import gradio as gr
import datasets
import huggingface_hub
import os
import time
import subprocess
import logging
import json
from transformers.pipelines import TextClassificationPipeline
from text_classification import check_column_mapping_keys_validity, text_classification_fix_column_mapping
from utils import read_scanners, write_scanners, read_inference_type, write_inference_type, convert_column_mapping_to_json
HF_REPO_ID = 'HF_REPO_ID'
HF_SPACE_ID = 'SPACE_ID'
HF_WRITE_TOKEN = 'HF_WRITE_TOKEN'
theme = gr.themes.Soft(
primary_hue="green",
)
def check_model(model_id):
try:
task = huggingface_hub.model_info(model_id).pipeline_tag
except Exception:
return None, None
try:
from transformers import pipeline
ppl = pipeline(task=task, model=model_id)
return model_id, ppl
except Exception as e:
return model_id, e
def check_dataset(dataset_id, dataset_config="default", dataset_split="test"):
try:
configs = datasets.get_dataset_config_names(dataset_id)
except Exception:
# Dataset may not exist
return None, dataset_config, dataset_split
if dataset_config not in configs:
# Need to choose dataset subset (config)
return dataset_id, configs, dataset_split
ds = datasets.load_dataset(dataset_id, dataset_config)
if isinstance(ds, datasets.DatasetDict):
# Need to choose dataset split
if dataset_split not in ds.keys():
return dataset_id, None, list(ds.keys())
elif not isinstance(ds, datasets.Dataset):
# Unknown type
return dataset_id, None, None
return dataset_id, dataset_config, dataset_split
def try_validate(m_id, ppl, dataset_id, dataset_config, dataset_split, column_mapping='{}'):
# Validate model
if m_id is None:
gr.Warning('Model is not accessible. Please set your HF_TOKEN if it is a private model.')
return (
gr.update(interactive=False), # Submit button
gr.update(visible=True), # Loading row
gr.update(visible=False), # Preview row
gr.update(visible=False), # Model prediction input
gr.update(visible=False), # Model prediction preview
gr.update(visible=False), # Label mapping preview
gr.update(visible=False), # feature mapping preview
)
if isinstance(ppl, Exception):
gr.Warning(f'Failed to load model": {ppl}')
return (
gr.update(interactive=False), # Submit button
gr.update(visible=True), # Loading row
gr.update(visible=False), # Preview row
gr.update(visible=False), # Model prediction input
gr.update(visible=False), # Model prediction preview
gr.update(visible=False), # Label mapping preview
gr.update(visible=False), # feature mapping preview
)
# Validate dataset
d_id, config, split = check_dataset(dataset_id=dataset_id, dataset_config=dataset_config, dataset_split=dataset_split)
dataset_ok = False
if d_id is None:
gr.Warning(f'Dataset "{dataset_id}" is not accessible. Please set your HF_TOKEN if it is a private dataset.')
elif isinstance(config, list):
gr.Warning(f'Dataset "{dataset_id}" does not have "{dataset_config}" config. Please choose a valid config.')
config = gr.update(choices=config, value=config[0])
elif isinstance(split, list):
gr.Warning(f'Dataset "{dataset_id}" does not have "{dataset_split}" split. Please choose a valid split.')
split = gr.update(choices=split, value=split[0])
else:
dataset_ok = True
if not dataset_ok:
return (
gr.update(interactive=False), # Submit button
gr.update(visible=True), # Loading row
gr.update(visible=False), # Preview row
gr.update(visible=False), # Model prediction input
gr.update(visible=False), # Model prediction preview
gr.update(visible=False), # Label mapping preview
gr.update(visible=False), # feature mapping preview
)
# TODO: Validate column mapping by running once
prediction_result = None
id2label_df = None
if isinstance(ppl, TextClassificationPipeline):
try:
print('validating phase, ', column_mapping)
column_mapping = json.loads(column_mapping)
except Exception:
column_mapping = {}
column_mapping, prediction_input, prediction_result, id2label_df, feature_df = \
text_classification_fix_column_mapping(column_mapping, ppl, d_id, config, split)
column_mapping = json.dumps(column_mapping, indent=2)
if prediction_result is None and id2label_df is not None:
gr.Warning('The model failed to predict with the first row in the dataset. Please provide column mappings in "Advance" settings.')
return (
gr.update(interactive=False), # Submit button
gr.update(visible=False), # Loading row
gr.update(visible=True), # Preview row
gr.update(value=f'**Sample Input**: {prediction_input}', visible=True), # Model prediction input
gr.update(visible=False), # Model prediction preview
gr.update(value=id2label_df, visible=True, interactive=True), # Label mapping preview
gr.update(value=feature_df, visible=True, interactive=True), # feature mapping preview
)
elif id2label_df is None:
gr.Warning('The prediction result does not conform the labels in the dataset. Please provide label mappings in "Advance" settings.')
return (
gr.update(interactive=False), # Submit button
gr.update(visible=False), # Loading row
gr.update(visible=True), # Preview row
gr.update(value=f'**Sample Input**: {prediction_input}', visible=True), # Model prediction input
gr.update(value=prediction_result, visible=True), # Model prediction preview
gr.update(visible=True, interactive=True), # Label mapping preview
gr.update(visible=True, interactive=True), # feature mapping preview
)
gr.Info("Model and dataset validations passed. Your can submit the evaluation task.")
return (
gr.update(interactive=True), # Submit button
gr.update(visible=False), # Loading row
gr.update(visible=True), # Preview row
gr.update(value=f'**Sample Input**: {prediction_input}', visible=True), # Model prediction input
gr.update(value=prediction_result, visible=True), # Model prediction preview
gr.update(value=id2label_df, visible=True, interactive=True), # Label mapping preview
gr.update(value=feature_df, visible=True, interactive=True), # feature mapping preview
)
def try_submit(m_id, d_id, config, split, id2label_mapping_dataframe, feature_mapping_dataframe, local):
label_mapping = {}
for i, label in id2label_mapping_dataframe["Model Prediction Labels"].items():
label_mapping.update({str(i): label})
feature_mapping = {}
for i, feature in feature_mapping_dataframe["Dataset Features"].items():
feature_mapping.update({feature_mapping_dataframe["Model Input Features"][i]: feature})
# TODO: Set column mapping for some dataset such as `amazon_polarity`
if local:
command = [
"python",
"cli.py",
"--loader", "huggingface",
"--model", m_id,
"--dataset", d_id,
"--dataset_config", config,
"--dataset_split", split,
"--hf_token", os.environ.get(HF_WRITE_TOKEN),
"--discussion_repo", os.environ.get(HF_REPO_ID) or os.environ.get(HF_SPACE_ID),
"--output_format", "markdown",
"--output_portal", "huggingface",
"--feature_mapping", json.dumps(feature_mapping),
"--label_mapping", json.dumps(label_mapping),
"--scan_config", "./config.yaml",
]
eval_str = f"[{m_id}]<{d_id}({config}, {split} set)>"
start = time.time()
logging.info(f"Start local evaluation on {eval_str}")
evaluator = subprocess.Popen(
command,
cwd=os.path.join(os.path.dirname(os.path.realpath(__file__)), "cicd"),
stderr=subprocess.STDOUT,
)
result = evaluator.wait()
logging.info(f"Finished local evaluation exit code {result} on {eval_str}: {time.time() - start:.2f}s")
gr.Info(f"Finished local evaluation exit code {result} on {eval_str}: {time.time() - start:.2f}s")
else:
gr.Info("TODO: Submit task to an endpoint")
return gr.update(interactive=True) # Submit button
with gr.Blocks(theme=theme) as iface:
with gr.Tab("Text Classification"):
def check_dataset_and_get_config(dataset_id):
try:
configs = datasets.get_dataset_config_names(dataset_id)
return gr.Dropdown(configs, value=configs[0], visible=True)
except Exception:
# Dataset may not exist
pass
def check_dataset_and_get_split(dataset_config, dataset_id):
try:
splits = list(datasets.load_dataset(dataset_id, dataset_config).keys())
return gr.Dropdown(splits, value=splits[0], visible=True)
except Exception as e:
# Dataset may not exist
gr.Warning(f"Failed to load dataset {dataset_id} with config {dataset_config}: {e}")
pass
def gate_validate_btn(model_id, dataset_id, dataset_config, dataset_split, id2label_mapping_dataframe=None, feature_mapping_dataframe=None):
column_mapping = '{}'
_, ppl = check_model(model_id=model_id)
if id2label_mapping_dataframe is not None:
labels = convert_column_mapping_to_json(id2label_mapping_dataframe.value, label="data")
features = convert_column_mapping_to_json(feature_mapping_dataframe.value, label="text")
column_mapping = json.dumps({**labels, **features}, indent=2)
if check_column_mapping_keys_validity(column_mapping, ppl) is False:
gr.Warning('Label mapping table has invalid contents. Please check again.')
return (gr.update(interactive=False),
gr.update(),
gr.update(),
gr.update(),
gr.update(),
gr.update(),
gr.update())
else:
if model_id and dataset_id and dataset_config and dataset_split:
return try_validate(model_id, ppl, dataset_id, dataset_config, dataset_split, column_mapping)
else:
return (gr.update(interactive=False),
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False))
with gr.Row():
gr.Markdown('''
<h1 style="text-align: center;">
Giskard Evaluator
</h1>
Welcome to Giskard Evaluator Space! Get your report immediately by simply input your model id and dataset id below. Follow our leads and improve your model in no time.
''')
with gr.Row():
run_local = gr.Checkbox(value=True, label="Run in this Space")
use_inference = read_inference_type('./config.yaml')[0] == 'hf_inference_api'
run_inference = gr.Checkbox(value=use_inference, label="Run with Inference API")
with gr.Row() as advanced_row:
selected = read_scanners('./config.yaml')
scan_config = selected + ['data_leakage']
scanners = gr.CheckboxGroup(choices=scan_config, value=selected, label='Scan Settings', visible=True)
with gr.Row():
model_id_input = gr.Textbox(
label="Hugging Face model id",
placeholder="cardiffnlp/twitter-roberta-base-sentiment-latest",
)
dataset_id_input = gr.Textbox(
label="Hugging Face Dataset id",
placeholder="tweet_eval",
)
with gr.Row():
dataset_config_input = gr.Dropdown(['default'], value='default', label='Dataset Config', visible=False)
dataset_split_input = gr.Dropdown(['default'], value='default', label='Dataset Split', visible=False)
dataset_id_input.blur(check_dataset_and_get_config, dataset_id_input, dataset_config_input)
dataset_id_input.submit(check_dataset_and_get_config, dataset_id_input, dataset_config_input)
dataset_config_input.blur(
check_dataset_and_get_split,
inputs=[dataset_config_input, dataset_id_input],
outputs=[dataset_split_input])
with gr.Row(visible=True) as loading_row:
gr.Markdown('''
<p style="text-align: center;">
🚀🐢Please validate your model and dataset first...
</p>
''')
with gr.Row(visible=False) as preview_row:
gr.Markdown('''
<h1 style="text-align: center;">
Confirm Pre-processing Details
</h1>
Base on your model and dataset, we inferred this label mapping and feature mapping. <b>If the mapping is incorrect, please modify it in the table below.</b>
''')
with gr.Row():
id2label_mapping_dataframe = gr.DataFrame(label="Preview of label mapping", interactive=True, visible=False)
feature_mapping_dataframe = gr.DataFrame(label="Preview of feature mapping", interactive=True, visible=False)
with gr.Row():
example_input = gr.Markdown('Sample Input: ', visible=False)
with gr.Row():
example_labels = gr.Label(label='Model Prediction Sample', visible=False)
run_btn = gr.Button(
"Get Evaluation Result",
variant="primary",
interactive=False,
size="lg",
)
model_id_input.blur(gate_validate_btn,
inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
outputs=[run_btn, loading_row, preview_row, example_input, example_labels, id2label_mapping_dataframe, feature_mapping_dataframe])
dataset_id_input.blur(gate_validate_btn,
inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
outputs=[run_btn, loading_row, preview_row, example_input, example_labels, id2label_mapping_dataframe, feature_mapping_dataframe])
dataset_config_input.input(gate_validate_btn,
inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
outputs=[run_btn, loading_row, preview_row, example_input, example_labels, id2label_mapping_dataframe, feature_mapping_dataframe])
dataset_split_input.input(gate_validate_btn,
inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
outputs=[run_btn, loading_row, preview_row, example_input, example_labels, id2label_mapping_dataframe, feature_mapping_dataframe])
id2label_mapping_dataframe.input(gate_validate_btn,
inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input, id2label_mapping_dataframe, feature_mapping_dataframe],
outputs=[run_btn, loading_row, preview_row, example_input, example_labels, id2label_mapping_dataframe, feature_mapping_dataframe])
feature_mapping_dataframe.input(gate_validate_btn,
inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input, id2label_mapping_dataframe, feature_mapping_dataframe],
outputs=[run_btn, loading_row, preview_row, example_input, example_labels, id2label_mapping_dataframe, feature_mapping_dataframe])
scanners.change(write_scanners, inputs=scanners)
run_inference.change(
write_inference_type,
inputs=[run_inference]
)
run_btn.click(
try_submit,
inputs=[
model_id_input,
dataset_id_input,
dataset_config_input,
dataset_split_input,
id2label_mapping_dataframe,
feature_mapping_dataframe,
run_local,
],
outputs=[
run_btn,
],
)
with gr.Tab("More"):
pass
if __name__ == "__main__":
iface.queue(max_size=20).launch()