app.py

import gradio as gr
import torch
from torchvision import models, transforms

# -- get torch and cuda version
TORCH_VERSION = ".".join(torch.__version__.split(".")[:2])
CUDA_VERSION = torch.__version__.split("+")[-1]
'''
# -- install pre-build detectron2
!pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/{CUDA_VERSION}/{TORCH_VERSION}/index.html

import detectron2
from detectron2.utils.logger import setup_logger # ????

from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg

# ????
setup_logger()

# -- load rcnn model
cfg = get_cfg()
# add project-specific config (e.g., TensorMask) here if you're not running a model in detectron2's core library
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
# Find a model from detectron2's model zoo. You can use the https://dl.fbaipublicfiles... url as well
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
predictor = DefaultPredictor(cfg)

!wget http://images.cocodataset.org/val2017/000000439715.jpg -q -O input.jpg
im = cv2.imread("./input.jpg")
cv2_imshow(im)

outputs = predictor(im)

print(outputs["instances"].pred_classes)
print(outputs["instances"].pred_boxes)
'''
# -- load Mask R-CNN model for segmentation
DesignModernityModel = torch.load("DesignModernityModel.pt")

#INPUT_FEATURES = DesignModernityModel.fc.in_features
#linear = nn.linear(INPUT_FEATURES, 5)

DesignModernityModel.eval() # set state of the model to inference

LABELS = ['2000-2004', '2006-2008', '2009-2011', '2012-2015', '2016-2018']

carTransforms = transforms.Compose([transforms.Resize(224)])

def classifyCar(im):
  im = Image.fromarray(im.astype('uint8'), 'RGB')
  im = carTransforms(im).unsqueeze(0)  # transform and add batch dimension
  with torch.no_grad():
    scores = torch.nn.functional.softmax(model(im)[0])
  return {LABELS[i]: float(scores[i]) for i in range(2)}

examples = [[example_img.jpg], [example_img2.jpg]]  # must be uploaded in repo

# create interface for model
interface = gr.Interface(classifyCar, inputs='Image', outputs='label', cache_examples=False, title='VW Up or Fiat 500', example=examples)
interface.launch()