app.py

# %%
from jinja2 import Environment, FileSystemLoader
import pandas as pd
import gradio as gr

df = pd.read_csv("./data.csv")


def parse_into_jinja_markdown(
    model_name,
    performance,
    accuracy,
    Precision,
    Recall,
    Robustness,
    Fairness,
    Failure_Clusters,
):
    env = Environment(loader=FileSystemLoader("."), autoescape=True)
    temp = env.get_template("mc_template.md")
    return temp.render(
        model_id=model_name,
        accuracy=accuracy,
        Precision=Precision,
        Recall=Recall,
        Robustness=Robustness,
        Fairness=Fairness,
        Performance=performance,
        Failure_Cluster=Failure_Clusters,
    )


def md_builder(model, dataset, displayed_metrics):
    row = df[df["friendly_name"] == model]
    str = ""
    ## f"# <span style='font-size: 16px;'> Model Card for <code style='font-weight: 400'>{model}</code></span>\n"
    ##f"On dataset `{dataset}`\n"

    ## )

    # init vars to empty string
    (
        perform_val,
        accuracy_val,
        precision_val,
        recall_val,
        robustness_val,
        fairness_val,
        fail_cluster,
    ) = ("", "", "", "", "", "", "")

    if "Performance" in displayed_metrics:
        perform_val = f"\nPerformance: `{row['performance'].values[0]}`"

    if "Accuracy" in displayed_metrics:
        accuracy_val = f"\nAccuracy: `{row['accuracy'].values[0]}`"

    if "Precision" in displayed_metrics:
        precision_val = f"\nPrecision: `{row['precision_weighted'].values[0]}`"

    if "Recall" in displayed_metrics:
        recall_val = f"\nRecall: `{row['recall_weighted'].values[0]}`"

    if "Robustness" in displayed_metrics:
        robustness_val = f"\nRobustness: `{100-row['robustness'].values[0]}`"

    if "Fairness" in displayed_metrics:
        fairness_val = f"\nFairness: `{0}`"

    if "Failure Clusters" in displayed_metrics:
        cl_count = row["cluster_count"].values[0]
        fail_cluster = f"\nTop failures: {row['top_failure_cluster'].values[0]} (+{cl_count - 1} others) (details for all {cl_count} clusters)"

    str += "\n<div style='text-align: right'>⛶ Expand safety card</div>"
    str = parse_into_jinja_markdown(
        model,
        perform_val,
        accuracy_val,
        precision_val,
        recall_val,
        robustness_val,
        fairness_val,
        fail_cluster,
    )
    return str


iface = gr.Interface(
    md_builder,
    [
        gr.Dropdown(
            list(df["friendly_name"]),
            label="Model",
            value="ViT",
            info="Select a model to use for testing.",
        ),
        gr.Dropdown(
            ["marmal88/skin_cancer"],
            value="marmal88/skin_cancer",
            label="Dataset",
            info="Select the sampling dataset to use for testing.",
        ),
        gr.CheckboxGroup(
            [
                "Performance",
                "Accuracy",
                "Precision",
                "Recall",
                "Robustness",
                "Fairness",
                "Failure Clusters",
            ],
            value=["Accuracy", "Robustness", "Fairness", "Failure Clusters"],
            label="Metrics",
            info="Select displayed metrics.",
        ),
        # gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        # gr.Dropdown(
        #    ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        # ),
        # gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "markdown",
    examples=[
        [
            "ViT",
            "marmal88/skin_cancer",
            ["Accuracy", "Robustness", "Fairness", "Failure Clusters"],
        ],
    ],
)

iface.launch()

# %%