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()