app.py

import gradio as gr
from autogluon.multimodal import MultiModalPredictor


def text_embedding(query: str):
    model_name = "sentence-transformers/all-MiniLM-L6-v2"

    predictor = MultiModalPredictor(
        pipeline="feature_extraction",
        hyperparameters={
            "model.hf_text.checkpoint_name": model_name
        }
    )
    query_embedding = predictor.extract_embedding([query])
    return query_embedding["0"]   


def main():
    with gr.Blocks(title="OpenSearch Demo") as demo:
        gr.Markdown("# Text Embedding for Search Queries")
        gr.Markdown("Ask an open question!")
        with gr.Row():
            inp = gr.Textbox(show_label=False)
        with gr.Row():    
            btn = gr.Button("Generate Embedding")
        with gr.Row():
            out = gr.DataFrame(label="Embedding", show_label=True)
        
        btn.click(fn=text_embedding, inputs=inp, outputs=out)  
    
    demo.launch()   


if __name__ == "__main__":
    main()