queryFeatures, limit = 3):
#         # initialize our dictionary of results
#         results = {}
#         # open the index file for reading
#         with open(self.indexPath) as f:
#             # initialize the CSV reader
#             reader = csv.reader(f)
#             # loop over the rows in the index
#             for row in reader:
#                 # parse out the image ID and features, then compute the
#                 # chi-squared distance between the features in our index
#                 # and our query features
#                 features = [float(x) for x in row[1:]]
#                 d = self.chi2_distance(features, queryFeatures)
#                 # now that we have the distance between the two feature
#                 # vectors, we can udpate the results dictionary -- the
#                 # key is the current image ID in the index and the
#                 # value is the distance we just computed, representing
#                 # how 'similar' the image in the index is to our query
#                 results[row[0]] = d

#             # close the reader
#             f.close()

#         # sort our results, so that the smaller distances (i.e. the
#         # more relevant images are at the front of the list)
#         path = "home/user/app/static/images/"
#         results = sorted([(v, f"{path}{k}") for (k, v) in results.items()])

#         # return our (limited) results
#         return results[:limit]