app.py

from transformers import pipeline
from PIL import Image
import gradio as gr

# Load the Hugging Face depth estimation pipeline
pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-small-hf")

def estimate_depth(image):
    # Perform depth estimation on the input image
    depth = pipe(image)["depth"]
    return depth

# Create a Gradio interface
iface = gr.Interface(
    fn=estimate_depth, 
    inputs=gr.Image(type="pil"), 
    outputs=gr.Image(type="pil"),
    title="Depth Estimation",
    description="Upload an image to get its depth estimation map."
)

# Launch the Gradio app
iface.launch()



""" 
from transformers import pipeline
from PIL import Image
import requests

# load pipe
pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-small-hf")

# load image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

# inference
depth = pipe(image)["depth"]

"""