Update app.py

app.py

@@ -1,25 +1,25 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-from deepfloorplan_inference import DeepFloorPlanModel
-
-# Load model once at startup
-model = DeepFloorPlanModel(model_dir='pretrained')
-
-def predict_floorplan(image):
-    # image: PIL Image from Gradio
-    result = model.predict(image)
-    # Convert numpy array to PIL Image for Gradio output
-    return Image.fromarray(result.astype(np.uint8))
-
-iface = gr.Interface(
-    fn=predict_floorplan,
-    inputs=gr.Image(type="pil", label="Upload Floorplan Image"),
-    outputs=gr.Image(type="pil", label="Predicted Segmentation"),
-    title="Deep Floor Plan Segmentation",
-    description="Upload a floorplan image to get the predicted segmentation using the Deep Floor Plan model.",
-    allow_flagging="never"
-)
-
-if __name__ == "__main__":
+import gradio as gr
+import numpy as np 
+from PIL import Image
+from deepfloorplan_inference import DeepFloorPlanModel
+
+# Load model once at startup
+model = DeepFloorPlanModel(model_dir='pretrained')
+
+def predict_floorplan(image):
+    # image: PIL Image from Gradio
+    result = model.predict(image)
+    # Convert numpy array to PIL Image for Gradio output
+    return Image.fromarray(result.astype(np.uint8))
+
+iface = gr.Interface(
+    fn=predict_floorplan,
+    inputs=gr.Image(type="pil", label="Upload Floorplan Image"),
+    outputs=gr.Image(type="pil", label="Predicted Segmentation"),
+    title="Deep Floor Plan Segmentation",
+    description="Upload a floorplan image to get the predicted segmentation using the Deep Floor Plan model.",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
     iface.launch()