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app.py

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+from gradio.outputs import Label
+from icevision.all import *
+from icevision.models.checkpoint import *
+import PIL
+import gradio as gr
+import os
+
+# Load model
+class_map = ClassMap(['selected_variant'])
+backbone = faster_rcnn.backbones.resnet_fpn.resnet18(pretrained=True)
+model = faster_rcnn.model(backbone=backbone, num_classes=len(class_map))
+model.load_state_dict(torch.load('object_localization_full-ancestry.model.pth', map_location=torch.device('cpu')))
+
+def predict(
+    model, image, detection_threshold: float = 0.5, mask_threshold: float = 0.5
+):
+    infer_ds = Dataset.from_images([image])
+
+    batch, samples = faster_rcnn.build_infer_batch(infer_ds)
+    preds = faster_rcnn.predict(
+        model=model,
+        batch=batch,
+        detection_threshold=detection_threshold
+    )
+    return samples[0]["img"], preds[0]
+
+def show_preds(input_image):
+    img = PIL.Image.fromarray(input_image, "RGB")
+    pred_dict = model_type.end2end_detect(img, model=model, 
+                                          class_map=class_map, 
+                                          detection_threshold=0.5,
+                                          display_label=False, 
+                                          display_bbox=True, 
+                                          return_img=True, 
+                                          font_size=16, 
+                                          label_color="#FF59D6")
+    return pred_dict["img"]
+
+# Populate examples in Gradio interface
+examples = [
+    ['1.jpg'],
+    ['2.jpg'],
+    ['3.jpg']
+]
+
+display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display")
+detection_threshold_slider = gr.inputs.Slider(minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold")
+
+outputs = gr.outputs.Image(type="pil")
+
+gr_interface = gr.Interface(
+    fn=show_preds,
+    inputs=["image", display_chkbox,  detection_threshold_slider],
+    outputs=outputs,
+    title='Selection Scan - Object Detection'
+    examples=examples)
+gr_interface.launch(inline=False, share=False, debug=True)