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| from transformers import pipeline | |
| import gradio as gr | |
| import logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| pipelines = { | |
| 'small': pipeline('zero-shot-classification', model='MoritzLaurer/deberta-v3-xsmall-zeroshot-v1.1-all-33'), | |
| 'base': pipeline('zero-shot-classification', model='MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33'), | |
| 'large': pipeline('zero-shot-classification', model='MoritzLaurer/deberta-v3-large-zeroshot-v1.1-all-33') | |
| } | |
| def infer(text, classes, multi_label, model_size): | |
| try: | |
| output = pipelines[model_size](text, classes, multi_label=multi_label) | |
| logger.info(f"Model size: {model_size}, Output: {output}") | |
| return dict(zip(output['labels'], output['scores'])) | |
| except Exception as e: | |
| logger.error(f"Error: {e}") | |
| return {} | |
| text_input = gr.Textbox(lines=5, placeholder='Once upon a time...', label='Text Source', show_label=True) | |
| class_input = gr.Textbox(value='positive, negative', label='Class Label', show_label=True, info='Use commas (,) to seperate classes') | |
| allow_multi_label = gr.Checkbox(value=True, label='Multiple True Classes') | |
| model_sizes = gr.Radio(choices=['small', 'base', 'large'], value='base', label='Model Sizes', show_label=True) | |
| app = gr.Interface(fn=infer, inputs=[text_input, class_input, allow_multi_label, model_sizes], outputs='label') | |
| app.launch() |