import gradio as gr
from fastembed import SparseTextEmbedding

def sparseembed(docs):
    model = SparseTextEmbedding(model_name="Qdrant/bm25")
    embeddings = list(model.embed(docs))
    # преобразуем x.values и x.indices в list
    return [ (x.values.tolist(), x.indices.tolist()) for x in embeddings ]

iface = gr.Interface(
    fn=sparseembed,
    inputs=[
        gr.JSON(label="Docs (JSON array of objects)")
    ],
    outputs=gr.Dataframe(type="array", headers=["values", "indices"]),
    api_name="rerank"
)

if __name__ == "__main__":
    iface.launch()