REMOTE_FILE
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Load a remote file from an HTTP URL endpoint, infer the type, and convert it to a DataContainer class. Note: If both the file_url and default are not specified when file_type="Image", a default image will be loaded.
For now, REMOTE_FILE only supports HTTP file URLs, in particular GCP URL (starting with gcp://). S3 URL (starting with s3://) and other bucket-like URLs are not supported.
If the file url is not specified and the default input is not connected, or if the file url is not a valid URL, a ValueError is raised. Params: file_url : str URL of the file to be loaded. default : Optional[String] If this input node is connected, the file URL 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. Returns: out : Image|DataFrame Image for file_type 'image'.
DataFrame for file_type 'json', 'csv', 'excel', 'xml'.
Python Code
from urllib.parse import urlparse
from flojoy import flojoy, Image, DataFrame, Grayscale, String
from typing import Literal, Optional
import numpy as np
from PIL import Image as PIL_Image
import pandas as pd
import requests
from io import BytesIO
def get_file_url(url: str):
parse_result = urlparse(url)
if not all([parse_result.scheme, parse_result.netloc]):
raise ValueError(f"'{url}' is not a valid URL.")
return url
# TODO: Consolidate remote files (S3, HTTP, etc...) and perhaps even merge REMOTE_FILE AND LOCAL_FILE in a single node.
@flojoy(
deps={
"xlrd": "2.0.1",
"lxml": "4.9.2",
"openpyxl": "3.0.10",
"scikit-image": "0.21.0",
}
)
def REMOTE_FILE(
file_url: str = None,
default: Optional[String] = None,
file_type: Literal["Image", "Grayscale", "JSON", "CSV", "Excel", "XML"] = "Image",
) -> Image | DataFrame:
"""Load a remote file from an HTTP URL endpoint, infer the type, and convert it to a DataContainer class.
Note: If both the file_url and default are not specified when file_type="Image", a default image will be loaded.
For now, REMOTE_FILE only supports HTTP file URLs, in particular GCP URL (starting with gcp://). S3 URL (starting with s3://) and other bucket-like URLs are not supported.
If the file url is not specified and the default input is not connected, or if the file url is not a valid URL, a ValueError is raised.
Parameters
----------
file_url : str
URL of the file to be loaded.
default : Optional[String]
If this input node is connected, the file URL 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.
Returns
-------
Image|DataFrame
Image for file_type 'image'.
DataFrame for file_type 'json', 'csv', 'excel', 'xml'.
"""
file_url = default.s if default else file_url
file_url = "" if file_url is None else file_url
match file_type:
case "Image":
file_url = get_file_url(file_url)
response = requests.get(file_url)
f = PIL_Image.open(BytesIO(response.content))
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_url = get_file_url(file_url)
return Grayscale(img=skimage.io.imread(file_url, as_gray=True))
case "CSV":
file_url = get_file_url(file_url)
df = pd.read_csv(file_url)
return DataFrame(df=df)
case "JSON":
file_url = get_file_url(file_url)
df = pd.read_json(file_url)
return DataFrame(df=df)
case "XML":
file_url = get_file_url(file_url)
df = pd.read_xml(file_url)
return DataFrame(df=df)
case "Excel":
file_url = get_file_url(file_url)
df = pd.read_excel(file_url)
return DataFrame(df=df)
Example App
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In this example, a REMOTE_FILE node loads Flojoy’s logo from the internet and plots using a IMAGE node.