Update app.py

app.py

@@ -1,11 +1,4 @@
-import subprocess
-import sys
-
-print("Reinstalling mmcv")
-subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "-y", "mmcv-full==1.3.17"])
-subprocess.check_call([sys.executable, "-m", "pip", "install", "mmcv-full==1.3.17", "-f", "https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html"])
-print("mmcv install complete") 
-
+from gradio.outputs import Label
 from icevision.all import *
 from icevision.models.checkpoint import *
 import PIL
@@ -13,7 +6,7 @@ import gradio as gr
 import os
 
 # Load model
-checkpoint_path = 'models/model_checkpoint.pth'
+checkpoint_path = "models/model_checkpoint.pth"
 checkpoint_and_model = model_from_checkpoint(checkpoint_path)
 
 model = checkpoint_and_model["model"]
@@ -25,40 +18,81 @@ 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 root, dirs, files in os.walk(r"sample_images/"):
     for filename in files:
         print(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
-
-
-#examples = [['sample_images/3.jpg']]
-examples = [["sample_images/"+file] for file in files] 
+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],
+    [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, 0.5],
+    [example_images[7], False, True, 0.5],
+]
 
 def show_preds(input_image, display_label, display_bbox, detection_threshold):
 
-    if detection_threshold==0: detection_threshold=0.5
+    if detection_threshold == 0:
+        detection_threshold = 0.5
+
+    img = PIL.Image.fromarray(input_image, "RGB")
 
-    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=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"])
 
-    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_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")
+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")
+outputs = [
+    gr.outputs.Image(type="pil", label="RetinaNet Inference"),
+    gr.outputs.Textbox(type='number', label='Microalgae Count')
+    ]
 
 # Option 1: Get an image from local drive
-gr_interface = gr.Interface(fn=show_preds, inputs=["image", display_chkbox_label, display_chkbox_box,  detection_threshold_slider], outputs=outputs, title='Microalgae Detection', article=article, examples=examples)
+gr_interface = gr.Interface(
+    fn=show_preds,
+    inputs=[
+        "image",
+        display_chkbox_label,
+        display_chkbox_box,
+        detection_threshold_slider,
+    ],
+    outputs=outputs,
+    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=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)