Update app.py

app.py

@@ -1,18 +1,40 @@
 import gradio as gr
-from transformers import pipeline
+
+import torch
+import numpy as np
 from PIL import Image
 import requests
+from transformers import AutoImageProcessor, AutoModelForDepthEstimation
 
 def greet(name):
-    # load pipe
-    pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-small-hf")
-
-    # load image
-    url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+    
+    
+    
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
     image = Image.open(requests.get(url, stream=True).raw)
-
-    # inference
-    depth = pipe(image)["depth"]
+    
+    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)
     return name+": " + depth 
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")