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1aa43b4
1
Parent(s):
79d57ff
change space ui and loading dynamically (#6)
Browse files- change space ui and loading dynamically (a8753de99eeb0183a0cded2dbf46b3710665268c)
Co-authored-by: zcy <ZeroCommand@users.noreply.huggingface.co>
app.py
CHANGED
@@ -31,7 +31,7 @@ def check_model(model_id):
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try:
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from transformers import pipeline
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ppl = pipeline(task=task, model=model_id)
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-
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return model_id, ppl
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except Exception as e:
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return model_id, e
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@@ -59,7 +59,6 @@ def check_dataset(dataset_id, dataset_config="default", dataset_split="test"):
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return dataset_id, None, None
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return dataset_id, dataset_config, dataset_split
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-
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def try_validate(model_id, dataset_id, dataset_config, dataset_split, column_mapping):
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# Validate model
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m_id, ppl = check_model(model_id=model_id)
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@@ -144,7 +143,8 @@ def try_validate(model_id, dataset_id, dataset_config, dataset_split, column_map
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gr.Info("Model and dataset validations passed. Your can submit the evaluation task.")
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return (
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-
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gr.update(interactive=True), # Submit button
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gr.update(value=prediction_result, visible=True), # Model prediction preview
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gr.update(value=id2label_df, visible=True), # Label mapping preview
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@@ -199,52 +199,86 @@ def try_submit(m_id, d_id, config, split, column_mappings, local):
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with gr.Blocks(theme=theme) as iface:
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with gr.
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model_id_input = gr.Textbox(
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label="Hugging Face model id",
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placeholder="
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)
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# TODO: Add supported model pairs: Text Classification - text-classification
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model_type = gr.Dropdown(
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label="Hugging Face model type",
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choices=[
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("Auto-detect", 0),
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("Text Classification", 1),
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],
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value=0,
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)
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example_labels = gr.Label(label='Model prediction result', visible=False)
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id2label_mapping_dataframe = gr.DataFrame(
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label="Preview of label mapping",
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visible=False,
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)
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with gr.Column():
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dataset_id_input = gr.Textbox(
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label="Hugging Face
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placeholder="
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)
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dataset_split_input = gr.Dropdown(
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label="Hugging Face dataset split",
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choices=[
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"test",
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],
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allow_custom_value=True,
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value="test",
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)
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with gr.Accordion("Advance", open=False):
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run_local = gr.Checkbox(value=True, label="Run in this Space")
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@@ -259,10 +293,8 @@ with gr.Blocks(theme=theme) as iface:
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'}',
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)
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with gr.Row():
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validate_btn = gr.Button("Validate model and dataset", variant="primary")
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run_btn = gr.Button(
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"
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variant="primary",
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interactive=False,
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)
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@@ -273,17 +305,17 @@ with gr.Blocks(theme=theme) as iface:
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dataset_id_input,
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dataset_config_input,
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dataset_split_input,
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column_mapping_input,
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],
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outputs=[
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run_btn,
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example_labels,
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id2label_mapping_dataframe,
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column_mapping_input,
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],
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)
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run_btn.click(
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try_submit,
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inputs=[
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@@ -299,5 +331,8 @@ with gr.Blocks(theme=theme) as iface:
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],
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)
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-
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try:
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from transformers import pipeline
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ppl = pipeline(task=task, model=model_id)
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return model_id, ppl
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except Exception as e:
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return model_id, e
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return dataset_id, None, None
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return dataset_id, dataset_config, dataset_split
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def try_validate(model_id, dataset_id, dataset_config, dataset_split, column_mapping):
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# Validate model
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m_id, ppl = check_model(model_id=model_id)
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gr.Info("Model and dataset validations passed. Your can submit the evaluation task.")
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return (
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gr.update(visible=False), # Loading row
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gr.update(visible=True), # Preview row
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gr.update(interactive=True), # Submit button
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gr.update(value=prediction_result, visible=True), # Model prediction preview
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gr.update(value=id2label_df, visible=True), # Label mapping preview
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with gr.Blocks(theme=theme) as iface:
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with gr.Tab("Text Classification"):
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global_ds_id = gr.State('ds')
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def check_dataset_and_get_config(dataset_id):
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global_ds_id.value = dataset_id
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try:
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configs = datasets.get_dataset_config_names(dataset_id)
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print(configs)
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return gr.Dropdown(configs, value=configs[0], visible=True)
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except Exception:
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# Dataset may not exist
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pass
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def check_dataset_and_get_split(choice):
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print('choice: ',choice, global_ds_id.value)
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try:
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splits = list(datasets.load_dataset(global_ds_id.value, choice).keys())
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print('splits: ',splits)
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return gr.Dropdown(splits, value=splits[0], visible=True)
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except Exception as e:
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# Dataset may not exist
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print(e)
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pass
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def gate_validate_btn(model_id, dataset_id, dataset_config, dataset_split):
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print('model_id: ',model_id)
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if model_id and dataset_id and dataset_config and dataset_split:
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return gr.update(interactive=True)
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else:
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return gr.update(interactive=False)
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with gr.Row():
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model_id_input = gr.Textbox(
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label="Hugging Face model id",
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placeholder="cardiffnlp/twitter-roberta-base-sentiment-latest",
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)
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dataset_id_input = gr.Textbox(
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label="Hugging Face Dataset id",
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placeholder="tweet_eval",
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)
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with gr.Row():
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dataset_config_input = gr.Dropdown(['default'], value=['default'], label='Dataset Config', visible=False)
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dataset_split_input = gr.Dropdown(['default'], value=['default'], label='Dataset Split', visible=False)
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dataset_id_input.change(check_dataset_and_get_config, dataset_id_input, dataset_config_input)
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dataset_config_input.change(check_dataset_and_get_split, dataset_config_input, dataset_split_input)
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with gr.Row():
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validate_btn = gr.Button("Validate Model and Dataset", variant="primary", interactive=False)
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model_id_input.change(gate_validate_btn,
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inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
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outputs=[validate_btn])
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dataset_id_input.change(gate_validate_btn,
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inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
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outputs=[validate_btn])
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dataset_config_input.change(gate_validate_btn,
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inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
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outputs=[validate_btn])
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dataset_split_input.change(gate_validate_btn,
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inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input],
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outputs=[validate_btn])
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with gr.Row(visible=True) as loading_row:
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gr.Markdown('''
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<h1 style="text-align: center;">
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Please validate your model and dataset first...
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</h1>
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''')
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with gr.Row(visible=False) as preview_row:
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with gr.Column():
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id2label_mapping_dataframe = gr.DataFrame(label="Preview of label mapping")
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gr.Markdown('''
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<span style="background-color:#5fc269; color:white">Does this look right? If not, Check and update your feature mapping -></span>
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''')
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example_labels = gr.Label(label='Model Prediction Sample')
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with gr.Accordion("Advance", open=False):
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run_local = gr.Checkbox(value=True, label="Run in this Space")
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'}',
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)
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run_btn = gr.Button(
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"Get Evaluation Result",
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variant="primary",
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interactive=False,
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)
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dataset_id_input,
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dataset_config_input,
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dataset_split_input,
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],
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outputs=[
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loading_row,
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preview_row,
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run_btn,
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example_labels,
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id2label_mapping_dataframe,
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column_mapping_input,
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],
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)
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run_btn.click(
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try_submit,
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inputs=[
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],
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)
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with gr.Tab("More"):
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pass
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if __name__ == "__main__":
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iface.queue(max_size=20).launch()
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