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# imports | |
import gradio as gr | |
from transformers import pipeline | |
# load model via inference pipeline | |
classifier_pipe = pipeline("text-classification", model="LennardZuendorf/legalis-BERT", top_k=None) | |
# function to predict the case winner via the model | |
def predict_fun(text): | |
predictions = classifier_pipe(text) | |
return {p["label"]: p["score"] for p in predictions[0]} | |
# gradio interface as a block setup | |
with gr.Blocks(title='Legalis') as interface: | |
# top row | |
with gr.Row(): | |
gr.Markdown( | |
""" | |
# Legalis BERT Demo | |
Start typing below to see the output. | |
""") | |
# middle row with input text, predict button and output label | |
with gr.Row(): | |
with gr.Column(): | |
input_text = gr.Textbox(label="Case Facts") | |
with gr.Row(): | |
predict = gr.Button("Predict") | |
with gr.Column(): | |
label = gr.Label(label="Predicted Winner") | |
with gr.Row(): | |
interpretation = gr.components.Interpretation(input_text, visible=False) | |
# link predict button to predict function | |
predict.click(predict_fun, input_text, label) | |
# launch command | |
interface.launch() | |