app.py

print("Reinstalling mmcv")
import pip
pip.main(['uninstall', '-y', 'mmcv-full'])
pip.main(['install', 'mmcv-full==1.3.17', '--find-links', 'https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html'])
print("mmcv install complete")

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(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] 

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']

# display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
display_chkbox_label = gr.inputs.Checkbox(label="Label", default=True)
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")

# 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)

# #  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=True, debug=True)