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",
        )