def post_predict_day():
answer = {}
content = request.json
if 'time' in content and 'title' in content and 'text' in content and 'subreddit' in content:
time = content['time']
title = content['title']
text = content['text']
subreddit = content['subreddit']
if subreddit > 0 and subreddit <= len(rd.subreddit_list):
titles, times, subreddits, texts = rd.dailydata(
title, time, subreddit, text)
predictions = rm.getprediction(model, titles, times, subreddits,
texts)
answer = {"times": times, "predictions": predictions}
else:
answer = {
"error":
"Subreddit must be an integer between 1 and " +
str(len(rd.subreddit_list))
}
else:
answer = {
"error":
"Missing one or more fields. Please provide time, title, text, and subreddit"
}
return jsonify(answer)
def post_predict():
answer = {}
content = request.json
if 'time' in content and 'title' in content and 'text' in content and 'subreddit' in content:
time = [content['time']]
title = [content['title']]
text = [content['text']]
subreddit = [content['subreddit']]
if subreddit[0] > 0 and subreddit[0] <= len(rd.subreddit_list):
predictions = rm.getprediction(model, title, time, subreddit, text)
answer = {"prediction": predictions[0]}
else:
answer = {
"error":
"Subreddit must be an integer between 1 and " +
str(len(rd.subreddit_list))
}
else:
answer = {
"error":
"Missing one or more fields. Please provide time, title, text, and subreddit"
}
return jsonify(answer)
#mongodb setup
connection_string = "mongodb://localhost:27017" if len(
sys.argv) == 1 else sys.argv[1]
client = MongoClient(connection_string)
db = client["reddit-comment-vote-predictor"]
collection = db.comments
model = rm.getmodelandweights()
comments = []
database_comments = collection.find().limit(200)
for comment in database_comments:
comments += [comment]
titles = [c['submission_title'] for c in comments]
times = [c['timepostedutc'] for c in comments]
subreddits = [rd.convertsubreddittoint(c['subreddit']) for c in comments]
texts = [c['text'] for c in comments]
predictions = rm.getprediction(model, titles, times, subreddits, texts,
collection)
predictions.sort()
predictions = rd.removedecimals(predictions)
print(predictions)
print("Max prediction: " + str(max(predictions)))
print("Min prediction: " + str(min(predictions)))
modelgenerative = rmg.getmodel(vocab_size=vocab_size,
embedding_dim=rmg.embedding_dim,
rnn_units=rmg.rnn_units,
batch_size=1)
modelgenerative.load_weights(rmg.checkpoint_dir)
modelgenerative.build(tf.TensorShape([1, None]))
#Model which predicts if a comment will be removed on /r/science
modelscience = rms.getmodelandweights()
commentstoremove = []
obtainedcommentstoremovetime = None
predictions = rm.getprediction(model, ["title"], [111111], [1], ["text"],
collection)
print("Predictions:")
print(predictions)
predictions_science = rms.getprediction(modelscience, ["title"], ["text"])
print("Science predictions:")
print(predictions_science)
generatedtext = rmg.generatesentence(modelgenerative, "This ", char2idx,
idx2char)
print("Generated text:")
print(generatedtext)