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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, get_logs_file
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.yaml') == '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.yaml')
# 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]) |