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import argparse |
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from allennlp.models.archival import archive_model, load_archive |
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from allennlp.predictors.text_classifier import TextClassifierPredictor |
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import gradio as gr |
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import platform |
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from project_settings import project_path |
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from toolbox.allennlp.data.dataset_readers.text_classification_json import TextClassificationJsonReader |
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from toolbox.os.command import Command |
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def get_args(): |
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parser = argparse.ArgumentParser() |
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args = parser.parse_args() |
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return args |
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model_names = { |
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"allennlp_text_classification": { |
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"qgyd2021/language_identification": "https://huggingface.co/qgyd2021/language_identification" |
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} |
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} |
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trained_model_dir = project_path / "trained_models/huggingface" |
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trained_model_dir.mkdir(parents=True, exist_ok=True) |
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def click_button_allennlp_text_classification(text: str, model_name: str): |
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model_path = trained_model_dir / model_name |
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if not model_path.exists(): |
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model_path.parent.mkdir(exist_ok=True) |
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Command.cd(model_path.parent.as_posix()) |
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Command.popen("git clone https://huggingface.co/{}".format(model_name)) |
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archive = load_archive(archive_file=model_path.as_posix()) |
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predictor = TextClassifierPredictor( |
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model=archive.model, |
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dataset_reader=archive.dataset_reader, |
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) |
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json_dict = { |
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"sentence": text |
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} |
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outputs = predictor.predict_json( |
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json_dict |
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) |
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label = outputs["label"] |
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probs = outputs["probs"] |
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return label, round(max(probs), 4) |
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def main(): |
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args = get_args() |
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brief_description = """ |
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## NLP Tools |
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NLP Tools Demo |
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""" |
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with gr.Blocks() as blocks: |
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gr.Markdown(value=brief_description) |
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with gr.Tabs(): |
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with gr.TabItem("AllenNLP Text Classification"): |
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with gr.Row(): |
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with gr.Column(scale=3): |
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text = gr.Text(label="text") |
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ground_true = gr.Text(label="ground_true") |
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model_name = gr.Dropdown( |
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choices=list(model_names["allennlp_text_classification"].keys()) |
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) |
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button = gr.Button("infer", variant="primary") |
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with gr.Column(scale=3): |
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label = gr.Text(label="label") |
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prob = gr.Text(label="prob") |
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gr.Examples( |
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examples=[ |
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["你好", "zh", "qgyd2021/language_identification"] |
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], |
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inputs=[text, ground_true, model_name], |
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outputs=[label, prob], |
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) |
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button.click( |
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click_button_allennlp_text_classification, |
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inputs=[text, model_name], |
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outputs=[label, prob] |
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) |
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blocks.queue().launch( |
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share=False if platform.system() == "Windows" else False, |
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server_name="127.0.0.1" if platform.system() == "Windows" else "0.0.0.0", |
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server_port=7860 |
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) |
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return |
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if __name__ == '__main__': |
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main() |
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