app.py

import gradio as gr
from flwr_datasets import FederatedDataset
from flwr_datasets.partitioner import (
    DirichletPartitioner,
    IidPartitioner,
    PathologicalPartitioner,
    ShardPartitioner,
    LinearPartitioner,
    SquarePartitioner,
    ExponentialPartitioner,
    NaturalIdPartitioner
)
from flwr_datasets.visualization import plot_label_distributions
import matplotlib.pyplot as plt

partitioner_types = {
    "DirichletPartitioner": DirichletPartitioner,
    "IidPartitioner": IidPartitioner,
    "PathologicalPartitioner": PathologicalPartitioner,
    "ShardPartitioner": ShardPartitioner,
    "LinearPartitioner": LinearPartitioner,
    "SquarePartitioner": SquarePartitioner,
    "ExponentialPartitioner": ExponentialPartitioner,
    "NaturalIdPartitioner": NaturalIdPartitioner,
}

partitioner_parameters = {
    "DirichletPartitioner": ["num_partitions", "alpha", "partition_by", "min_partition_size", "self_balancing"],
    "IidPartitioner": ["num_partitions"],
    "PathologicalPartitioner": ["num_partitions", "partition_by", "num_classes_per_partition", "class_assignment_mode"],
    "ShardPartitioner": ["num_partitions", "partition_by", "num_shards_per_partition", "shard_size", "keep_incomplete_shard"],
    "NaturalIdPartitioner": ["partition_by"],
    "LinearPartitioner": ["num_partitions"],
    "SquarePartitioner": ["num_partitions"],
    "ExponentialPartitioner": ["num_partitions"],
}

def update_parameter_visibility(partitioner_type):
    required_params = partitioner_parameters.get(partitioner_type, [])
    updates = []
    # For num_partitions_input
    if "num_partitions" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    # For alpha_input
    if "alpha" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    # For partition_by_input
    if "partition_by" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    # For min_partition_size_input
    if "min_partition_size" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    # For self_balancing_input
    if "self_balancing" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    # For num_classes_per_partition_input
    if "num_classes_per_partition" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    # For class_assignment_mode_input
    if "class_assignment_mode" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    # For num_shards_per_partition_input
    if "num_shards_per_partition" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    # For shard_size_input
    if "shard_size" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    # For keep_incomplete_shard_input
    if "keep_incomplete_shard" in required_params:
        updates.append(gr.update(visible=True))
    else:
        updates.append(gr.update(visible=False))
    return updates

def partition_and_plot(
    dataset,
    partitioner_type,
    num_partitions,
    alpha,
    partition_by,
    min_partition_size,
    self_balancing,
    num_classes_per_partition,
    class_assignment_mode,
    num_shards_per_partition,
    shard_size,
    keep_incomplete_shard,
    label_name,
    title,
    legend,
    verbose_labels,
    size_unit,
    partition_id_axis,
):
    partitioner_params = {}
    try:
        if partitioner_type == "DirichletPartitioner":
            partitioner_params = {
                "num_partitions": int(num_partitions),
                "partition_by": partition_by,
                "alpha": float(alpha),
                "min_partition_size": int(min_partition_size),
                "self_balancing": self_balancing,
            }
        elif partitioner_type == "IidPartitioner":
            partitioner_params = {
                "num_partitions": int(num_partitions),
            }
        elif partitioner_type == "PathologicalPartitioner":
            partitioner_params = {
                "num_partitions": int(num_partitions),
                "partition_by": partition_by,
                "num_classes_per_partition": int(num_classes_per_partition),
                "class_assignment_mode": class_assignment_mode,
            }
        elif partitioner_type == "ShardPartitioner":
            partitioner_params = {
                "num_partitions": int(num_partitions),
                "partition_by": partition_by,
                "num_shards_per_partition": int(num_shards_per_partition),
                "shard_size": int(shard_size),
                "keep_incomplete_shard": keep_incomplete_shard == "True",
            }
        elif partitioner_type == "NaturalIdPartitioner":
            partitioner_params = {
                "partition_by": partition_by,
            }
        elif partitioner_type in ["LinearPartitioner", "SquarePartitioner", "ExponentialPartitioner"]:
            partitioner_params = {
                "num_partitions": int(num_partitions),
            }

        partitioner_class = partitioner_types[partitioner_type]
        partitioner = partitioner_class(**partitioner_params)
        fds = FederatedDataset(
            dataset=dataset,
            partitioners={
                "train": partitioner,
            },
            trust_remote_code=True,
        )
        partitioner = fds.partitioners["train"]
        figure, axis, dataframe = plot_label_distributions(
            partitioner=partitioner,
            label_name=label_name,
            title=title,
            legend=legend,
            verbose_labels=verbose_labels,
            size_unit=size_unit,
            partition_id_axis=partition_id_axis,
        )

        # Save plot to a file
        plot_filename = "label_distribution.png"
        figure.savefig(plot_filename, bbox_inches='tight')

        # Generate the code
        partitioner_params_str = "\n"
        n_params = len(partitioner_params)
        i = 0
        for k, v in partitioner_params.items():
            if isinstance(v, str):
                v = f'"{v}"'
            if i != (n_params - 1):
                partitioner_params_str = partitioner_params_str + f"\t{k} = {v},\n"    
            else:
                partitioner_params_str = partitioner_params_str + f"\t{k} = {v}\n" 
            i +=1

        code = f"""
from flwr_datasets import FederatedDataset
from flwr_datasets.partitioner import {partitioner_type}
from flwr_datasets.visualization import plot_label_distributions

partitioner = {partitioner_type}({partitioner_params_str})
fds = FederatedDataset(
    dataset="{dataset}",
    partitioners={{
        "train": partitioner,
    }},
    trust_remote_code=True,
)
partitioner = fds.partitioners["train"]
figure, axis, dataframe = plot_label_distributions(
    partitioner=partitioner,
    label_name="label",
    title="{title}",
    legend={legend},
    verbose_labels={verbose_labels},
    size_unit="{size_unit}",
    partition_id_axis="{partition_id_axis}",
)
    """
        return plot_filename, code#, plot_filename # with df: plot_filename, code, dataframe, plot_filename
    except Exception as e:
        # Return error messages
        error_message = str(e)
        return None, f"Error: {error_message}", None, None

with gr.Blocks() as demo:
    gr.Markdown("# Federated Dataset: Partitioning Visualization")
    gr.Markdown("See partitioned datasets for Federated Learning experiments. The partitioning and visualization were created using `flwr-datasets`. To open in a new tab, click the [link](https://huggingface.co/spaces/flwrlabs/federated-learning-datasets-by-flwr-datasets).")

    with gr.Row():
        with gr.Column(scale=1):
            # gr.Markdown("## Federated Dataset Parameters")
            with gr.Accordion("Federated Dataset Parameters", open=True):
                dataset_input = gr.Textbox(label="Dataset", value="cifar10")
                partitioner_type_input = gr.Dropdown(label="Partitioner", choices=list(partitioner_types.keys()), value="DirichletPartitioner")
                num_partitions_input = gr.Number(label="num_partitions", value=10, visible=True)
                alpha_input = gr.Number(label="alpha", value=0.3, visible=True)
                partition_by_input = gr.Textbox(label="partition_by", value="label", visible=True)
                min_partition_size_input = gr.Number(label="min_partition_size", value=0, visible=True)
                self_balancing_input = gr.Radio(label="self_balancing", choices=[True, False], value=False, visible=True)
    
                num_classes_per_partition_input = gr.Number(label="num_classes_per_partition", value=2, visible=False)
                class_assignment_mode_input = gr.Dropdown(label="class_assignment_mode", choices=["random", "first-deterministic", "deterministic"], value="first-deterministic", visible=False)
                num_shards_per_partition_input = gr.Number(label="num_shards_per_partition", value=2, visible=False)
                shard_size_input = gr.Number(label="shard_size", value=0, visible=False)
                keep_incomplete_shard_input = gr.Radio(label="keep_incomplete_shard", choices=["True", "False"], value="True", visible=False)
            with gr.Accordion("Plot Parameters", open=False):
                label_name = gr.Textbox(label="label_name", value="label")
                title = gr.Textbox(label="title", value="Per Partition Label Distribution")
                # legend_title = gr.Textbox(label="legend_title", value=None)
                legend = gr.Radio(label="legend", choices=[True, False], value=True)
                verbose_labels = gr.Radio(label="verbose_labels", choices=[True, False], value=True)
                size_unit = gr.Radio(label="size_unit", choices=["absolute", "percent"], value="absolute")
                partition_id_axis = gr.Radio(label="partition_id_axis", choices=["x", "y"], value="x")
                
            
            
            # Update parameter visibility when partitioner_type_input changes
            partitioner_type_input.change(
                fn=update_parameter_visibility,
                inputs=[partitioner_type_input],
                outputs=[
                    num_partitions_input,
                    alpha_input,
                    partition_by_input,
                    min_partition_size_input,
                    self_balancing_input,
                    num_classes_per_partition_input,
                    class_assignment_mode_input,
                    num_shards_per_partition_input,
                    shard_size_input,
                    keep_incomplete_shard_input
                ]
            )    
        with gr.Column(scale=3, min_width=480):
            gr.Markdown("## Label Distribution Plot")
            plot_output = gr.Image(label="Label Distribution Plot")
            submit_button = gr.Button("Partition and Plot", variant="primary")
            # download_button = gr.DownloadButton(label="Download Plot", value="label_distribution.png")
            gr.Markdown("## Code")
            code_output = gr.Code(label="Code", language="python")
            # Uncomment to show dataframe (note that it only works with header that is of type "string")
            # gr.Markdown("## Partitioning DataFrame")
            # dataframe_output = gr.Dataframe(label="Partitioning DataFrame")
    size_skew_examples = gr.Examples(
        examples=[
            ["cifar10", "IidPartitioner", 10],
            ["cifar10", "LinearPartitioner", 10],
            ["cifar10", "SquarePartitioner", 10],
            ["cifar10", "ExponentialPartitioner", 10],
        ],
        inputs=[
            dataset_input,
            partitioner_type_input,
            num_partitions_input,
        ],
        label="Size Skew Examples",
    )
    
    dirichlet_examples = gr.Examples(
        examples=[
            ["cifar10", "DirichletPartitioner", 10, 0.1, "label", 0, False, "absolute"],
            ["cifar10", "DirichletPartitioner", 10, 0.1, "label", 0, False, "percent"],
        ],
        inputs=[
            dataset_input,
            partitioner_type_input,
            num_partitions_input,
            alpha_input,
            partition_by_input,
            min_partition_size_input,
            self_balancing_input,
            size_unit,
        ],
        label="Dirichlet Examples",
    )

    pathological_examples = gr.Examples(
        examples=[
            ["cifar10", "PathologicalPartitioner", 10, 2, "first-deterministic", "label"],
            ["cifar10", "PathologicalPartitioner", 10, 3, "deterministic", "label"],
        ],
        inputs=[
            dataset_input,
            partitioner_type_input,
            num_partitions_input,
            num_classes_per_partition_input,
            class_assignment_mode_input,
            partition_by_input,
        ],
        label="Pathological Examples",
    )
    markdown = gr.Markdown("See more tutorial, examples and documentation on [https://flower.ai/docs/datasets/index.html](https://flower.ai/docs/datasets/index.html).")

    # Set up the event handler for the submit_button
    submit_button.click(
        fn=partition_and_plot,
        inputs=[
            dataset_input,
            partitioner_type_input,
            num_partitions_input,
            alpha_input,
            partition_by_input,
            min_partition_size_input,
            self_balancing_input,
            num_classes_per_partition_input,
            class_assignment_mode_input,
            num_shards_per_partition_input,
            shard_size_input,
            keep_incomplete_shard_input,
            label_name,
            title,
            legend,
            verbose_labels,
            size_unit,
            partition_id_axis,
        ],
        outputs=[
            plot_output,
            code_output,
            # dataframe_output,
            # download_button
        ]
    )

if __name__ == "__main__":
    demo.launch()