Spaces:
Sleeping
Sleeping
GSK-2352 create a leaderboard tab
#15
by
ZeroCommand
- opened
- app.py +6 -4
- app_leaderboard.py +98 -0
- app_legacy.py +1 -1
- app_text_classification.py +64 -26
- cicd +1 -0
- fetch_utils.py +26 -0
- utils.py β io_utils.py +9 -10
- wordings.py +8 -3
app.py
CHANGED
@@ -5,11 +5,13 @@
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import gradio as gr
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from app_text_classification import get_demo as get_demo_text_classification
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-
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with gr.Blocks(theme=gr.themes.Soft(primary_hue="green")) as demo:
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with gr.Tab("Text Classification"):
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get_demo_text_classification()
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with gr.Tab("Leaderboard
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import gradio as gr
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from app_text_classification import get_demo as get_demo_text_classification
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from app_leaderboard import get_demo as get_demo_leaderboard
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with gr.Blocks(theme=gr.themes.Soft(primary_hue="green")) as demo:
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with gr.Tab("Text Classification"):
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get_demo_text_classification()
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with gr.Tab("Leaderboard"):
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get_demo_leaderboard()
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demo.queue(max_size=100)
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demo.launch(share=False)
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app_leaderboard.py
CHANGED
@@ -0,0 +1,98 @@
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import gradio as gr
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import datasets
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import logging
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from fetch_utils import check_dataset_and_get_config, check_dataset_and_get_split
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def get_records_from_dataset_repo(dataset_id):
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dataset_config = check_dataset_and_get_config(dataset_id)
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logging.info(f"Dataset {dataset_id} has configs {dataset_config}")
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dataset_split = check_dataset_and_get_split(dataset_id, dataset_config[0])
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logging.info(f"Dataset {dataset_id} has splits {dataset_split}")
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try:
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ds = datasets.load_dataset(dataset_id, dataset_config[0])[dataset_split[0]]
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df = ds.to_pandas()
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return df
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except Exception as e:
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logging.warning(f"Failed to load dataset {dataset_id} with config {dataset_config}: {e}")
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return None
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def get_model_ids(ds):
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logging.info(f"Dataset {ds} column names: {ds['model_id']}")
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models = ds['model_id'].tolist()
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# return unique elements in the list model_ids
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model_ids = list(set(models))
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return model_ids
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def get_dataset_ids(ds):
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logging.info(f"Dataset {ds} column names: {ds['dataset_id']}")
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datasets = ds['dataset_id'].tolist()
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dataset_ids = list(set(datasets))
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return dataset_ids
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def get_types(ds):
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# set all types for each column
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types = [str(t) for t in ds.dtypes.to_list()]
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types = [t.replace('object', 'markdown') for t in types]
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types = [t.replace('float64', 'number') for t in types]
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types = [t.replace('int64', 'number') for t in types]
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return types
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def get_display_df(df):
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# style all elements in the model_id column
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display_df = df.copy()
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columns = display_df.columns.tolist()
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if 'model_id' in columns:
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display_df['model_id'] = display_df['model_id'].apply(lambda x: f'<p href="https://huggingface.co/{x}" style="color:blue">π{x}</p>')
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# style all elements in the dataset_id column
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if 'dataset_id' in columns:
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display_df['dataset_id'] = display_df['dataset_id'].apply(lambda x: f'<p href="https://huggingface.co/datasets/{x}" style="color:blue">π{x}</p>')
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# style all elements in the report_link column
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if 'report_link' in columns:
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display_df['report_link'] = display_df['report_link'].apply(lambda x: f'<p href="{x}" style="color:blue">π{x}</p>')
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return display_df
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def get_demo():
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records = get_records_from_dataset_repo('ZeroCommand/test-giskard-report')
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model_ids = get_model_ids(records)
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dataset_ids = get_dataset_ids(records)
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column_names = records.columns.tolist()
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default_columns = ['model_id', 'dataset_id', 'total_issues', 'report_link']
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# set the default columns to show
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default_df = records[default_columns]
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types = get_types(default_df)
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display_df = get_display_df(default_df)
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with gr.Row():
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task_select = gr.Dropdown(label='Task', choices=['text_classification', 'tabular'], value='text_classification', interactive=True)
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model_select = gr.Dropdown(label='Model id', choices=model_ids, interactive=True)
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dataset_select = gr.Dropdown(label='Dataset id', choices=dataset_ids, interactive=True)
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with gr.Row():
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columns_select = gr.CheckboxGroup(label='Show columns', choices=column_names, value=default_columns, interactive=True)
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with gr.Row():
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leaderboard_df = gr.DataFrame(display_df, datatype=types, interactive=False)
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@gr.on(triggers=[model_select.change, dataset_select.change, columns_select.change, task_select.change],
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inputs=[model_select, dataset_select, columns_select, task_select],
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outputs=[leaderboard_df])
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def filter_table(model_id, dataset_id, columns, task):
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# filter the table based on task
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df = records[(records['task'] == task)]
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# filter the table based on the model_id and dataset_id
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if model_id:
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df = records[(records['model_id'] == model_id)]
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if dataset_id:
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df = records[(records['dataset_id'] == dataset_id)]
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# filter the table based on the columns
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df = df[columns]
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types = get_types(df)
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display_df = get_display_df(df)
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return (
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gr.update(value=display_df, datatype=types, interactive=False)
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)
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app_legacy.py
CHANGED
@@ -11,7 +11,7 @@ import json
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from transformers.pipelines import TextClassificationPipeline
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from text_classification import check_column_mapping_keys_validity, text_classification_fix_column_mapping
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from
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from wordings import CONFIRM_MAPPING_DETAILS_MD, CONFIRM_MAPPING_DETAILS_FAIL_MD
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HF_REPO_ID = 'HF_REPO_ID'
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from transformers.pipelines import TextClassificationPipeline
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from text_classification import check_column_mapping_keys_validity, text_classification_fix_column_mapping
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from io_utils import read_scanners, write_scanners, read_inference_type, write_inference_type, convert_column_mapping_to_json
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from wordings import CONFIRM_MAPPING_DETAILS_MD, CONFIRM_MAPPING_DETAILS_FAIL_MD
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HF_REPO_ID = 'HF_REPO_ID'
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app_text_classification.py
CHANGED
@@ -4,14 +4,15 @@ import os
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import time
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import subprocess
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import logging
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import json
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from transformers.pipelines import TextClassificationPipeline
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from text_classification import get_labels_and_features_from_dataset, check_model, get_example_prediction
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from
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from wordings import
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HF_REPO_ID = 'HF_REPO_ID'
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HF_SPACE_ID = 'SPACE_ID'
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def get_demo():
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with gr.Row():
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gr.Markdown(
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with gr.Row():
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model_id_input = gr.Textbox(
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label="Hugging Face model id",
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example_prediction = gr.Label(label='Model Prediction Sample', visible=False)
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with gr.Row():
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with gr.Accordion(label='Model Wrap Advance Config (optional)', open=False):
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run_local = gr.Checkbox(value=True, label="Run in this Space")
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all_mappings["features"][feat] = ds_features[i]
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write_column_mapping(all_mappings)
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def list_labels_and_features_from_dataset(
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ds_labels, ds_features = get_labels_and_features_from_dataset(dataset_id, dataset_config, dataset_split)
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if ds_labels is None or ds_features is None:
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return [gr.Dropdown(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)]
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model_labels = list(model_id2label.values())
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lables = [gr.Dropdown(label=f"{label}", choices=model_labels, value=model_id2label[i], interactive=True, visible=True) for i, label in enumerate(ds_labels[:MAX_LABELS])]
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lables += [gr.Dropdown(visible=False) for _ in range(MAX_LABELS - len(lables))]
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-
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features += [gr.Dropdown(visible=False) for _ in range(MAX_FEATURES - len(features))]
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return lables + features
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@gr.on(triggers=[model_id_input.change, dataset_config_input.change, dataset_split_input.change],
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inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
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outputs=[example_input, example_prediction, *column_mappings])
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def check_model_and_show_prediction(model_id, dataset_id, dataset_config, dataset_split):
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ppl = check_model(model_id)
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if ppl is None or not isinstance(ppl, TextClassificationPipeline):
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gr.update(visible=False),
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*[gr.update(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)]
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)
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model_id2label = ppl.model.config.id2label
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-
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column_mappings = list_labels_and_features_from_dataset(
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model_id2label,
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model_features
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)
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return (
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gr.update(visible=False),
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gr.update(visible=False),
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*column_mappings
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)
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prediction_input, prediction_output = get_example_prediction(ppl, dataset_id, dataset_config, dataset_split)
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return (
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gr.update(value=prediction_input, visible=True),
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gr.update(value=prediction_output, visible=True),
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*column_mappings
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)
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check_dataset_and_get_split,
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inputs=[dataset_id_input, dataset_config_input],
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outputs=[dataset_split_input])
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gr.on(
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triggers=[
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run_btn.click,
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import time
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import subprocess
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import logging
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import collections
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import json
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from transformers.pipelines import TextClassificationPipeline
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from text_classification import get_labels_and_features_from_dataset, check_model, get_example_prediction
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from io_utils import read_scanners, write_scanners, read_inference_type, read_column_mapping, write_column_mapping, write_inference_type
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from wordings import INTRODUCTION_MD, CONFIRM_MAPPING_DETAILS_MD, CONFIRM_MAPPING_DETAILS_FAIL_RAW
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HF_REPO_ID = 'HF_REPO_ID'
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HF_SPACE_ID = 'SPACE_ID'
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def get_demo():
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with gr.Row():
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gr.Markdown(INTRODUCTION_MD)
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with gr.Row():
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model_id_input = gr.Textbox(
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label="Hugging Face model id",
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example_prediction = gr.Label(label='Model Prediction Sample', visible=False)
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with gr.Row():
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with gr.Accordion(label='Label and Feature Mapping', visible=False, open=False) as column_mapping_accordion:
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with gr.Row():
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gr.Markdown(CONFIRM_MAPPING_DETAILS_MD)
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column_mappings = []
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with gr.Row():
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with gr.Column():
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for _ in range(MAX_LABELS):
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column_mappings.append(gr.Dropdown(visible=False))
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with gr.Column():
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for _ in range(MAX_LABELS, MAX_LABELS + MAX_FEATURES):
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column_mappings.append(gr.Dropdown(visible=False))
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with gr.Accordion(label='Model Wrap Advance Config (optional)', open=False):
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run_local = gr.Checkbox(value=True, label="Run in this Space")
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all_mappings["features"][feat] = ds_features[i]
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write_column_mapping(all_mappings)
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def list_labels_and_features_from_dataset(ds_labels, ds_features, model_id2label):
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model_labels = list(model_id2label.values())
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lables = [gr.Dropdown(label=f"{label}", choices=model_labels, value=model_id2label[i], interactive=True, visible=True) for i, label in enumerate(ds_labels[:MAX_LABELS])]
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lables += [gr.Dropdown(visible=False) for _ in range(MAX_LABELS - len(lables))]
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# TODO: Substitute 'text' with more features for zero-shot
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features = [gr.Dropdown(label=f"{feature}", choices=ds_features, value=ds_features[0], interactive=True, visible=True) for feature in ['text']]
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features += [gr.Dropdown(visible=False) for _ in range(MAX_FEATURES - len(features))]
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return lables + features
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@gr.on(triggers=[model_id_input.change, dataset_config_input.change, dataset_split_input.change],
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inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
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outputs=[example_input, example_prediction, column_mapping_accordion, *column_mappings])
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def check_model_and_show_prediction(model_id, dataset_id, dataset_config, dataset_split):
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ppl = check_model(model_id)
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if ppl is None or not isinstance(ppl, TextClassificationPipeline):
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gr.update(visible=False),
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*[gr.update(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)]
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)
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dropdown_placement = [gr.Dropdown(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)]
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if ppl is None: # pipeline not found
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gr.Warning("Model not found")
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return (
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gr.update(visible=False),
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gr.update(visible=False),
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gr.update(visible=False, open=False),
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*dropdown_placement
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)
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model_id2label = ppl.model.config.id2label
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ds_labels, ds_features = get_labels_and_features_from_dataset(dataset_id, dataset_config, dataset_split)
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# when dataset does not have labels or features
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if not isinstance(ds_labels, list) or not isinstance(ds_features, list):
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gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
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return (
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gr.update(visible=False),
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gr.update(visible=False),
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gr.update(visible=False, open=False),
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*dropdown_placement
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)
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column_mappings = list_labels_and_features_from_dataset(
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ds_labels,
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ds_features,
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model_id2label,
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)
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# when labels or features are not aligned
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# show manually column mapping
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if collections.Counter(model_id2label.items()) != collections.Counter(ds_labels) or ds_features[0] != 'text':
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gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
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return (
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gr.update(visible=False),
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gr.update(visible=False),
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gr.update(visible=True, open=True),
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*column_mappings
|
237 |
)
|
238 |
+
|
239 |
prediction_input, prediction_output = get_example_prediction(ppl, dataset_id, dataset_config, dataset_split)
|
240 |
return (
|
241 |
gr.update(value=prediction_input, visible=True),
|
242 |
gr.update(value=prediction_output, visible=True),
|
243 |
+
gr.update(visible=True, open=False),
|
244 |
*column_mappings
|
245 |
)
|
246 |
|
|
|
250 |
check_dataset_and_get_split,
|
251 |
inputs=[dataset_id_input, dataset_config_input],
|
252 |
outputs=[dataset_split_input])
|
253 |
+
|
254 |
+
scanners.change(
|
255 |
+
write_scanners,
|
256 |
+
inputs=scanners
|
257 |
+
)
|
258 |
+
|
259 |
+
run_inference.change(
|
260 |
+
write_inference_type,
|
261 |
+
inputs=[run_inference]
|
262 |
+
)
|
263 |
+
|
264 |
gr.on(
|
265 |
triggers=[
|
266 |
run_btn.click,
|
cicd
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
Subproject commit 24d96209fb943568e001d582999345e2c58e0876
|
fetch_utils.py
ADDED
@@ -0,0 +1,26 @@
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1 |
+
import huggingface_hub
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+
import datasets
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+
import logging
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+
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5 |
+
def check_dataset_and_get_config(dataset_id):
|
6 |
+
try:
|
7 |
+
configs = datasets.get_dataset_config_names(dataset_id)
|
8 |
+
return configs
|
9 |
+
except Exception:
|
10 |
+
# Dataset may not exist
|
11 |
+
return None
|
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+
|
13 |
+
def check_dataset_and_get_split(dataset_id, dataset_config):
|
14 |
+
try:
|
15 |
+
ds = datasets.load_dataset(dataset_id, dataset_config)
|
16 |
+
except Exception as e:
|
17 |
+
# Dataset may not exist
|
18 |
+
logging.warning(f"Failed to load dataset {dataset_id} with config {dataset_config}: {e}")
|
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+
return None
|
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+
try:
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+
splits = list(ds.keys())
|
22 |
+
return splits
|
23 |
+
except Exception as e:
|
24 |
+
# Dataset has no splits
|
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+
logging.warning(f"Dataset {dataset_id} with config {dataset_config} has no splits: {e}")
|
26 |
+
return None
|
utils.py β io_utils.py
RENAMED
@@ -17,13 +17,13 @@ def read_scanners(path):
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17 |
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18 |
# convert a list of scanners to yaml file
|
19 |
def write_scanners(scanners):
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20 |
-
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|
21 |
config = yaml.load(f, Loader=yaml.FullLoader)
|
22 |
-
|
23 |
-
|
24 |
-
|
25 |
-
|
26 |
-
yaml.dump(config, f, Dumper=Dumper)
|
27 |
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28 |
# read model_type from yaml file
|
29 |
def read_inference_type(path):
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@@ -35,15 +35,14 @@ def read_inference_type(path):
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35 |
|
36 |
# write model_type to yaml file
|
37 |
def write_inference_type(use_inference):
|
38 |
-
with open(YAML_PATH, "r") as f:
|
39 |
config = yaml.load(f, Loader=yaml.FullLoader)
|
40 |
if use_inference:
|
41 |
config["inference_type"] = 'hf_inference_api'
|
42 |
else:
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43 |
config["inference_type"] = 'hf_pipeline'
|
44 |
-
|
45 |
-
|
46 |
-
yaml.dump(config, f, Dumper=Dumper)
|
47 |
|
48 |
# read column mapping from yaml file
|
49 |
def read_column_mapping(path):
|
|
|
17 |
|
18 |
# convert a list of scanners to yaml file
|
19 |
def write_scanners(scanners):
|
20 |
+
print(scanners)
|
21 |
+
with open(YAML_PATH, "r+") as f:
|
22 |
config = yaml.load(f, Loader=yaml.FullLoader)
|
23 |
+
if config:
|
24 |
+
config["detectors"] = scanners
|
25 |
+
# save scanners to detectors in yaml
|
26 |
+
yaml.dump(config, f, Dumper=Dumper)
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|
27 |
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28 |
# read model_type from yaml file
|
29 |
def read_inference_type(path):
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|
35 |
|
36 |
# write model_type to yaml file
|
37 |
def write_inference_type(use_inference):
|
38 |
+
with open(YAML_PATH, "r+") as f:
|
39 |
config = yaml.load(f, Loader=yaml.FullLoader)
|
40 |
if use_inference:
|
41 |
config["inference_type"] = 'hf_inference_api'
|
42 |
else:
|
43 |
config["inference_type"] = 'hf_pipeline'
|
44 |
+
# save inference_type to inference_type in yaml
|
45 |
+
yaml.dump(config, f, Dumper=Dumper)
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|
|
46 |
|
47 |
# read column mapping from yaml file
|
48 |
def read_column_mapping(path):
|
wordings.py
CHANGED
@@ -1,10 +1,15 @@
|
|
1 |
-
|
2 |
<h1 style="text-align: center;">
|
3 |
-
Giskard Evaluator
|
4 |
</h1>
|
5 |
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.
|
6 |
'''
|
7 |
-
|
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|
8 |
CONFIRM_MAPPING_DETAILS_FAIL_MD = '''
|
9 |
<h1 style="text-align: center;">
|
10 |
Confirm Pre-processing Details
|
|
|
1 |
+
INTRODUCTION_MD = '''
|
2 |
<h1 style="text-align: center;">
|
3 |
+
π’Giskard Evaluator
|
4 |
</h1>
|
5 |
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.
|
6 |
'''
|
7 |
+
CONFIRM_MAPPING_DETAILS_MD = '''
|
8 |
+
<h1 style="text-align: center;">
|
9 |
+
Confirm Pre-processing Details
|
10 |
+
</h1>
|
11 |
+
Please confirm the pre-processing details below. If you are not sure, please double check your model and dataset.
|
12 |
+
'''
|
13 |
CONFIRM_MAPPING_DETAILS_FAIL_MD = '''
|
14 |
<h1 style="text-align: center;">
|
15 |
Confirm Pre-processing Details
|