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from sklearn.feature_extraction import DictVectorizer
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from sklearn.feature_extraction.text import TfidfTransformer
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from sklearn.metrics.pairwise import linear_kernel
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from sklearn.metrics.pairwise import cosine_similarity
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# Load Index
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idx_file = 'myphotoindex.dvect'
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d_index = pickle.load(open(idx_file, 'r'))
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indices = np.array(d_index.keys())
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# Load Model
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tfidf = pickle.load(open('tfidf_model.pkl'))
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# Load features
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features = pickle.load(open('features.pkl'))
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### WEB ###
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from flask import Flask
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from flask import request
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from flask.ext.cors import CORS, cross_origin
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app = Flask(__name__)
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cors = CORS(app, resources={r"/*": {"origins": "*"}})
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# stop words
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stop_words = [u'i', u'me', u'my', u'myself', u'we', u'our', u'ours', u'ourselves', u'you', u'your',
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u'yours', u'yourself', u'yourselves', u'he', u'him', u'his', u'himself', u'she', u'her',
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u'hers', u'herself', u'it', u'its', u'itself', u'they', u'them', u'their', u'theirs',
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u'themselves', u'what', u'which', u'who', u'whom', u'this', u'that', u'these', u'those',
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u'am', u'is', u'are', u'was', u'were', u'be', u'been', u'being', u'have', u'has', u'had',
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u'having', u'do', u'does', u'did', u'doing', u'a', u'an', u'the', u'and', u'but', u'if',
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u'or', u'because', u'as', u'until', u'while', u'of', u'at', u'by', u'for', u'with',
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u'about', u'against', u'between', u'into', u'through', u'during', u'before', u'after',
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u'above', u'below', u'to', u'from', u'up', u'down', u'in', u'out', u'on', u'off', u'over',
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u'under', u'again', u'further', u'then', u'once', u'here', u'there', u'when', u'where',
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u'why', u'how', u'all', u'any', u'both', u'each', u'few', u'more', u'most', u'other',
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u'some', u'such', u'no', u'nor', u'not', u'only', u'own', u'same', u'so', u'than', u'too',
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u'very', u's', u't', u'can', u'will', u'just', u'don', u'should', u'now', u'd', u'll', u'm',
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u'o', u're', u've', u'y', u'ain', u'aren', u'couldn', u'didn', u'doesn', u'hadn', u'hasn',
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u'haven', u'isn', u'ma', u'mightn', u'mustn', u'needn', u'shan', u'shouldn', u'wasn',
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u'weren', u'won', u'wouldn', 'people', 'image', 'images']
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########## APP ENDPOINTS ################################
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@app.route('/search')
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@cross_origin(origin='localhost', headers=['Content- Type','Authorization'])
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def search():
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qry = request.args.get('query', '')
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test = np.zeros((tfidf[0].shape))
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keywords = []
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for word in qry.split(' '):
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# validate word
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if len(word) <2 or word in stop_words:
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continue
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try:
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idx = features.index(word)
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test[0][idx] = 1
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except ValueError, e:
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pass
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cosine_similarities = cosine_similarity(test, tfidf).flatten()
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related_docs_indices = cosine_similarities.argsort()[:-100:-1] # TOP 100 results
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MAX = 100
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data = []
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related_docs_indices = related_docs_indices[:MAX]
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tag_map = {} # All tags and their counts
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for img in indices[related_docs_indices]:
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file_path = "/Users/smallya/workspace/Rekognition-personal-searchengine/" + img
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labels = d_index[img]
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word = qry.split(' ')[0]
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data.append(file_path)
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print related_docs_indices
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return json.dumps(data)
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if __name__ == "__main__":
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app.run(port=8000, threaded=True)
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# <FILESEP>
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import os
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import sys
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import time
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import random
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import string
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import argparse
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import torch
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import torch.backends.cudnn as cudnn
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import torch.nn.init as init
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import torch.optim as optim
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import torch.utils.data
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import numpy as np
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from utils import CTCLabelConverter, CTCLabelConverterForBaiduWarpctc, AttnLabelConverter, Averager
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from dataset import hierarchical_dataset, AlignCollate, Batch_Balanced_Dataset
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