Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2 import model_zoo
+from detectron2.utils.visualizer import Visualizer, ColorMode
+
+# set Detectron2 
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
+cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
+predictor = DefaultPredictor(cfg)
+
+# build Gradio interface 
+def segment_image(image):
+    outputs = predictor(image)
+    instances = outputs["instances"].to("cpu")
+    v = Visualizer(image[:, :, ::-1], metadata=None, scale=1.2, instance_mode=ColorMode.IMAGE_BW)
+    out = v.draw_instance_predictions(instances)
+    segmented_image = out.get_image()[:, :, ::-1]
+    return segmented_image
+
+def process_image(image):
+    image = np.array(image)
+    segmented_image = segment_image(image)
+    return Image.fromarray(segmented_image)
+
+iface = gr.Interface(fn=process_image, inputs="image", outputs="image")
+iface.launch()