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# Load model
import torch
import torchvision
import os
import gradio as gr
from torchvision import transforms
from model import create_effnet
from typing import Tuple, Dict
from timeit import default_timer as timer
# Device agnostic code
if torch.backends.mps.is_available():
device = "mps"
elif torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
class_name = ["NORMAL", "COVID"]
EffNetB0_load_model, EffNetB0_transforms = create_effnet(
pretrained_weights=torchvision.models.EfficientNet_B0_Weights.DEFAULT,
model=torchvision.models.efficientnet_b0,
in_features=1280,
dropout=0.2,
out_features=len(class_name),
device="cpu",
)
# Write a transform for image
data_transform = transforms.Compose(
[
# Resize our images to 64x64
transforms.Resize(size=(64, 64)),
# Flip the images randomly on the horizontal
transforms.RandomHorizontalFlip(p=0.5),
# Turns image into grayscale
transforms.Grayscale(num_output_channels=3),
# Turn the image into a torch.Tensor
transforms.ToTensor()
# Permute the channel height and width
]
)
EffNetB0_load_model.load_state_dict(
torch.load("./EffNetB0_data_auto_10_epochs.pth", map_location=torch.device("cpu"))
)
### Predict function ---------------------------------------------------- ###
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
class_names = ["normal", "covid"]
# Transform the input image for use with ViT Model
img = EffNetB0_transforms(img).unsqueeze(
0
) # unsqueeze = add batch dimension on 0th index (3, 224, 224) into (1, 3, 224, 224)
# Put model into eval mode, make prediction
EffNetB0_load_model.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn the prediction logits into probabilities
pred_logits = EffNetB0_load_model(img)
pred_probs = torch.softmax(pred_logits, dim=1)
# Create a prediction label and prediction probability dictionary
pred_labels_and_probs = {
class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
}
# Calculate pred time
end_timer = timer()
pred_time = round(end_timer - start_time, 4)
# Return pred dict and pred time
return pred_labels_and_probs, pred_time
# Create title and description
title = "Covid Prediction: EfficientNetB0 Model"
description = (
"An EfficientNet model trained on Covid-19 Dataset to classify X-RAY images"
)
# Create example list
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the Gradio demo
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=[
gr.Label(num_top_classes=2, label="Predictions"),
gr.Number(label="Prediction time(s)"),
],
title=title,
description=description,
examples=example_list,
)
demo.launch()
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