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 GLiClass Python library:

            ```

            !pip install gliclass

            ```

         

            ## Usage

            Once you've downloaded the GLiClass library, you can import the GLiClassModel and ZeroShotClassificationPipeline classes.

            """
        )
        gr.Code(
            '''

from gliclass import GLiClassModel, ZeroShotClassificationPipeline

from transformers import AutoTokenizer



model = GLiClassModel.from_pretrained("knowledgator/gliclass-small-v1")

tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-small-v1")



pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')



text = "One day I will see the world!"

labels = ["travel", "dreams", "sport", "science", "politics"]

results = pipeline(text, labels, threshold=0.5)[0] #because we have one text



for result in results:

    print(result["label"], "=>", result["score"])

            ''',
            language="python",
        )