from transformers_js import import_transformers_js, as_url import gradio as gr transformers = await import_transformers_js() pipeline = transformers.pipeline depth_estimator = await pipeline('depth-estimation', 'Xenova/depth-anything-small-hf'); async def estimate(input_image): output = await depth_estimator(as_url(input_image)) depth_image = output["depth"].to_pil() tensor = output["predicted_depth"] tensor_data = { "dims": tensor.dims, "type": tensor.type, "data": tensor.data, "size": tensor.size, } return depth_image, tensor_data demo = gr.Interface( fn=estimate, inputs=[ gr.Image(type="filepath") ], outputs=[ gr.Image(label="Depth Image"), gr.JSON(label="Tensor"), ], examples=[ ["bread_small.png"] ] ) demo.launch() transformers_js_py