app.py

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 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(model_id, dataset_id, dataset_config, dataset_split, column_mapping='{}'):
    # Validate model
    m_id, ppl = check_model(model_id=model_id)
    if m_id is None:
        gr.Warning(f'Model "{model_id}" 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_id} 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)

    del ppl

    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)
                print(configs)
                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())
                print('splits: ',splits)
                return gr.Dropdown(splits, value=splits[0], visible=True)
            except Exception as e:
                # Dataset may not exist
                print(e)
                pass
        
        def gate_validate_btn(model_id, dataset_id, dataset_config, dataset_split, id2label_mapping_dataframe=None):
            print('model_id: ',model_id)
            column_mapping = '{}'
            if id2label_mapping_dataframe is not None:
                column_mapping = id2label_mapping_dataframe.to_json(orient="split")
                print(column_mapping)   
            if model_id and dataset_id and dataset_config and dataset_split:
                return try_validate(model_id, 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))

        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('''
                        <h1 style="text-align: center;">
                        Please validate your model and dataset first...
                        </h1>
                        ''')

        with gr.Row(visible=False) as preview_row:
            gr.Markdown('''
                <h1 style="text-align: center;">
                Confirm Label Details
                </h1>
                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,
            ],
            outputs=[
                run_btn,
            ],
        )

    with gr.Tab("More"):
        pass

if __name__ == "__main__":
    iface.queue(max_size=20).launch()