fix: fix typo

app.py

@@ -8,6 +8,8 @@ from torchvision.models.detection.faster_rcnn import fasterrcnn_resnet50_fpn
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
 from src.detection.graph_utils import add_bbox
 from src.detection.vision import presets
+import torchvision.transforms as T
+
 logging.getLogger('PIL').setLevel(logging.CRITICAL)
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -36,8 +38,9 @@ def frcnn_motsynth(image):
     image_tensor = image_tensor.to(device)
     prediction = model([image_tensor])[0]
     image_w_bbox = add_bbox(image_tensor, prediction, 0.80)
-    torchvision.io.write_png(image_w_bbox, "custom_out.png")
-    return "custom_out.png"
+    transform = T.ToPILImage()
+    output = transform(image_w_bbox)
+    return output
 
 
 def frcnn_coco(image):
@@ -47,8 +50,9 @@ def frcnn_coco(image):
     image_tensor = image_tensor.to(device)
     prediction = model([image_tensor])[0]
     image_w_bbox = add_bbox(image_tensor, prediction, 0.80)
-    torchvision.io.write_png(image_w_bbox, "baseline_out.png")
-    return "baseline_out.png"
+    transform = T.ToPILImage()
+    output = transform(image_w_bbox)
+    return output
 
 
 title = "Domain shift adaption on pedestrian detection with Faster R-CNN"
@@ -57,10 +61,10 @@ examples = ["001.jpg", "002.jpg", "003.jpg",
             "004.jpg", "005.jpg", "006.jpg", "007.jpg", ]
 
 io_baseline = gr.Interface(frcnn_coco, gr.Image(type="pil"), gr.Image(
-    type="file", shape=(1920, 1080), label="Baseline Model trained on COCO + FT on MOT17"))
+    type="pil", label="Baseline Model trained on COCO + FT on MOT17"))
 
-io_custom = gr.Interface(frcnn_motsynth, gr.Image(type="pil"), gr.Image(
-    type="file", shape=(1920, 1080), label="Faster R-CNN trained on MOTSynth + FT on MOT17"))
+io_custom = gr.Interface(frcnn_motsynth,  inputs=examples, outputs=gr.Image(
+    type="pil", label="Faster R-CNN trained on MOTSynth + FT on MOT17"))
 
 gr.Parallel(io_baseline, io_custom, title=title,
             description=description, examples=examples).launch(enable_queue=True)