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import gradio as gr |
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import matplotlib.pyplot as plt |
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from inference import RelationsInference |
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from utils import KGType,Model_Type |
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examples = [["What's the meaning of life?", "eli5", "constraint"], |
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["boat, water, bird", "commongen", "constraint"], |
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["What flows under a bridge?", "commonsense_qa", "constraint"]] |
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bart = RelationsInference( |
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model_path='MrVicente/commonsense_bart_commongen', |
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kg_type=KGType.CONCEPTNET, |
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model_type=Model_Type.RELATIONS, |
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max_length=32 |
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) |
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def infer_bart(context, task_type, decoding_type_str): |
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response, encoder_attentions, model_input = bart.generate_based_on_context(context, use_kg=False) |
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return response[0] |
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def plot_attention(layer, head): |
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fig = plt.figure() |
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plt.plot([1, 2, 3], [2, 4, 6]) |
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plt.title("Things") |
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plt.ylabel("Cases") |
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plt.xlabel("Days since Day 0") |
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return fig |
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app = gr.Blocks() |
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with app: |
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gr.Markdown( |
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""" |
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# Demo |
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### Test Commonsense Relation-Aware BART (BART-RA) model |
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Tutorial: <br> |
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1) Select the possible model variations and tasks;<br> |
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2) Change the inputs and Click the buttons to produce results;<br> |
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3) See attention visualisations, by choosing a specific layer and head;<br> |
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""") |
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with gr.Row(): |
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context_input = gr.Textbox(lines=2, value="What's the meaning of life?", label='Input:') |
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model_result_output = gr.Textbox(lines=2, label='Model result:') |
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with gr.Column(): |
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task_type_choice = gr.Radio( |
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["eli5", "commongen"], value="eli5", label="What task do you want to try?" |
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) |
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decoding_type_choice = gr.Radio( |
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["default", "constraint"], value="default", label="What decoding strategy do you want to use?" |
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) |
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with gr.Row(): |
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model_btn = gr.Button(value="See Model Results") |
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gr.Markdown( |
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""" |
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--- |
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Observe Attention |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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layer = gr.Slider(0, 11, 0, step=1, label="Layer") |
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head = gr.Slider(0, 15, 0, step=1, label="Head") |
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with gr.Column(): |
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plot_output = gr.Plot() |
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with gr.Row(): |
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vis_btn = gr.Button(value="See Attention Scores") |
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model_btn.click(fn=infer_bart, inputs=[context_input, task_type_choice, decoding_type_choice], |
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outputs=[model_result_output]) |
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vis_btn.click(fn=plot_attention, inputs=[layer, head], outputs=[plot_output]) |
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if __name__ == '__main__': |
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app.launch() |
