def main():
data_set = SquADDataSet(data_path='./data/SQuAD/train-v1.1.json')
print('size: ', data_set.size())
for index in range(20):
context, question, answer = data_set.get_data(index)
print('#' + str(index) + ' context:', context)
print('#' + str(index) + ' question:', question)
print('#' + str(index) + ' answer:', answer)
print('++++++++++++++++++++++++++++++++++++++++++++++++++++')
def main():
qa = Seq2SeqV2QA()
qa.load_model(model_dir_path='./models')
data_set = SquADDataSet(data_path='./data/SQuAD/train-v1.1.json')
for i in range(20):
index = i * 10
paragraph, question, actual_answer = data_set.get_data(index)
predicted_answer = qa.reply(paragraph, question)
print('context: ', paragraph)
print('question: ', question)
print({'guessed_answer': predicted_answer, 'actual_answer': actual_answer})
def main():
with open('./data/SQuAD/train-v1.1.json') as file:
ds = json.load(file)
data_set = SquADDataSet(queries=ds)
# data_set = SquADDataSet(data_path='./data/SQuAD/train-v1.1.json')
print('size: ', data_set.size())
for index, value in enumerate(data_set.get_data()[0:1]):
context, question, answer = value
print('#' + str(index) + ' context:', context)
print('#' + str(index) + ' question:', question)
print('#' + str(index) + ' answer:', answer)
print('++++++++++++++++++++++++++++++++++++++++++++++++++++')
def main():
qa = Seq2SeqQA()
qa.load_model(model_dir_path='./models')
data_set = SquADDataSet(data_path='./data/SQuAD/train-v1.1.json')
with open('./data/SQuAD/train-v1.1.json') as f:
import json
print(json.load(f)['data'][0]['paragraphs'][0]['qas'])
def main():
random_state = 42
output_dir_path = './models'
np.random.seed(random_state)
data_set = SquADDataSet(data_path='./data/SQuAD/train-v1.1.json')
qa = Seq2SeqQA()
batch_size = 64
epochs = 200
history = qa.fit(data_set,
model_dir_path=output_dir_path,
batch_size=batch_size,
epochs=epochs,
random_state=random_state)
from flask import Flask, request, redirect, render_template, flash, abort, jsonify
from keras_question_and_answering_system.library.seq2seq import Seq2SeqQA
from keras_question_and_answering_system.library.utility.squad import SquADDataSet
app = Flask(__name__)
app.config.from_object(__name__) # load config from this file , flaskr.py
# Load default config and override config from an environment variable
app.config.from_envvar('FLASKR_SETTINGS', silent=True)
app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024
seq2seq = Seq2SeqQA()
data_set = SquADDataSet(data_path=None)
qa_list = list()
@app.route('/')
def home():
return render_template('home.html', qa_list=qa_list)
#@app.route('/about')
#def about():
# return 'About Us'
@app.route('/qa', methods=['POST', 'GET'])
def qa():
if request.method == 'POST':
if 'question_context' not in request.form and 'question' not in request.form:
flash('No sentence post')