Upload app.py

app.py

@@ -20,6 +20,7 @@ from detectron2.engine import DefaultPredictor
 from detectron2.config import get_cfg
 from detectron2.utils.visualizer import Visualizer
 from detectron2.data import MetadataCatalog
+from detectron2.utils.visualizer import ColorMode
 
 
 model_path = "https://huggingface.co/asalhi85/Smartathon-Detectron2/resolve/9f4d573340b033e651d4937906f23850f9b6bc57/phase2_detectron_model.pth"
@@ -56,19 +57,19 @@ def inference(image_url, image, min_score):
 
     outputs = predictor(im)
 
-    v = Visualizer(im, my_metadata, scale=1.2)
+    v = Visualizer(im, my_metadata, scale=1.2, instance_mode=ColorMode.IMAGE )
     out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
 
     return out.get_image()
 
 
-title = "DBMDZ Detectron2 Model Demo"
-description = "This demo introduces an interactive playground for our trained Detectron2 model. <br>The model was trained on manually annotated segments from digitized books to detect Illustration or Illumination segments on a given page."
-article = '<p>Detectron model is available from our repository <a href="">here</a> on the Hugging Face Model Hub.</p>'
+title = "Smarathon Phase2 Demo - Baseer"
+description = "This demo introduces an interactive playground for our trained Detectron2 model."
+article = '<p>Detectron model is available from our repository <a href="https://github.com/asalhi/Smartathon-Baseer">here</a>.</p>'
 
 gr.Interface(
     inference,
-    [gr.inputs.Textbox(label="Image URL", placeholder="https://api.digitale-sammlungen.de/iiif/image/v2/bsb10483966_00008/full/500,/0/default.jpg"),
+    [gr.inputs.Textbox(label="Image URL", placeholder=""),
      gr.inputs.Image(type="numpy", label="Input Image"),
      gr.Slider(minimum=0.0, maximum=1.0, value=0.5, label="Minimum score"),
     ],