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•
f9983ab
1
Parent(s):
5f9a95f
remove overused warnings & fix wording & prevent un-matchable models and datasets submissions
Browse files- app_text_classification.py +5 -5
- text_classification_ui_helpers.py +16 -4
- wordings.py +9 -1
app_text_classification.py
CHANGED
@@ -2,7 +2,7 @@ import uuid
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import gradio as gr
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-
from io_utils import
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from text_classification_ui_helpers import (
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get_related_datasets_from_leaderboard,
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align_columns_and_show_prediction,
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@@ -11,7 +11,7 @@ from text_classification_ui_helpers import (
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try_submit,
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write_column_mapping_to_config,
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)
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from wordings import CONFIRM_MAPPING_DETAILS_MD, INTRODUCTION_MD, USE_INFERENCE_API_TIP
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MAX_LABELS = 40
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MAX_FEATURES = 20
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@@ -114,8 +114,8 @@ def get_demo():
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with gr.Row():
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logs = gr.Textbox(
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value=
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label="Giskard Bot Evaluation
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visible=False,
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every=0.5,
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)
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@@ -135,7 +135,7 @@ def get_demo():
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)
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gr.on(
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triggers=[dataset_id_input.
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fn=check_dataset,
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inputs=[dataset_id_input],
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outputs=[dataset_config_input, dataset_split_input, loading_status]
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import gradio as gr
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+
from io_utils import read_scanners, write_scanners
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from text_classification_ui_helpers import (
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get_related_datasets_from_leaderboard,
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align_columns_and_show_prediction,
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try_submit,
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write_column_mapping_to_config,
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)
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from wordings import CONFIRM_MAPPING_DETAILS_MD, INTRODUCTION_MD, USE_INFERENCE_API_TIP, CHECK_LOG_SECTION_RAW
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MAX_LABELS = 40
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MAX_FEATURES = 20
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with gr.Row():
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logs = gr.Textbox(
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value=CHECK_LOG_SECTION_RAW,
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label="Giskard Bot Evaluation Guide:",
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visible=False,
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every=0.5,
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)
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)
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gr.on(
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triggers=[dataset_id_input.change],
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fn=check_dataset,
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inputs=[dataset_id_input],
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outputs=[dataset_config_input, dataset_split_input, loading_status]
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text_classification_ui_helpers.py
CHANGED
@@ -23,6 +23,7 @@ from wordings import (
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CONFIRM_MAPPING_DETAILS_FAIL_RAW,
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MAPPING_STYLED_ERROR_WARNING,
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NOT_TEXT_CLASSIFICATION_MODEL_RAW,
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get_styled_input,
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)
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@@ -41,7 +42,7 @@ def get_related_datasets_from_leaderboard(model_id):
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if len(datasets_unique) == 0:
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return gr.update(choices=[], value="")
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return gr.update(choices=datasets_unique, value=
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logger = logging.getLogger(__file__)
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@@ -185,7 +186,7 @@ def precheck_model_ds_enable_example_btn(
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return (gr.update(interactive=True), gr.update(value=df, visible=True), "")
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except Exception as e:
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# Config or split wrong
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return (gr.update(interactive=False), gr.update(value=pd.DataFrame(), visible=False), "")
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@@ -255,6 +256,17 @@ def align_columns_and_show_prediction(
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"",
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*dropdown_placement,
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)
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column_mappings = list_labels_and_features_from_dataset(
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ds_labels,
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@@ -301,10 +313,10 @@ def check_column_mapping_keys_validity(all_mappings):
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def construct_label_and_feature_mapping(all_mappings, ds_labels, ds_features):
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label_mapping = {}
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if len(all_mappings["labels"].keys()) != len(ds_labels):
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-
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if len(all_mappings["features"].keys()) != len(ds_features):
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-
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for i, label in zip(range(len(ds_labels)), ds_labels):
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# align the saved labels with dataset labels order
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CONFIRM_MAPPING_DETAILS_FAIL_RAW,
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MAPPING_STYLED_ERROR_WARNING,
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NOT_TEXT_CLASSIFICATION_MODEL_RAW,
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UNMATCHED_MODEL_DATASET,
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get_styled_input,
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)
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if len(datasets_unique) == 0:
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return gr.update(choices=[], value="")
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return gr.update(choices=datasets_unique, value="")
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logger = logging.getLogger(__file__)
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return (gr.update(interactive=True), gr.update(value=df, visible=True), "")
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except Exception as e:
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# Config or split wrong
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logger.warn(f"Check your dataset {dataset_id} and config {dataset_config} on split {dataset_split}: {e}")
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return (gr.update(interactive=False), gr.update(value=pd.DataFrame(), visible=False), "")
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"",
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*dropdown_placement,
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)
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if len(ds_labels) != len(model_labels):
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gr.Warning(UNMATCHED_MODEL_DATASET)
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return (
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gr.update(visible=False),
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gr.update(visible=False),
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gr.update(visible=False, open=False),
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gr.update(interactive=False),
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"",
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*dropdown_placement,
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)
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column_mappings = list_labels_and_features_from_dataset(
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ds_labels,
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def construct_label_and_feature_mapping(all_mappings, ds_labels, ds_features):
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label_mapping = {}
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if len(all_mappings["labels"].keys()) != len(ds_labels):
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logger.warn("Label mapping corrupted: " + CONFIRM_MAPPING_DETAILS_FAIL_RAW)
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if len(all_mappings["features"].keys()) != len(ds_features):
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logger.warn("Feature mapping corrupted: " + CONFIRM_MAPPING_DETAILS_FAIL_RAW)
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for i, label in zip(range(len(ds_labels)), ds_labels):
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# align the saved labels with dataset labels order
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wordings.py
CHANGED
@@ -17,14 +17,22 @@ CONFIRM_MAPPING_DETAILS_FAIL_MD = """
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Sorry, we cannot align the input/output of your dataset with the model. <b>Pleaser double check your model and dataset.</b>
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"""
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CONFIRM_MAPPING_DETAILS_FAIL_RAW = """
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Sorry, we cannot align the input/output of your dataset with the model.
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"""
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CHECK_CONFIG_OR_SPLIT_RAW = """
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Please check your dataset config or split.
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"""
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PREDICTION_SAMPLE_MD = """
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<h1 style="text-align: center;">
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Model Prediction Sample
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Sorry, we cannot align the input/output of your dataset with the model. <b>Pleaser double check your model and dataset.</b>
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"""
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UNMATCHED_MODEL_DATASET = """
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Model prediction labels do not align with the labels present in the dataset. Please double check your model and dataset.
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"""
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CONFIRM_MAPPING_DETAILS_FAIL_RAW = """
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Sorry, we cannot auto-align the input/output of your dataset with the model.
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"""
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CHECK_CONFIG_OR_SPLIT_RAW = """
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Please check your dataset config or split.
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"""
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CHECK_LOG_SECTION_RAW = """
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Your evaluation job has been successfully submitted. Further details are available in the Logs tab, providing information on your queue status and the current job log.
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"""
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PREDICTION_SAMPLE_MD = """
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<h1 style="text-align: center;">
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Model Prediction Sample
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