Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import depth_pro
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load model and preprocessing transform
+model, transform = depth_pro.create_model_and_transforms()
+model.eval()
+
+def predict_depth(input_image):
+    # Preprocess the image
+    result = depth_pro.load_rgb(input_image.name)
+    image = result[0]
+    f_px = result[-1]  # Assuming f_px is the last item in the returned tuple
+    image = transform(image)
+
+    # Run inference
+    prediction = model.infer(image, f_px=f_px)
+    depth = prediction["depth"]  # Depth in [m]
+    focallength_px = prediction["focallength_px"]  # Focal length in pixels
+
+    # Normalize depth for visualization
+    depth_normalized = (depth - np.min(depth)) / (np.max(depth) - np.min(depth))
+    
+    # Create a color map
+    plt.figure(figsize=(10, 10))
+    plt.imshow(depth_normalized, cmap='viridis')
+    plt.colorbar(label='Depth')
+    plt.title('Predicted Depth Map')
+    plt.axis('off')
+    
+    # Save the plot to a file
+    output_path = "depth_map.png"
+    plt.savefig(output_path)
+    plt.close()
+
+    return output_path, f"Focal length: {focallength_px:.2f} pixels"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_depth,
+    inputs=gr.Image(type="filepath"),
+    outputs=[gr.Image(type="filepath", label="Depth Map"), gr.Textbox(label="Focal Length")],
+    title="Depth Prediction Demo",
+    description="Upload an image to predict its depth map and focal length."
+)
+
+# Launch the interface
+iface.launch()