Create app.py

app.py

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+import os
+import gradio as gr
+from transformers import pipeline
+import torch
+import numpy as np
+from PIL import Image
+
+# Load the depth estimation model
+depth_estimator = pipeline(task="depth-estimation", model="Intel/dpt-hybrid-midas")
+
+# Function to process the image and return depth map
+def launch(input_image):
+    out = depth_estimator(input_image)
+
+    predicted_depth = out["predicted_depth"].view(1, 1, 480, 640)  # Assuming single image.
+
+    # Resize the prediction to match the raw image size (H, W).
+    prediction = torch.nn.functional.interpolate(
+        predicted_depth,
+        size=input_image.size[::-1],  # Match raw image size (H, W).
+        mode="bicubic",
+        align_corners=False,
+    )
+
+    # Normalize the prediction
+    output = prediction.squeeze().numpy()
+    formatted = (output * 255 / np.max(output)).astype("uint8")
+    depth = Image.fromarray(formatted)
+    return depth
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=launch, 
+    inputs=gr.Image(type='pil'), 
+    outputs=gr.Image(type='pil')
+)
+
+# Launch the interface
+iface.launch()