Update app.py

app.py

@@ -16,27 +16,31 @@ class_map = checkpoint_and_model["class_map"]
 img_size = checkpoint_and_model["img_size"]
 valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
 
-examples = [['1.jpg'],['2.jpg'],['3.jpg']]
+# Populate examples in Gradio interface
+examples = [
+    ['./1.jpg'],
+    ['./2.jpg'],
+    ['./3.jpg']
+]
 
 def show_preds(input_image):
     img = PIL.Image.fromarray(input_image, "RGB")
-    pred_dict  = model_type.end2end_detect(img, valid_tfms, model, class_map=class_map, detection_threshold=0.5,
-                                           display_label=False, display_bbox=True, return_img=True, 
-                                           font_size=16, label_color="#FF59D6")
-                                           
-    return pred_dict["img"], len(pred_dict["detection"]["bboxes"])
-
-#new
+    pred_dict = model_type.end2end_detect(img, valid_tfms, model, 
+                                          class_map=class_map, 
+                                          detection_threshold=0.5,
+                                          display_label=True, 
+                                          display_bbox=True, 
+                                          return_img=True, 
+                                          font_size=35,
+                                          label_color="#FF59D6")
+    return pred_dict["img"]
 
 gr_interface = gr.Interface(
     fn=show_preds,
     inputs=["image"],
-    outputs=[gr.outputs.Image(type="pil", label="RetinaNet Inference"), gr.outputs.Textbox(type="number", label="Microalgae Count")],
-    title="Microalgae Detector with RetinaNet",
-    description="This RetinaNet model counts microalgaes on a given image. Upload an image or click an example image below to use.",
-    article="<p style='text-align: center'><a href='https://dicksonneoh.com/portfolio/how_to_deploy_od_models_on_android_with_flutter/' target='_blank'>Blog post</a></p>",
+    outputs=[gr.outputs.Image(type="pil", label="VFNet Inference")],
+    title="Fridge Object Detector",
+    description="A VFNet model that detects common objects found in fridge. Upload an image or click an example image below to use.",
     examples=examples,
-    theme="dark-grass",
-    enable_queue=True
 )
-gr_interface.launch()
+gr_interface.launch(inline=False, share=False, debug=True)