Create app.py

app.py

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+import streamlit as st
+import matplotlib.pyplot as plt
+import torch
+import numpy as np
+from transformers import DPTImageProcessor, DPTForDepthEstimation
+from PIL import Image
+import requests
+
+# Load model and processor
+st.title("Depth Estimation using DPT")
+st.write("Upload an image to estimate its depth map.")
+
+@st.cache_resource
+def load_model():
+    processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
+    model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
+    return processor, model
+
+processor, model = load_model()
+
+# File uploader
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    
+    # Process image
+    inputs = 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,
+    )
+    
+    # Convert to NumPy array
+    output = prediction.squeeze().cpu().numpy()
+    normalized_depth = (output - output.min()) / (output.max() - output.min())  # Normalize to [0, 1]
+    
+    # Plot the results
+    fig, ax = plt.subplots(1, 2, figsize=(12, 6))
+    ax[0].imshow(image)
+    ax[0].set_title("Original Image")
+    ax[0].axis("off")
+    ax[1].imshow(normalized_depth, cmap="inferno")
+    ax[1].set_title("Predicted Depth Map")
+    ax[1].axis("off")
+    
+    # Display result
+    st.pyplot(fig)