Spaces:
Sleeping
Sleeping
File size: 9,939 Bytes
3573a39 8f809e2 3573a39 8f809e2 3573a39 8f809e2 3573a39 8f809e2 5704515 8f809e2 1afc3d2 58c39e0 a89f9d8 58c39e0 3573a39 8f809e2 3573a39 e163df8 a0a107f 8f809e2 a89f9d8 8092547 8f809e2 1c00552 8f809e2 3573a39 8f809e2 58c39e0 1c00552 58c39e0 1c00552 58c39e0 3573a39 1c00552 58c39e0 1c00552 58c39e0 a89f9d8 136af2d 3573a39 8f809e2 1c00552 8f809e2 58c39e0 8f809e2 1c00552 8f809e2 041cafd 136af2d 8f809e2 3573a39 8f809e2 3573a39 8f809e2 3573a39 8f809e2 3573a39 8f809e2 3573a39 8f809e2 3573a39 8f809e2 3573a39 8f809e2 3573a39 8f809e2 1c00552 8f809e2 3573a39 8f809e2 3573a39 8f809e2 3573a39 8f809e2 1c00552 8f809e2 3573a39 8f809e2 3573a39 8f809e2 5704515 8f809e2 3573a39 8f809e2 3573a39 8f809e2 136af2d 8f809e2 afd881d 58c39e0 afd881d 3573a39 8f809e2 1c00552 8f809e2 1784f11 3573a39 1afc3d2 1c00552 8f809e2 a0a107f e163df8 a0a107f 8f809e2 affded7 3573a39 8f809e2 3573a39 8f809e2 3573a39 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 |
import collections
import json
import logging
import os
import threading
import datasets
import gradio as gr
from transformers.pipelines import TextClassificationPipeline
from wordings import get_styled_input
from io_utils import (get_yaml_path, read_column_mapping, save_job_to_pipe,
write_column_mapping, write_inference_type,
write_log_to_user_file)
from text_classification import (check_model, get_example_prediction,
get_labels_and_features_from_dataset)
from wordings import (CHECK_CONFIG_OR_SPLIT_RAW,
CONFIRM_MAPPING_DETAILS_FAIL_RAW,
MAPPING_STYLED_ERROR_WARNING)
MAX_LABELS = 20
MAX_FEATURES = 20
HF_REPO_ID = "HF_REPO_ID"
HF_SPACE_ID = "SPACE_ID"
HF_WRITE_TOKEN = "HF_WRITE_TOKEN"
HF_GSK_HUB_URL = "GSK_HUB_URL"
HF_GSK_HUB_PROJECT_KEY = "GSK_HUB_PROJECT_KEY"
HF_GSK_HUB_KEY = "GSK_API_KEY"
HF_GSK_HUB_HF_TOKEN = "GSK_HF_TOKEN"
HF_GSK_HUB_UNLOCK_TOKEN = "GSK_HUB_UNLOCK_TOKEN"
def check_dataset_and_get_config(dataset_id):
try:
# write_column_mapping(None, uid) # reset column mapping
configs = datasets.get_dataset_config_names(dataset_id)
return gr.Dropdown(configs, value=configs[0], visible=True)
except Exception:
# Dataset may not exist
pass
def check_dataset_and_get_split(dataset_id, dataset_config):
try:
splits = list(datasets.load_dataset(dataset_id, dataset_config).keys())
return gr.Dropdown(splits, value=splits[0], visible=True)
except Exception:
# Dataset may not exist
# gr.Warning(f"Failed to load dataset {dataset_id} with config {dataset_config}: {e}")
pass
def select_run_mode(run_inf, inf_token, uid):
if run_inf:
if len(inf_token) > 0:
write_inference_type(run_inf, inf_token, uid)
return (gr.update(visible=True), gr.update(value=False))
else:
return (gr.update(visible=False), gr.update(value=True))
def deselect_run_inference(run_local):
if run_local:
return (gr.update(visible=False), gr.update(value=False))
else:
return (gr.update(visible=True), gr.update(value=True))
def write_column_mapping_to_config(
dataset_id, dataset_config, dataset_split, uid, *labels
):
# TODO: Substitute 'text' with more features for zero-shot
# we are not using ds features because we only support "text" for now
ds_labels, _ = get_labels_and_features_from_dataset(
dataset_id, dataset_config, dataset_split
)
if labels is None:
return
all_mappings = dict()
if "labels" not in all_mappings.keys():
all_mappings["labels"] = dict()
for i, label in enumerate(labels[:MAX_LABELS]):
if label:
all_mappings["labels"][label] = ds_labels[i % len(ds_labels)]
if "features" not in all_mappings.keys():
all_mappings["features"] = dict()
for _, feat in enumerate(labels[MAX_LABELS : (MAX_LABELS + MAX_FEATURES)]):
if feat:
# TODO: Substitute 'text' with more features for zero-shot
all_mappings["features"]["text"] = feat
write_column_mapping(all_mappings, uid)
def list_labels_and_features_from_dataset(ds_labels, ds_features, model_id2label):
model_labels = list(model_id2label.values())
len_model_labels = len(model_labels)
lables = [
gr.Dropdown(
label=f"{label}",
choices=model_labels,
value=model_id2label[i % len_model_labels],
interactive=True,
visible=True,
)
for i, label in enumerate(ds_labels[:MAX_LABELS])
]
lables += [gr.Dropdown(visible=False) for _ in range(MAX_LABELS - len(lables))]
# TODO: Substitute 'text' with more features for zero-shot
features = [
gr.Dropdown(
label=f"{feature}",
choices=ds_features,
value=ds_features[0],
interactive=True,
visible=True,
)
for feature in ["text"]
]
features += [
gr.Dropdown(visible=False) for _ in range(MAX_FEATURES - len(features))
]
return lables + features
def check_model_and_show_prediction(
model_id, dataset_id, dataset_config, dataset_split
):
ppl = check_model(model_id)
if ppl is None or not isinstance(ppl, TextClassificationPipeline):
gr.Warning("Please check your model.")
return (
gr.update(visible=False),
gr.update(visible=False),
*[gr.update(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)],
)
dropdown_placement = [
gr.Dropdown(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)
]
if ppl is None: # pipeline not found
gr.Warning("Model not found")
return (
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False, open=False),
*dropdown_placement,
)
model_id2label = ppl.model.config.id2label
ds_labels, ds_features = get_labels_and_features_from_dataset(
dataset_id, dataset_config, dataset_split
)
# when dataset does not have labels or features
if not isinstance(ds_labels, list) or not isinstance(ds_features, list):
gr.Warning(CHECK_CONFIG_OR_SPLIT_RAW)
return (
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=False, open=False),
*dropdown_placement,
)
column_mappings = list_labels_and_features_from_dataset(
ds_labels,
ds_features,
model_id2label,
)
# when labels or features are not aligned
# show manually column mapping
if (
collections.Counter(model_id2label.values()) != collections.Counter(ds_labels)
or ds_features[0] != "text"
):
return (
gr.update(value=MAPPING_STYLED_ERROR_WARNING, visible=True),
gr.update(visible=False),
gr.update(visible=True, open=True),
*column_mappings,
)
prediction_input, prediction_output = get_example_prediction(
ppl, dataset_id, dataset_config, dataset_split
)
return (
gr.update(value=get_styled_input(prediction_input), visible=True),
gr.update(value=prediction_output, visible=True),
gr.update(visible=True, open=False),
*column_mappings,
)
def try_submit(m_id, d_id, config, split, local, uid):
all_mappings = read_column_mapping(uid)
if all_mappings is None:
gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
return (gr.update(interactive=True), gr.update(visible=False))
if "labels" not in all_mappings.keys():
gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
return (gr.update(interactive=True), gr.update(visible=False))
label_mapping = {}
for i, label in zip(
range(len(all_mappings["labels"].keys())), all_mappings["labels"].keys()
):
label_mapping.update({str(i): label})
if "features" not in all_mappings.keys():
gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
return (gr.update(interactive=True), gr.update(visible=False))
feature_mapping = all_mappings["features"]
leaderboard_dataset = None
if os.environ.get("SPACE_ID") == "giskardai/giskard-evaluator":
leaderboard_dataset = "ZeroCommand/test-giskard-report"
# TODO: Set column mapping for some dataset such as `amazon_polarity`
if local:
command = [
"giskard_scanner",
"--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(feature_mapping),
"--label_mapping",
json.dumps(label_mapping),
"--scan_config",
get_yaml_path(uid),
"--leaderboard_dataset",
leaderboard_dataset,
]
if os.environ.get(HF_GSK_HUB_KEY):
command.append("--giskard_hub_api_key")
command.append(os.environ.get(HF_GSK_HUB_KEY))
if os.environ.get(HF_GSK_HUB_URL):
command.append("--giskard_hub_url")
command.append(os.environ.get(HF_GSK_HUB_URL))
if os.environ.get(HF_GSK_HUB_PROJECT_KEY):
command.append("--giskard_hub_project_key")
command.append(os.environ.get(HF_GSK_HUB_PROJECT_KEY))
if os.environ.get(HF_GSK_HUB_HF_TOKEN):
command.append("--giskard_hub_hf_token")
command.append(os.environ.get(HF_GSK_HUB_HF_TOKEN))
if os.environ.get(HF_GSK_HUB_UNLOCK_TOKEN):
command.append("--giskard_hub_unlock_token")
command.append(os.environ.get(HF_GSK_HUB_UNLOCK_TOKEN))
eval_str = f"[{m_id}]<{d_id}({config}, {split} set)>"
logging.info(f"Start local evaluation on {eval_str}")
save_job_to_pipe(uid, command, eval_str, threading.Lock())
write_log_to_user_file(
uid,
f"Start local evaluation on {eval_str}. Please wait for your job to start...\n",
)
gr.Info(f"Start local evaluation on {eval_str}")
return (
gr.update(interactive=False),
gr.update(lines=5, visible=True, interactive=False),
)
else:
gr.Info("TODO: Submit task to an endpoint")
return (gr.update(interactive=True), gr.update(visible=False)) # Submit button
|