app_text_classification.py

import gradio as gr
import uuid
from io_utils import (
    read_scanners,
    write_scanners,
    read_inference_type,
    write_inference_type,
    get_logs_file,
)
from wordings import INTRODUCTION_MD, CONFIRM_MAPPING_DETAILS_MD
from text_classification_ui_helpers import (
    try_submit,
    check_dataset_and_get_config,
    check_dataset_and_get_split,
    check_model_and_show_prediction,
    write_column_mapping_to_config,
)

MAX_LABELS = 20
MAX_FEATURES = 20

EXAMPLE_MODEL_ID = "cardiffnlp/twitter-roberta-base-sentiment-latest"
EXAMPLE_DATA_ID = "tweet_eval"
CONFIG_PATH = "./config.yaml"


def get_demo(demo):
    with gr.Row():
        gr.Markdown(INTRODUCTION_MD)
    with gr.Row():
        model_id_input = gr.Textbox(
            label="Hugging Face model id",
            placeholder=EXAMPLE_MODEL_ID + " (press enter to confirm)",
        )

        dataset_id_input = gr.Textbox(
            label="Hugging Face Dataset id",
            placeholder=EXAMPLE_DATA_ID + " (press enter to confirm)",
        )

    with gr.Row():
        dataset_config_input = gr.Dropdown(label="Dataset Config", visible=False)
        dataset_split_input = gr.Dropdown(label="Dataset Split", visible=False)

    with gr.Row():
        example_input = gr.Markdown("Example Input", visible=False)
    with gr.Row():
        example_prediction = gr.Label(label="Model Prediction Sample", visible=False)

    with gr.Row():
        with gr.Accordion(
            label="Label and Feature Mapping", visible=False, open=False
        ) as column_mapping_accordion:
            with gr.Row():
                gr.Markdown(CONFIRM_MAPPING_DETAILS_MD)
            column_mappings = []
            with gr.Row():
                with gr.Column():
                    for _ in range(MAX_LABELS):
                        column_mappings.append(gr.Dropdown(visible=False))
                with gr.Column():
                    for _ in range(MAX_LABELS, MAX_LABELS + MAX_FEATURES):
                        column_mappings.append(gr.Dropdown(visible=False))

    with gr.Accordion(label="Model Wrap Advance Config (optional)", open=False):
        run_local = gr.Checkbox(value=True, label="Run in this Space")
        use_inference = read_inference_type(CONFIG_PATH) == "hf_inference_api"
        run_inference = gr.Checkbox(value=use_inference, label="Run with Inference API")

    with gr.Accordion(label="Scanner Advance Config (optional)", open=False):
        selected = read_scanners(CONFIG_PATH)
        # currently we remove data_leakage from the default scanners
        # Reason: data_leakage barely raises any issues and takes too many requests
        # when using inference API, causing rate limit error
        scan_config = selected + ["data_leakage"]
        scanners = gr.CheckboxGroup(
            choices=scan_config, value=selected, label="Scan Settings", visible=True
        )

    with gr.Row():
        run_btn = gr.Button(
            "Get Evaluation Result",
            variant="primary",
            interactive=True,
            size="lg",
        )

    with gr.Row():
        uid = uuid.uuid4()
        uid_label = gr.Textbox(
            label="Evaluation ID:", value=uid, visible=False, interactive=False
        )
        logs = gr.Textbox(label="Giskard Bot Evaluation Log:", visible=False)
        demo.load(get_logs_file, uid_label, logs, every=0.5)

    gr.on(
        triggers=[label.change for label in column_mappings],
        fn=write_column_mapping_to_config,
        inputs=[
            dataset_id_input,
            dataset_config_input,
            dataset_split_input,
            *column_mappings,
        ],
    )

    gr.on(
        triggers=[
            model_id_input.change,
            dataset_config_input.change,
            dataset_split_input.change,
        ],
        fn=check_model_and_show_prediction,
        inputs=[
            model_id_input,
            dataset_id_input,
            dataset_config_input,
            dataset_split_input,
        ],
        outputs=[
            example_input,
            example_prediction,
            column_mapping_accordion,
            *column_mappings,
        ],
    )

    dataset_id_input.blur(
        check_dataset_and_get_config, dataset_id_input, dataset_config_input
    )

    dataset_config_input.change(
        check_dataset_and_get_split,
        inputs=[dataset_id_input, dataset_config_input],
        outputs=[dataset_split_input],
    )

    scanners.change(write_scanners, inputs=scanners)

    run_inference.change(write_inference_type, inputs=[run_inference])

    gr.on(
        triggers=[
            run_btn.click,
        ],
        fn=try_submit,
        inputs=[
            model_id_input,
            dataset_id_input,
            dataset_config_input,
            dataset_split_input,
            run_local,
            uid_label,
        ],
        outputs=[run_btn, logs],
    )

    def enable_run_btn():
        return gr.update(interactive=True)

    gr.on(
        triggers=[
            model_id_input.change,
            dataset_config_input.change,
            dataset_split_input.change,
            run_inference.change,
            run_local.change,
            scanners.change,
        ],
        fn=enable_run_btn,
        inputs=None,
        outputs=[run_btn],
    )

    gr.on(
        triggers=[label.change for label in column_mappings],
        fn=enable_run_btn,
        inputs=None,
        outputs=[run_btn],
    )