Clickbait Repel Flask App

app.py

from flask import Flask
import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_cnn import TextCNN
from tensorflow.contrib import learn

app = Flask(__name__)

@app.route('/check/<estring>')
def hello_world(estring):
    return check(estring)

tf.flags.DEFINE_string("checkpoint_dir", "runs/1483816176/checkpoints/", "Checkpoint directory from training run")
tf.flags.DEFINE_boolean("eval_train", True, "Evaluate on all training data")
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")
FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab")
vocab_processor = learn.preprocessing.VocabularyProcessor.restore(vocab_path)
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
graph = tf.Graph()
def check(estring):
    x_raw = [estring]
    x_test = np.array(list(vocab_processor.transform(x_raw)))
    with graph.as_default():
        session_conf = tf.ConfigProto(
          allow_soft_placement=FLAGS.allow_soft_placement,
          log_device_placement=FLAGS.log_device_placement)
        sess = tf.Session(config=session_conf)
        with sess.as_default():
            # Load the saved meta graph and restore variables
            saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
            saver.restore(sess, checkpoint_file)
            # Get the placeholders from the graph by name
            input_x = graph.get_operation_by_name("input_x").outputs[0]
            # input_y = graph.get_operation_by_name("input_y").outputs[0]
            dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]
            # Tensors we want to evaluate
            predictions = graph.get_operation_by_name("output/predictions").outputs[0]
            # Generate batches for one epoch
            batches = data_helpers.batch_iter(list(x_test), 1, 1, shuffle=False)
            # Collect the predictions here
            all_predictions = []
            for x_test_batch in batches:
                batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
                all_predictions = np.concatenate([all_predictions, batch_predictions])
            if(all_predictions[0]==1):
                return "L"
            else:
                return "C"