Update app.py

app.py

@@ -17,7 +17,6 @@ import os
 # Load model
 checkpoint_path = "models/model_checkpoint.pth"
 checkpoint_and_model = model_from_checkpoint(checkpoint_path)
-
 model = checkpoint_and_model["model"]
 model_type = checkpoint_and_model["model_type"]
 class_map = checkpoint_and_model["class_map"]
@@ -26,18 +25,13 @@ 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()])
 
-
 for root, dirs, files in os.walk(r"sample_images/"):
     for filename in files:
-        print(filename)
+        print("Loading sample image:", filename)
 
-examples = ["sample_images/" + file for file in files]
-article = "<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>"
-enable_queue = True
 
 # Populate examples in Gradio interface
 example_images = [["sample_images/" + file] for file in files]
-
 # Columns: Input Image | Label | Box | Detection Threshold
 examples = [
     [example_images[0], False, True, 0.5],
@@ -46,17 +40,15 @@ examples = [
     [example_images[3], True, True, 0.7],
     [example_images[4], False, True, 0.5],
     [example_images[5], False, True, 0.5],
-    [example_images[6], False, True, 0.5],
-    [example_images[7], False, True, 0.5],
+    [example_images[6], False, True, 0.6],
+    [example_images[7], False, True, 0.6],
 ]
 
-def show_preds(input_image, display_label, display_bbox, detection_threshold):
 
+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 = model_type.end2end_detect(
         img,
         valid_tfms,
@@ -69,22 +61,21 @@ def show_preds(input_image, display_label, display_bbox, detection_threshold):
         font_size=16,
         label_color="#FF59D6",
     )
-
     return pred_dict["img"], len(pred_dict["detection"]["bboxes"])
 
 
 # display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
 display_chkbox_label = gr.inputs.Checkbox(label="Label", default=False)
 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", label="RetinaNet Inference"),
-    gr.outputs.Textbox(type='number', label='Microalgae Count')
-    ]
+    gr.outputs.Textbox(type="number", label="Microalgae Count"),
+]
+
+article = "<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>"
 
 # Option 1: Get an image from local drive
 gr_interface = gr.Interface(
@@ -101,13 +92,8 @@ gr_interface = gr.Interface(
     article=article,
     examples=examples,
 )
-
-
 # #  Option 2: Grab an image from a webcam
 # gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False)
-
 # #  Option 3: Continuous image stream from the webcam
 # gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True)
-
-
-gr_interface.launch(inline=False, share=False, debug=True)
+gr_interface.launch(inline=False, share=False, debug=True)