General Discussion

Hi,

Here's some python code using the API that assumes you have a flow starting with an "Uploaded File" type dataset. This assumes you've written the code to read in an uploaded file from the webapp user - then it places this file in the initial dataset in your flow.

import dataiku
from dataiku import pandasutils as pdu
import pandas as pd

client = dataiku.api_client()

proj = client.get_project("PROJECT_KEY")

ds = proj.get_dataset("UPLOADED_FILE_DATASET_NAME")

#clear any existing file from this dataset
ds.clear()

upload_file_path = "your_path_here"

with open(upload_file_path, "rb") as f:
    ds.uploaded_add_file(f, upload_file_path)

Then, assuming you've created a scenario in your project that runs your ML pipeline, you can run this scenario via another API call in your webapp.

scenario = proj.get_scenario("SCENARIO_ID")

trigger_fire = scenario.run()

There are a number of variants to running scenarios via the API - doc here: https://doc.dataiku.com/dss/latest/python-api/scenarios.html#run-a-scenario

If using Bokeh, the way to load the latest trained model from the flow would indeed be via the API.

If you're interested in developing applications using flow components (replacing files in datasets, running scenarios, pulling ML model info) without having to code them yourself using the APIs - project applications would be a great thing to check out. Here's some hands-on tutorials: https://academy.dataiku.com/dataiku-applications-tutorials-open

Best,

Pat

Even if there are multiple projects, at the project level it is fairly easy to change the connection in bulk:

• In your flow,
• (bottom left) View > Connections > connection > Select all checked
• (bottom right) Other Actions > Change Connection

See the image below.

I hope this helps.