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import gradio as gr | |
theme = gr.themes.Soft( | |
primary_hue="green", | |
) | |
with gr.Blocks(theme=theme) as iface: | |
with gr.Row(): | |
with gr.Column(): | |
model_id_input = gr.Textbox( | |
label="Hugging Face model id", | |
placeholder="cardiffnlp/twitter-roberta-base-sentiment-latest", | |
) | |
model_type = gr.Dropdown( | |
label="Hugging Face model type", | |
choices=[ | |
("Auto-detect", 0), | |
("Text Classification", 1), | |
], | |
value=0, | |
) | |
with gr.Column(): | |
dataset_id_input = gr.Textbox( | |
label="Hugging Face dataset id", | |
placeholder="tweet_eval", | |
) | |
gr.Dropdown( | |
label="Hugging Face dataset subset", | |
) | |
gr.Dropdown( | |
label="Hugging Face dataset split", | |
) | |
iface.queue(max_size=20) | |
iface.launch() | |