async def get_path(request, file_path=""):
print(file_path)
as_records = request.args.get('records')
as_json = request.args.get('json')
as_log = request.args.get('log')
as_attachment = int(request.args.get('download', '0'))
is_recursive = request.args.get('recursive')
show_hidden = request.args.get('hidden')
query = request.args.get('query', "*").strip()
_start = request.args.get('start', None)
_stop = request.args.get('stop', None)
start = None if _start is None else int(_start)
stop = None if _stop is None else int(_stop)
reservoir_k = int(request.args.get('reservoir', '200'))
# limit for the search itself.
search_limit = 500
path = os.path.join(config.Args.logdir, file_path)
print("=============>", [query], [path], os.path.isdir(path))
if os.path.isdir(path):
from itertools import islice
with cwd(path):
print(os.getcwd(), query, is_recursive)
file_paths = list(islice(iglob(query, recursive=is_recursive), start or 0, stop or 200))
files = map(file_stat, file_paths)
res = response.json(files, status=200)
elif os.path.isfile(path):
if as_records:
from ml_logger.helpers import load_pickle_as_dataframe
df = load_pickle_as_dataframe(path, reservoir_k)
res = response.text(df.to_json(orient="records"), status=200, content_type='application/json')
elif as_log:
from ml_logger.helpers import load_pickle_as_dataframe
df = load_pickle_as_dataframe(path, reservoir_k)
res = response.text(df.to_json(orient="records"), status=200, content_type='application/json')
elif as_json:
from ml_logger.helpers import load_from_pickle
data = [_ for _ in load_from_pickle(path)]
res = response.json(data, status=200, content_type='application/json')
elif type(start) is int or type(stop) is int:
from itertools import islice
with open(path, 'r') as f:
text = ''.join([l for l in islice(f, start, stop)])
res = response.text(text, status=200)
else:
# todo: check the file handling here. Does this use correct mimeType for text files?
res = await response.file(path)
if as_attachment:
res.headers['Content-Disposition'] = 'attachment'
else:
res = response.text('Not found', status=404)
return res
def get_path(file_path=''):
from flask import request, make_response, jsonify, send_file, Response
print(file_path)
as_records = request.args.get('records')
as_json = request.args.get('json')
as_attachment = request.args.get('download')
is_recursive = request.args.get('recursive')
show_hidden = request.args.get('hidden')
query = request.args.get('query')
start = int(request.args.get('start', '0'))
stop = int(request.args.get('stop', '200'))
path = os.path.join(config.Args.logdir, file_path)
if os.path.isdir(path):
from itertools import islice
files = islice(list_directory(path, ".", is_recursive, show_hidden),
start, stop)
res = make_response(jsonify([*files]), 200)
elif os.path.isfile(path):
if as_records:
from ml_logger.helpers import load_pickle_as_dataframe
df = load_pickle_as_dataframe(path)
res = make_response(df.to_json(orient="records"), 200)
elif as_json:
from ml_logger.helpers import load_pickle_as_dataframe
df = load_pickle_as_dataframe(path)
res = make_response(df.to_json(orient="records"), 200)
else:
res = send_file(path)
if as_attachment:
res.headers.add('Content-Disposition', 'attachment')
else:
res = make_response('Not found', 404)
return res
def read_log(path, k=200):
from ml_logger.helpers import load_pickle_as_dataframe
df = load_pickle_as_dataframe(path, k)
return df.to_json(orient="records")
def read_dataframe(path, k=200):
from ml_logger.helpers import load_pickle_as_dataframe
try:
return load_pickle_as_dataframe(path, k)
except FileNotFoundError:
return None
