app.py

import json
from pathlib import Path
import gradio as gr

from collections import defaultdict
import fsspec.config
import math
from datatrove.io import DataFolder, get_datafolder
from datatrove.utils.stats import MetricStatsDict

BASE_DATA_FOLDER = get_datafolder("s3://fineweb-stats/summary/")


def find_folders(base_folder, path):
    return sorted(
        [
            folder["name"]
            for folder in base_folder.ls(path, detail=True)
            if folder["type"] == "directory" and not folder["name"].rstrip("/") == path
        ]
    )


def find_stats_folders(base_folder: DataFolder):
    # First find all stats-merged.json using globing for stats-merged.json
    stats_merged = base_folder.glob("**/stats-merged.json")

    # Then for each of stats.merged take the all but last two parts of the path (grouping/stat_name)
    stats_folders = [str(Path(x).parent.parent.parent) for x in stats_merged]
    # Finally get the unique paths
    return list(set(stats_folders))


RUNS = sorted(find_stats_folders(BASE_DATA_FOLDER))
print(RUNS)
GROUPS = [Path(x).name for x in find_folders(BASE_DATA_FOLDER, RUNS[0])]
print(GROUPS)
STATS = [
    Path(x).name for x in find_folders(BASE_DATA_FOLDER, str(Path(RUNS[0], GROUPS[0])))
]


def load_stats(path, stat_name, group_by):
    with BASE_DATA_FOLDER.open(
        f"{path}/{group_by}/{stat_name}/stats-merged.json",
        filecache={"cache_storage": "/tmp/files"},
    ) as f:
        json_stat = json.load(f)
        # No idea why this is necessary, but it is, otheriwse the Metric StatsDict is malforme
        return MetricStatsDict() + MetricStatsDict(init=json_stat)


def prepare_non_grouped_data(stats: MetricStatsDict):

    stats_rounded = defaultdict(lambda: 0)
    for key, value in stats.items():
        stats_rounded[float(key)] += value.total
    normalizer = sum(stats_rounded.values())
    normalizer = 1
    stats_rounded = {k: v / normalizer for k, v in stats_rounded.items()}
    return stats_rounded


def prepare_grouped_data(stats: MetricStatsDict, top_k=100):
    means = {key: value.mean for key, value in stats.items()}

    # Take the top_k most frequent keys
    top_keys = sorted(means, key=lambda x: means[x], reverse=True)[:top_k]
    return {key: means[key] for key in top_keys}


import math
import plotly.graph_objects as go
from plotly.offline import plot


def plot_scatter(histograms: dict[str, dict[float, float]], stat_name: str):
    fig = go.Figure()

    colors = iter(
        [
            "rgba(31, 119, 180, 0.5)",
            "rgba(255, 127, 14, 0.5)",
            "rgba(44, 160, 44, 0.5)",
            "rgba(214, 39, 40, 0.5)",
            "rgba(148, 103, 189, 0.5)",
        ]
    )

    for name, histogram in histograms.items():
        if all(isinstance(k, str) for k in histogram.keys()):
            x = [k for k, v in sorted(histogram.items(), key=lambda item: item[1])]
        else:
            x = sorted(histogram.keys())

        y = [histogram[k] for k in x]

        fig.add_trace(
            go.Scatter(x=x, y=y, mode="lines", name=name, line=dict(color=next(colors)))
        )

    fig.update_layout(
        title=f"Line Plots for {stat_name}",
        xaxis_title=stat_name,
        yaxis_title="Frequency",
        xaxis_type="log",
        width=1000,
        height=600,
    )

    return fig


def plot_bars(histograms: dict[str, dict[float, float]], stat_name: str):
    fig = go.Figure()

    for name, histogram in histograms.items():
        x = [k for k, v in sorted(histogram.items(), key=lambda item: item[1])]
        y = [histogram[k] for k in x]

        fig.add_trace(go.Bar(x=x, y=y, name=name))

    fig.update_layout(
        title=f"Bar Plots for {stat_name}",
        xaxis_title=stat_name,
        yaxis_title="Frequency",
        autosize=True,
        width=600,
        height=600,
    )

    return fig


def update_graph(multiselect_crawls, stat_name, grouping):
    if len(multiselect_crawls) <= 0 or not stat_name or not grouping:
        return None
    # Placeholder for logic to rerender the graph based on the inputs
    prepare_fc = (
        prepare_non_grouped_data if grouping == "histogram" else prepare_grouped_data
    )
    graph_fc = plot_scatter if grouping == "histogram" else plot_bars

    print("Loading stats")
    histograms = {
        path: prepare_fc(load_stats(path, stat_name, grouping))
        for path in multiselect_crawls
    }

    print("Plotting")
    return graph_fc(histograms, stat_name)


# Create the Gradio interface
with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=2):
            # Define the multiselect for crawls
            multiselect_crawls = gr.Dropdown(
                choices=RUNS,
                label="Multiselect for crawls",
                multiselect=True,
            )
        with gr.Column(scale=1):
            # Define the dropdown for stat_name
            stat_name_dropdown = gr.Dropdown(
                choices=STATS,
                label="Stat name",
                multiselect=False,
            )
            # Define the dropdown for grouping
            grouping_dropdown = gr.Dropdown(
                choices=GROUPS,
                label="Grouping",
                multiselect=False,
            )
            update_button = gr.Button("Update Graph", variant="primary")
    with gr.Row():
        # Define the graph output
        graph_output = gr.Plot(label="Graph")

    update_button.click(
        fn=update_graph,
        inputs=[multiselect_crawls, stat_name_dropdown, grouping_dropdown],
        outputs=graph_output,
    )


# Launch the application
if __name__ == "__main__":
    demo.launch()