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### Imports and class names setup ---------------------------------------------------- ###
import os
import torch
import torchvision
import gradio as gr
from model import create_vit
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
class_names = ["pizza", "steak", "sushi"]
# Device agnostic code
if torch.backends.mps.is_available():
device = "mps"
elif torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
### Model and transforms preparation ---------------------------------------------------- ###
vit_model, vit_transforms = create_vit(
pretrained_weights=torchvision.models.ViT_B_16_Weights.DEFAULT,
model=torchvision.models.vit_b_16,
in_features=768,
out_features=3,
device="cpu",
)
# Load save weights
vit_model.load_state_dict(
torch.load(f="pretrained_vit_foodvision.pth", map_location=torch.device("cpu"))
) # load the model to the CPU
### Predict function ---------------------------------------------------- ###
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
# Transform the input image for use with ViT Model
img = vit_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
vit_model.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn the prediction logits into probabilities
pred_logits = vit_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
### Gradio interface and launch ------------------------------------------------------------------ ###
# Create title and description
title = "FoodVision Mini: ViT Model"
description = "A ViT model trained on 20% of the Food101 dataset to classify images of pizza, steak or sushi."
# 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=3, label="Predictions"),
gr.Number(label="Prediction time(s)"),
],
title=title,
description=description,
examples=example_list,
)
demo.launch(debug=False) # print errors locally
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