def analyze_image():
# Get image data from body
file = request.files['file']
# Post header
headers = {
'Content-Type': 'application/octet-stream',
'Ocp-Apim-Subscription-Key': subscription_key
}
# Post params
params = {'visualFeatures': 'Description', 'language': 'ja'}
# Call API and get tags
try:
res = req.post(endpoint, headers=headers, params=params, data=file)
except Exception(e):
print(e)
print(res.json())
tags = res.json()['description']['tags']
splited_text = open('./resources/splited.txt').read()
text_model = NewlineText(splited_text)
for tag in tags:
try:
sentence = text_model.make_sentence_with_start(
tag, tries=300, max_overlap_ratio=0.7).replace(' ', '')
return sentence
except:
pass
return 'Could not create sentence'
def train_models(users):
users = dict(filter(lambda u: u[1][MESSAGES], users.items()))
for user_id, user in users.items():
model = NewlineText('\n'.join(user[MESSAGES]),
retain_original=False)
users[user_id][MODEL] = model
return users
def get_model(bucket, team_id, user_id):
key = f"models/{team_id}/{user_id}.json"
print(f'downloading model {key}')
model_json = BytesIO()
bucket.download_fileobj(key, model_json)
model = str(model_json.getbuffer(), 'utf-8')
return NewlineText.from_chain(model)
def get_compressed_model(bucket, team_id, user_id):
key = f"models/{team_id}/{user_id}.gz"
print(f'downloading model {key}')
model_gz = BytesIO()
bucket.download_fileobj(key, model_gz)
model = str(zlib.decompress(model_gz.getbuffer()), 'utf-8')
return NewlineText.from_chain(model)
async def create_model_for_user(self, user_id: int):
results = self.history.find({'author': user_id}, {'msg': 1})
f = StringIO()
async for doc in results:
f.write(doc['msg'])
f.write('\n')
model = NewlineText(f.getvalue(), well_formed=False)
if model:
await self.save_user_model(user_id, model)
return model
def handle_one(body):
if 'subtype' in body['event']:
subtype = body['event']['subtype']
print(f"ignoring event with subtype {subtype}")
return
print(body)
team_id = body['team_id']
user_id = body['event']['user']
channel_id = body['event']['channel']
text = body['event']['text']
print(f'message from {user_id} in {channel_id}: {text}')
old_model = get_model(bucket, team_id, user_id)
new_model = NewlineText(text, retain_original=False)
combined = markovify.combine([old_model, new_model])
put_model(bucket, team_id, user_id, combined)
async def fetch_user_model(self, user_id: int):
result = await self.models.find_one({'_id': user_id}, {'msg': 1})
if result:
return NewlineText.from_json(zlib.decompress(result['msg']))
from discord.ext.commands import command
from .cog import Cog
from tswift import Song
from markovify import NewlineText
try:
with open('all.txt', 'rt') as tmg:
text = NewlineText(tmg.read(), state_size=2)
except FileNotFoundError:
text = None
class Lyrics(Cog):
@command(aliases=['lyrics', 'lyric'])
async def get_lyrics(self, song_name):
"""only mountain goats songs, duh"""
song = Song.find_song(song_name + " The mountain goats")
if song:
lyrics = f'```\n{song.title} - {song.artist}\n{song.lyrics}\n```'
await self.bot.say(lyrics)
else:
await self.bot.reply("Sorry, couldn't find that song")
@command(aliases=['sing'])
async def make_lyric(self):
"""Generates Mountain Goats lyrics."""
if text:
await self.bot.say(text.make_sentence())
else:
await self.bot.say("No text file found for generation.")