Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+from segment_anything_2 import SAM2ImagePredictor, build_sam2
+
+# Load your model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+checkpoint = "checkpoints/sam2_hiera_large.pt"
+model_cfg = "sam2_hiera_l.yaml"
+model = build_sam2(model_cfg, checkpoint, device=device)
+predictor = SAM2ImagePredictor(model)
+
+def process_image(image, input_points, input_labels):
+    input_point = np.array([input_points])
+    input_label = np.array([input_labels])
+    
+    # Use predictor to predict mask
+    masks, scores, logits = predictor.predict(
+        point_coords=input_point,
+        point_labels=input_label,
+        multimask_output=True,
+    )
+    return Image.fromarray(masks[0].astype(np.uint8))
+
+# Define Gradio Interface
+image_input = gr.inputs.Image(type="pil")
+point_input = gr.inputs.Number(label="Point X,Y (comma-separated)")
+label_input = gr.inputs.Radio([0, 1], label="Label (0 for background, 1 for object)")
+
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[image_input, point_input, label_input],
+    outputs="image",
+    description="Interactive tool for mask prediction with Segment Anything 2 and CUTIE"
+)
+
+iface.launch()