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import gradio as gr
from transformers import AutoModel, AutoConfig
from main_idea_with_torch import predict_mainidea_sent_old
from main_idea_with_pipeline import predict_mainidea_sent
config = AutoConfig.from_pretrained("yutingg/custom-distill-bert-for-sentence-label", trust_remote_code=True)
model = AutoModel.from_pretrained("yutingg/custom-distill-bert-for-sentence-label", trust_remote_code=True, config=config)
def predict_main_idea(essay):
ret = predict_mainidea_sent(essay, model), predict_mainidea_sent_old(essay, model)
return ret
with gr.Blocks() as main_idea_demo:
with gr.Row():
essay_input = gr.Textbox(label="essay", lines=10)
with gr.Row():
predict_button = gr.Button("Predict Main Idea Sentence")
with gr.Row():
with gr.Column(scale=1, min_width=600):
output_1 = gr.Dataframe(
label="pipeline output",
headers=['label: is main idea', 'sentence'],
datatype=["str", "str"],
col_count=(2, "fixed"),
)
with gr.Column(scale=1, min_width=600):
output_2 = gr.Dataframe(
label="torch output with Triage",
headers=['label: is main idea', 'sentence'],
datatype=["str", "str"],
col_count=(2, "fixed"),
)
predict_button.click(predict_main_idea, inputs=essay_input, outputs=[output_1, output_2])
main_idea_demo.launch()
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