Update app.py

app.py

@@ -1,40 +1,23 @@
 import gradio as gr
 
-# import torch
-import numpy as np
+from transformers import pipeline
 from PIL import Image
 import requests
-from transformers import AutoImageProcessor, AutoModelForDepthEstimation
 
 def greet(name):
+    from transformers import pipeline
+    from PIL import Image
+    import requests
     
+    # load pipe
+    pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-small-hf")
     
-    
-    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    # load image
+    url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
     image = Image.open(requests.get(url, stream=True).raw)
     
-    image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-small-hf")
-    model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-small-hf")
-    
-    # prepare image for the model
-    inputs = image_processor(images=image, return_tensors="pt")
-    
-    with torch.no_grad():
-        outputs = model(**inputs)
-        predicted_depth = outputs.predicted_depth
-    
-    # interpolate to original size
-    prediction = torch.nn.functional.interpolate(
-        predicted_depth.unsqueeze(1),
-        size=image.size[::-1],
-        mode="bicubic",
-        align_corners=False,
-    )
-    
-    # visualize the prediction
-    output = prediction.squeeze().cpu().numpy()
-    formatted = (output * 255 / np.max(output)).astype("uint8")
-    depth = Image.fromarray(formatted)
+    # inference
+    depth = pipe(image)["depth"]
     return name+": " + depth 
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")