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import uuid
import gradio as gr
from io_utils import get_logs_file, read_scanners, write_scanners
from text_classification_ui_helpers import (
get_related_datasets_from_leaderboard,
align_columns_and_show_prediction,
check_dataset,
precheck_model_ds_enable_example_btn,
try_submit,
write_column_mapping_to_config,
)
from wordings import CONFIRM_MAPPING_DETAILS_MD, INTRODUCTION_MD, USE_INFERENCE_API_TIP
MAX_LABELS = 40
MAX_FEATURES = 20
EXAMPLE_MODEL_ID = "cardiffnlp/twitter-roberta-base-sentiment-latest"
CONFIG_PATH = "./config.yaml"
def get_demo():
with gr.Row():
gr.Markdown(INTRODUCTION_MD)
uid_label = gr.Textbox(
label="Evaluation ID:", value=uuid.uuid4, visible=False, interactive=False
)
with gr.Row():
model_id_input = gr.Textbox(
label="Hugging Face model id",
placeholder=EXAMPLE_MODEL_ID + " (press enter to confirm)",
)
with gr.Column():
dataset_id_input = gr.Dropdown(
choices=[],
value="",
allow_custom_value=True,
label="Hugging Face Dataset id",
)
with gr.Row():
dataset_config_input = gr.Dropdown(label="Dataset Config", visible=False, allow_custom_value=True)
dataset_split_input = gr.Dropdown(label="Dataset Split", visible=False, allow_custom_value=True)
with gr.Row():
first_line_ds = gr.DataFrame(label="Dataset preview", visible=False)
with gr.Row():
loading_status = gr.HTML(visible=True)
with gr.Row():
example_btn = gr.Button(
"Validate model & dataset",
visible=True,
variant="primary",
interactive=False,
)
with gr.Row():
example_input = gr.HTML(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():
gr.Markdown("# Label Mapping")
for _ in range(MAX_LABELS):
column_mappings.append(gr.Dropdown(visible=False))
with gr.Column():
gr.Markdown("# Feature Mapping")
for _ in range(MAX_LABELS, MAX_LABELS + MAX_FEATURES):
column_mappings.append(gr.Dropdown(visible=False))
with gr.Accordion(label="Model Wrap Advance Config", open=True):
gr.HTML(USE_INFERENCE_API_TIP)
run_inference = gr.Checkbox(value=True, label="Run with Inference API")
inference_token = gr.Textbox(
placeholder="hf-xxxxxxxxxxxxxxxxxxxx",
value="",
label="HF Token for Inference API",
visible=True,
interactive=True,
)
with gr.Accordion(label="Scanner Advance Config (optional)", open=False):
scanners = gr.CheckboxGroup(label="Scan Settings", visible=True)
@gr.on(triggers=[uid_label.change], inputs=[uid_label], outputs=[scanners])
def get_scanners(uid):
selected = read_scanners(uid)
# 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"]
return gr.update(
choices=scan_config, value=selected, label="Scan Settings", visible=True
)
with gr.Row():
run_btn = gr.Button(
"Get Evaluation Result",
variant="primary",
interactive=False,
size="lg",
)
with gr.Row():
logs = gr.Textbox(
value=get_logs_file,
label="Giskard Bot Evaluation Log:",
visible=False,
every=0.5,
)
scanners.change(write_scanners, inputs=[scanners, uid_label])
gr.on(
triggers=[model_id_input.change],
fn=get_related_datasets_from_leaderboard,
inputs=[model_id_input],
outputs=[dataset_id_input],
).then(
fn=check_dataset,
inputs=[dataset_id_input],
outputs=[dataset_config_input, dataset_split_input, loading_status]
)
gr.on(
triggers=[dataset_id_input.input],
fn=check_dataset,
inputs=[dataset_id_input],
outputs=[dataset_config_input, dataset_split_input, loading_status]
)
gr.on(
triggers=[label.change for label in column_mappings],
fn=write_column_mapping_to_config,
inputs=[
uid_label,
*column_mappings,
],
)
# label.change sometimes does not pass the changed value
gr.on(
triggers=[label.input for label in column_mappings],
fn=write_column_mapping_to_config,
inputs=[
uid_label,
*column_mappings,
],
)
gr.on(
triggers=[
model_id_input.change,
dataset_id_input.change,
dataset_config_input.change,
dataset_split_input.change,
],
fn=precheck_model_ds_enable_example_btn,
inputs=[
model_id_input,
dataset_id_input,
dataset_config_input,
dataset_split_input,
],
outputs=[example_btn, first_line_ds, loading_status],
)
gr.on(
triggers=[
example_btn.click,
],
fn=align_columns_and_show_prediction,
inputs=[
model_id_input,
dataset_id_input,
dataset_config_input,
dataset_split_input,
uid_label,
run_inference,
inference_token,
],
outputs=[
example_input,
example_prediction,
column_mapping_accordion,
run_btn,
loading_status,
*column_mappings,
],
)
gr.on(
triggers=[
run_btn.click,
],
fn=try_submit,
inputs=[
model_id_input,
dataset_id_input,
dataset_config_input,
dataset_split_input,
run_inference,
inference_token,
uid_label,
],
outputs=[run_btn, logs, uid_label],
)
def enable_run_btn(run_inference, inference_token, model_id, dataset_id, dataset_config, dataset_split):
if not run_inference or inference_token == "":
return gr.update(interactive=False)
if model_id == "" or dataset_id == "" or dataset_config == "" or dataset_split == "":
return gr.update(interactive=False)
return gr.update(interactive=True)
gr.on(
triggers=[
run_inference.input,
inference_token.input,
scanners.input,
],
fn=enable_run_btn,
inputs=[
run_inference,
inference_token,
model_id_input,
dataset_id_input,
dataset_config_input,
dataset_split_input
],
outputs=[run_btn],
)
gr.on(
triggers=[label.input for label in column_mappings],
fn=enable_run_btn,
inputs=[
run_inference,
inference_token,
model_id_input,
dataset_id_input,
dataset_config_input,
dataset_split_input
], # FIXME
outputs=[run_btn],
)
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