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class TrieNode:
    def __init__(self):
        self.child = {}
        self.last = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, data):

prakhar21

class TrieNode:
    def __init__(self):
        self.child = {}
        self.last = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, data):

prakhar21

class TrieNode:
    def __init__(self):
        self.child = {}
        self.last = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, data):

prakhar21

class TrieNode:
    def __init__(self):
        self.child = {}
        self.last = False

class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, data):

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document_embeddings = DocumentRNNEmbeddings(word_embeddings, hidden_size=512, reproject_words=True, reproject_words_dimension=256, rnn_type='LSTM', rnn_layers=1, bidirectional=False)
classifier = TextClassifier(document_embeddings, label_dictionary=corpus.make_label_dictionary(), multi_label=False)
trainer = ModelTrainer(classifier, corpus)
trainer.train('./model', max_epochs=20, patience=5, mini_batch_size=32, learning_rate=0.1)

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def initialize_embeddings():
    """
    Summary:
        Stacks the list of pre-trained embedding vectors to be used as word representation (in concat.)
    Return:
        list: Returns list of pretrained embeddings vectors
    """
    word_embeddings = [
			WordEmbeddings('glove'),
			FlairEmbeddings('news-forward'),

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def segment_data(data_file):
    try:
        import pandas as pd
    except ImportError:
        raise
    
    data = pd.read_csv(data_file, encoding='latin-1').sample(frac=1).drop_duplicates()
    data = data[['classes', 'title']].rename(columns={"classes":"label", "title":"text"})
    data['label'] = '__label__' +data['label'].astype(str)
    data['text'] = data['text'].apply(lambda k: k.lower().strip())

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 import random  
 import os  
 from locust import HttpLocust, TaskSet, task

 TEST_DATA_PATH = 'test.csv' 
 
 def load_test_sentences():  
   utterances = []  
   with open(TEST_DATA_PATH, 'r') as fp:  
     for row in fp:  

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import folium
from folium.plugins import MarkerCluster

city_latlong = {
    'Agra': [27.1767, 78.0081], 'Ahmedabad': [23.0225, 72.5714], 'Durgapur': [23.5204, 87.3119], 
    'Aurangabad': [19.8762, 75.3433], 'Bengaluru': [12.9716, 77.5946], 'Bhopal': [23.2599, 77.4126], 
    'Coimbatore': [11.0168, 76.9558], 'Delhi': [28.7041, 77.1025], 'Dhanbad': [23.7957, 86.4304], 
    'Faridabad': [28.4089, 77.3178], 'Ghaziabad': [28.6692, 77.4538], 'Gwalior': [26.2183, 78.1828], 
    'Hyderabad': [17.3850, 78.4867], 'Indore': [22.7196, 75.8577], 'Jaipur': [26.9124, 75.7873], 
    'Jabalpur': [23.1815, 79.9864], 'Jamshedpur': [22.8046, 86.2029], 'Jodhpur': [26.2389, 73.0243], 

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#!/usr/bin/env python
from flask import Flask, jsonify, request
import numpy as np
import pickle

app = Flask(__name__)

model_filepath = 'best_model.pickle'

def load_model():