interfaces/landing.py

import gradio as gr

with open('materials/introduction.html', 'r', encoding='utf-8') as file:
    html_description = file.read()

with gr.Blocks() as landing_interface:
    gr.HTML(html_description)
    
    with gr.Accordion("How to run this model locally", open=False):
        gr.Markdown(
            """

            ## Installation

            To use this model, you must install the GLiNER Python library:

            ```

            pip install gliner

            ```

            ## Usage

            Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.

            """
        )
        gr.Code(
            '''

from gliner import GLiNER

model = GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5")

text = "Your text here"

labels = ["person", "award", "date", "competitions", "teams"]

entities = model.predict_entities(text, labels)

for entity in entities:

    print(entity["text"], "=>", entity["label"])

            ''',
            language="python",
        )