Delete server.py

-import numpy as np
-import pickle
-import tensorflow as tf
-from flask import Flask, jsonify, render_template, request
-import model
-
-# Load in data structures
-with open("data/wordList.txt", "rb") as fp:
-    wordList = pickle.load(fp)
-wordList.append('<pad>')
-wordList.append('<EOS>')
-
-# Load in hyperparamters
-vocabSize = len(wordList)
-batchSize = 24
-maxEncoderLength = 15
-maxDecoderLength = 15
-lstmUnits = 112
-numLayersLSTM = 3
-
-# Create placeholders
-encoderInputs = [tf.placeholder(tf.int32, shape=(None,)) for i in range(maxEncoderLength)]
-decoderLabels = [tf.placeholder(tf.int32, shape=(None,)) for i in range(maxDecoderLength)]
-decoderInputs = [tf.placeholder(tf.int32, shape=(None,)) for i in range(maxDecoderLength)]
-feedPrevious = tf.placeholder(tf.bool)
-
-encoderLSTM = tf.nn.rnn_cell.BasicLSTMCell(lstmUnits, state_is_tuple=True)
-#encoderLSTM = tf.nn.rnn_cell.MultiRNNCell([singleCell]*numLayersLSTM, state_is_tuple=True)
-decoderOutputs, decoderFinalState = tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq(encoderInputs, decoderInputs, encoderLSTM, 
-                                                            vocabSize, vocabSize, lstmUnits, feed_previous=feedPrevious)
-
-decoderPrediction = tf.argmax(decoderOutputs, 2)
-
-# Start session and get graph
-sess = tf.Session()
-#y, variables = model.getModel(encoderInputs, decoderLabels, decoderInputs, feedPrevious)
-
-# Load in pretrained model
-saver = tf.train.Saver()
-saver.restore(sess, tf.train.latest_checkpoint('models'))
-zeroVector = np.zeros((1), dtype='int32')
-
-def pred(inputString):
-    inputVector = model.getTestInput(inputString, wordList, maxEncoderLength)
-    feedDict = {encoderInputs[t]: inputVector[t] for t in range(maxEncoderLength)}
-    feedDict.update({decoderLabels[t]: zeroVector for t in range(maxDecoderLength)})
-    feedDict.update({decoderInputs[t]: zeroVector for t in range(maxDecoderLength)})
-    feedDict.update({feedPrevious: True})
-    ids = (sess.run(decoderPrediction, feed_dict=feedDict))
-    return model.idsToSentence(ids, wordList)
-
-# webapp
-app = Flask(__name__, template_folder='./')
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