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 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 ) 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 ) # 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=True), # Column mapping ) # 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 = \ 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: 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=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(value=column_mapping, visible=True, interactive=True), # Column mapping ) 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=False), # Label mapping preview # gr.update(value=column_mapping, visible=True, interactive=True), # Column mapping ) 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 ) def try_submit(m_id, d_id, config, split, column_mappings, local): label_mapping = {} try: column_mapping = json.loads(column_mappings) if "label" in column_mapping: label_mapping = column_mapping.pop("label", {}) except Exception: column_mapping = {} 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(column_mapping), "--label_mapping", json.dumps(label_mapping), ] 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): column_mapping = '{}' m_id, ppl = check_model(model_id=model_id) if id2label_mapping_dataframe is not None: column_mapping = id2label_mapping_dataframe.to_json(orient="split") 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()) else: if model_id and dataset_id and dataset_config and dataset_split: return try_validate(m_id, ppl, dataset_id, dataset_config, dataset_split, column_mapping) else: del ppl 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)) with gr.Row(): gr.Markdown('''

Giskard Evaluator

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") 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.change(check_dataset_and_get_config, dataset_id_input, dataset_config_input) dataset_config_input.change( 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('''

Please validate your model and dataset first...

''') with gr.Row(visible=False) as preview_row: gr.Markdown('''

Confirm Label Details

Base on your model and dataset, we inferred this label mapping. **If the mapping is incorrect, please modify it in the table below.** ''') with gr.Row(): id2label_mapping_dataframe = gr.DataFrame(label="Preview of label 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.change(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]) dataset_id_input.change(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]) dataset_config_input.change(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]) dataset_split_input.change(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]) id2label_mapping_dataframe.input(gate_validate_btn, inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input, id2label_mapping_dataframe], outputs=[run_btn, loading_row, preview_row, example_input, example_labels, id2label_mapping_dataframe]) run_btn.click( try_submit, inputs=[ model_id_input, dataset_id_input, dataset_config_input, dataset_split_input, id2label_mapping_dataframe, run_local, ], outputs=[ run_btn, ], ) with gr.Tab("More"): pass if __name__ == "__main__": iface.queue(max_size=20).launch()