add code

app.py

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+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") 
+
+## Only works if we reinstall mmcv here.
+
+from gradio.outputs import Label
+from icevision.all import *
+from icevision.models.checkpoint import *
+import PIL
+import gradio as gr
+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"]
+
+# Transforms
+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("Loading sample image:", filename)
+
+
+# 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],
+]
+
+
+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,
+        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"])
+
+
+# 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"),
+]
+
+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(
+    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)
+# #  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)

packages.txt

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+python3-opencv

requirements.txt

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+mmdet==2.19.0
+gradio
+icevision[all]
+mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html
+