File size: 3,224 Bytes
2f4d877
841e241
 
 
460930f
841e241
30a0c61
2f4d877
841e241
 
ca2b34f
651545d
 
 
ca2b34f
651545d
ca2b34f
651545d
ca2b34f
 
 
 
841e241
 
30a0c61
daff9c0
841e241
 
 
 
 
 
 
 
 
 
 
2f4d877
460930f
841e241
 
 
 
30a0c61
841e241
 
 
 
 
 
2f4d877
841e241
 
 
 
 
 
 
2f4d877
 
 
841e241
 
 
 
 
 
 
 
0a4c821
 
841e241
 
bd64e7a
841e241
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1c1cb58
841e241
 
1c1cb58
841e241
 
8f7c83f
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
import asyncio

import gradio as gr
import pandas as pd
from huggingface_hub import HfFileSystem

import src.constants as constants
from src.hub import load_details_file


def update_task_description_component(task):
    base_description = constants.TASK_DESCRIPTIONS.get(task, "")
    additional_info = "A higher score is a better score."
    description = f"{base_description}\n\n{additional_info}" if base_description else additional_info
    return gr.Textbox(
        description,
        label="Task Description",
        lines=5,
        visible=True,
    )


def update_subtasks_component(task):
    return gr.Radio(
        constants.SUBTASKS.get(task),
        info="Evaluation subtasks to be loaded",
        value=None,
    )


def update_load_details_component(model_id_1, model_id_2, subtask):
    if (model_id_1 or model_id_2) and subtask:
        return gr.Button("Load Details", interactive=True)
    else:
        return gr.Button("Load Details", interactive=False)


async def load_details_dataframe(model_id, subtask):
    fs = HfFileSystem()
    if not model_id or not subtask:
        return
    model_name_sanitized = model_id.replace("/", "__")
    paths = fs.glob(
        f"{constants.DETAILS_DATASET_ID}/**/{constants.DETAILS_FILENAME}".format(
            model_name_sanitized=model_name_sanitized, subtask=subtask
        )
    )
    if not paths:
        return
    path = max(paths)
    data = await load_details_file(path)
    df = pd.json_normalize(data)
    # df = df.rename_axis("Parameters", axis="columns")
    df["model_name"] = model_id  # Keep model_name
    return df
    # return df.set_index(pd.Index([model_id])).reset_index()


async def load_details_dataframes(subtask, *model_ids):
    result = await asyncio.gather(*[load_details_dataframe(model_id, subtask) for model_id in model_ids])
    return result


def display_details(sample_idx, *dfs):
    rows = [df.iloc[sample_idx] for df in dfs if "model_name" in df.columns and sample_idx < len(df)]
    if not rows:
        return
    # Pop model_name and add it to the column name
    df = pd.concat([row.rename(row.pop("model_name")) for row in rows], axis="columns")
    # Wrap long strings to avoid overflow; e.g. URLs in "doc.Websites visited_NEV_2"
    df = df.apply(lambda x: x.str.wrap(140) if x.dtype == "object" else x)
    return (
        df.style
        .format(escape="html", na_rep="")
        # .hide(axis="index")
        .to_html()
    )


def update_sample_idx_component(*dfs):
    maximum = max([len(df) - 1 for df in dfs])
    return gr.Number(
        label="Sample Index",
        info="Index of the sample to be displayed",
        value=0,
        minimum=0,
        maximum=maximum,
        visible=True,
    )


def clear_details():
    # model_id_1, model_id_2, details_dataframe_1, details_dataframe_2, details_task, subtask, load_details_btn, sample_idx
    return (
        None, None, None, None, None, None,
        gr.Button("Load Details", interactive=False),
        gr.Number(label="Sample Index", info="Index of the sample to be displayed", value=0, minimum=0,visible=False),
    )


def display_loading_message_for_details():
    return "<h3 style='text-align: center;'>Loading...</h3>"