LOCAL_FILE
Download Flojoy Studio to try this app
The LOCAL_FILE node loads a local file of a different type and converts it to a DataContainer class. Params: file_path : str The path to the file to be loaded. This can be either an absolute path or
a path relative to the "nodes" directory. default : Optional[String] If this input node is connected, the file name will be taken from
the output of the connected node.
To be used in conjunction with batch processing. file_type : str Type of file to load, default = image.
If both 'file_path' and 'default' are not specified when 'file_type="Image"',
a default image will be loaded.
If the file path is not specified and the default input is not connected,
a ValueError is raised. Returns: out : Image | DataFrame Image for file_type 'image'.
Grayscale from file_type 'Grayscale'.
DataFrame for file_type 'json', 'csv'.
Python Code
import os
from typing import Literal, Optional
import numpy as np
import pandas as pd
from flojoy import DataFrame, Grayscale, Image, String, flojoy
from PIL import Image as PIL_Image
def get_file_path(file_path: str, default_path: str | None = None):
# TODO: We should not do this, this is too fragile
# We need to get an actual file picker going to get the absolute path
f_path = file_path if file_path != "" else default_path
if not f_path:
raise ValueError(
"The file path of the input file is missing. "
"Please provide a input String or a provide `file_path` with a value!"
)
if not os.path.isabs(f_path):
path_to_nodes = __file__[: __file__.rfind("blocks") + 6]
return os.path.abspath(os.path.join(path_to_nodes, f_path))
return f_path
@flojoy(
deps={
"scikit-image": "0.21.0",
}
)
def LOCAL_FILE(
file_path: str | None = None,
default: Optional[String] = None,
file_type: Literal["Image", "Grayscale", "JSON", "CSV"] = "Image",
) -> Image | DataFrame | Grayscale:
"""The LOCAL_FILE node loads a local file of a different type and converts it to a DataContainer class.
Parameters
----------
file_path : str
The path to the file to be loaded. This can be either an absolute path or
a path relative to the "nodes" directory.
default : Optional[String]
If this input node is connected, the file name will be taken from
the output of the connected node.
To be used in conjunction with batch processing.
file_type : str
Type of file to load, default = image.
If both 'file_path' and 'default' are not specified when 'file_type="Image"',
a default image will be loaded.
If the file path is not specified and the default input is not connected,
a ValueError is raised.
Returns
-------
Image | DataFrame
Image for file_type 'image'.
Grayscale from file_type 'Grayscale'.
DataFrame for file_type 'json', 'csv'.
"""
default_image_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)),
"assets",
"astronaut.png",
)
file_path = default.s if default else file_path
file_path = "" if file_path is None else file_path
match file_type:
case "Image":
file_path = get_file_path(file_path, default_image_path)
f = PIL_Image.open(file_path)
img_array = np.array(f.convert("RGBA"))
red_channel = img_array[:, :, 0]
green_channel = img_array[:, :, 1]
blue_channel = img_array[:, :, 2]
if img_array.shape[2] == 4:
alpha_channel = img_array[:, :, 3]
else:
alpha_channel = None
return Image(
r=red_channel,
g=green_channel,
b=blue_channel,
a=alpha_channel,
)
case "Grayscale":
import skimage.io
file_path = get_file_path(file_path, default_image_path)
return Grayscale(img=skimage.io.imread(file_path, as_gray=True))
case "CSV":
file_path = get_file_path(file_path)
df = pd.read_csv(file_path)
return DataFrame(df=df)
case "JSON":
file_path = get_file_path(file_path)
df = pd.read_json(file_path)
return DataFrame(df=df)
# TODO: we might add support for following file types later
# case "XML":
# file_path = get_file_path(file_path)
# df = pd.read_xml(file_path)
# return DataFrame(df=df)
# case "Excel":
# file_path = get_file_path(file_path)
# df = pd.read_excel(file_path)
# return DataFrame(df=df)
Example App
Having problems with this example app? Join our Discord community and we will help you out!
In this example LOCAL_FILE node is loading a default astronaut image which is then visualized with a plotly visualizer node IMAGE.