Add SAM segmentation app

app.py

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+import gradio as gr
+from transformers import pipeline
+import numpy as np
+from PIL import Image
+
+# Initialize the pipeline for SAM
+# Use "facebook/sam-vit-base" for better speed on Spaces
+generator = pipeline("mask-generation", model="facebook/sam-vit-base", device=-1) # device=-1 for CPU
+
+def segment_image(input_image):
+    # Generate masks automatically
+    outputs = generator(input_image, points_per_batch=64)
+    masks = outputs["masks"]
+    
+    # Create a combined visualization of the masks
+    # Each mask is a boolean array; we can overlay them with colors
+    width, height = input_image.size
+    overlay = np.zeros((height, width, 3), dtype=np.uint8)
+    
+    for mask in masks:
+        color = np.random.randint(0, 255, (3,))
+        overlay[mask] = color
+        
+    # Blend the original image with the masks
+    combined = Image.blend(input_image.convert("RGB"), Image.fromarray(overlay), alpha=0.5)
+    return combined
+
+# Define the Gradio Interface
+demo = gr.Interface(
+    fn=segment_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil", label="Segmented Output"),
+    title="Segment Anything with SAM",
+    description="Upload an image to see automatic zero-shot semantic segmentation."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers
+torch
+torchvision
+numpy
+pillow