Update app.py

app.py

@@ -1,19 +1,18 @@
-from gradio.outputs import Label
 from icevision.all import *
 import PIL
 import torch
+from torchvision import transforms
 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')))
+#model_type = models.torchvision.faster_rcnn
 
-def predict(
-    model, image, detection_threshold: float = 0.5, mask_threshold: float = 0.5
-):
+def predict(model, image, detection_threshold: float = 0.5):
+    # Whenever you have images in memory (numpy arrays) you can use `Dataset.from_images`
     infer_ds = Dataset.from_images([image])
 
     batch, samples = faster_rcnn.build_infer_batch(infer_ds)
@@ -24,15 +23,12 @@ def predict(
     )
     return samples[0]["img"], preds[0]
     
-def show_preds(input_image, display_list, detection_threshold):
-    display_label = ("Label" in display_list)
-    display_bbox = ("BBox" in display_list)
 
-    if detection_threshold==0: detection_threshold=0.5
+def show_preds(input_image):
 
-    img, pred = predict(model=model, image=input_image, detection_threshold=detection_threshold)
+    img, pred = predict(model=model, image=input_image, detection_threshold=0.5)
     # print(pred)
-    img = draw_pred(img=img, pred=pred, class_map=class_map, display_label=display_label, display_bbox=display_bbox)
+    img = draw_pred(img=img, pred=pred, class_map=class_map, display_label=False, display_bbox=True)
     img = PIL.Image.fromarray(img)
     # print("Output Image: ", img.size, type(img))
     return img
@@ -44,15 +40,11 @@ examples = [
     ['3.png']
 ]
 
-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,
+    inputs=["image"],
+    outputs=gr.Image(type="pil"),
     title='Selection Scan - Object Detection',
     examples=examples)
+
 gr_interface.launch(inline=False, share=False, debug=True)