Creating custom labels review recipe to remove noise from the dataset

[ryanwesslen]

hi @nlp-guy,

@nlp-guy

I can create a JSON file with image paths and labels from the data frame.

Yep. To pre-fill, you need to have the key of each label align to the name of the field_id, for example:

{"image": "data/image-arms/images.png", "arm_a": "needle_driver", "arm_b": "nan", "arm_c": "needle_driver", "arm_d": "cadiere_forceps"}

See this post for more details

Pre-filled text_input interface to capture edit operations

Hi! I hope I understand your question correctly, but I think I'm actually doing the same thing in my image captioning tutorial to manually correct the captions suggested by the model. Prodigy's input and output format is identical, so you can pre-populate the field you're storing the free-form text in (configurable by the field_id), and the pre-populated text will show up in the UI. In the example recipe, I've used the field ID caption for the editable caption, and added another key orig_captio…

Since you'll be loading a file (.jsonl), you'll need to use the JSONL loader but also the fetch_media importer to get the images.

I wrote a modified version of the script above that assumes a .jsonl like I showed above:

import prodigy
from prodigy.components.preprocess import fetch_media
from prodigy.components.loaders import JSONL

@prodigy.recipe(("data-review-recipe"))
def data_review_recipe(dataset, image_file):    
    
    stream = JSONL(image_file)
    stream = fetch_media(stream, ["image"], skip=True)
   
    tools = [
        "needle_driver",
        "monopolar_curved_scissor",
        "force_bipolar",
        "clip_applier",
        "tip_up_fenestrated_grasper",
        "cadiere_forceps",
        "bipolar_forceps",
        "vessel_sealer",
        "suction_irrigator",
        "bipolar_dissector",
        "prograsp_forceps",
        "stapler",
        "permanent_cautery_hook_spatula",
        "grasping_retractor",
        "nan",
        "blank"
    ]

    blocks = [
        {"view_id":"image"},
        {"view_id": "text_input", "field_id": "arm_a", "field_placeholder": "Arm A", "field_suggestions": tools},
        {"view_id": "text_input", "field_id": "arm_b", "field_placeholder": "Arm B", "field_suggestions": tools},
        {"view_id": "text_input", "field_id": "arm_c", "field_placeholder": "Arm C", "field_suggestions": tools},
        {"view_id": "text_input", "field_id": "arm_d", "field_placeholder": "Arm D", "field_suggestions": tools},
    ]

    return {
        "view_id": "blocks",
        "config": {"blocks": blocks},
        "dataset": dataset,
		"stream": stream,
    }

It seemed to work for me. Hope this helps!