Update app.py

app.py

@@ -33,34 +33,43 @@ for root, dirs, files in os.walk(r"sample_images/"):
 # 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],
-    [example_images[1], True, True, 0.5],
-    [example_images[2], False, True, 0.7],
-    [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, 0.6],
-    [example_images[7], False, True, 0.6],
-]
+#examples = [
+#    [example_images[0], False, True, 0.5],
+#    [example_images[1], True, True, 0.5],
+#    [example_images[2], False, True, 0.7],
+#    [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, 0.6],
+#    [example_images[7], False, True, 0.6],
+#]
+
+examples = [[example_images[0], example_images[1], example_images[2], example_images[3]]]
 
 
-def show_preds(input_image, display_label, display_bbox, detection_threshold):
-    if detection_threshold == 0:
-        detection_threshold = 0.5
+#def show_preds(input_image, display_label, display_bbox, detection_threshold):
+def show_preds(input_image):
+    # if detection_threshold == 0:
+        #detection_threshold = 0.5
     img = PIL.Image.fromarray(input_image, "RGB")
-    pred_dict = model_type.end2end_detect(
-        img,
-        valid_tfms,
-        model,
-        class_map=class_map,
-        detection_threshold=detection_threshold,
-        display_label=display_label,
-        display_bbox=display_bbox,
-        return_img=True,
-        font_size=16,
-        label_color="#FF59D6",
-    )
+    
+    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=16, label_color="#FF59D6")
+                                           
+    #pred_dict = model_type.end2end_detect(
+    #    img,
+    #    valid_tfms,
+    #    model,
+    #    class_map=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"], len(pred_dict["detection"]["bboxes"])
 
 
@@ -81,10 +90,10 @@ article = "<p style='text-align: center'><a href='https://dicksonneoh.com/' targ
 gr_interface = gr.Interface(
     fn=show_preds,
     inputs=[
-        "image",
-        display_chkbox_label,
-        display_chkbox_box,
-        detection_threshold_slider,
+        "image"#,
+        #display_chkbox_label,
+        #display_chkbox_box,
+        #detection_threshold_slider,
     ],
     outputs=outputs,
     title="Microalgae Detector with RetinaNet",