Create app.py

app.py

@@ -0,0 +1,43 @@
+from huggingface_hub import from_pretrained_fastai
+import gradio as gr
+from fastai.vision.all import *
+from icevision.all import *
+import PIL
+
+class_map = ClassMap(['apple','banana','orange'])
+
+presize = 512
+size = 384
+
+train_tfms = tfms.A.Adapter(
+    [*tfms.A.aug_tfms(size=size, presize=presize), tfms.A.Normalize()]
+)
+valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size), tfms.A.Normalize()])
+
+model1 = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet18_fpn(pretrained=True), num_classes=len(class_map))
+state_dict = torch.load('fasterRCNNFruits.pth', map_location=torch.device('cpu'))
+model1.load_state_dict(state_dict)
+
+def show_preds(input_image, display_label, display_bbox, detection_threshold):
+
+    if detection_threshold==0: detection_threshold=0.5
+
+    img = PIL.Image.fromarray(input_image, 'RGB')
+
+    pred_dict  = models.torchvision.faster_rcnn.end2end_detect(img, valid_tfms, model1, class_map=ClassMap(class_map), detection_threshold=detection_threshold,
+                                           display_label=display_label, display_bbox=display_bbox, return_img=True, 
+                                           font_size=16, label_color="#FF59D6")
+
+    return pred_dict['img']
+
+# display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
+display_chkbox_label = gr.inputs.Checkbox(label="Label", default=True)
+display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True)
+
+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_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, examples=[['mixed_24.jpg', True, True, 0.5], ['mixed_24.jpg', True, True, 0.5]])
+
+gr_interface.launch(inline=False, share=False, debug=True)