app.py

import gradio as gr

from src.TextSummarizer.pipeline.prediction import PredictionPipeline


def predict(document):
    """
    Method will take the document and summarize it.
    """

    # predict the summary using my own pre-trained model.
    summary = PredictionPipeline().predict(document)
    return summary


# Create the frontend.
input_interfaces: list = []

with gr.Blocks(theme=gr.themes.Soft()) as app:
    with gr.Row():
        gr.Label("Text Summarizer...")

    with gr.Row():
        gr.Markdown("Type in your document which you wanna summarize...")

with gr.Row():
    with gr.Column():
        input_text_box = gr.Textbox(
            label="Document",
            info="Example text",
            lines=20,
            value="Wikipedia is a free, open content online encyclopedia created through the collaborative effort of a community of users known as Wikipedians. Anyone registered on the site can create an article for publication; registration is not required to edit articles.",
        )
    with gr.Column():
        output_text_box = gr.Label("summary")

input_interfaces.append(input_text_box)


with gr.Row():
    predict_but = gr.Button("Predict")


# Add the button actions.
predict_but.click(predict, inputs=input_interfaces, outputs=output_text_box)

app.launch()