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### 1. Imports and class names setup ###
# Imports
import gradio as gr
import os
import torch
from model import create_vit_model
from timeit import default_timer as timer
from typing import Tuple, Dict
# Setup class names
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
### 2. Model and Transforms Preparation ###
vit_model, vit_transforms = create_vit_model(num_classes = 10)
# Load save weights
vit_model.load_state_dict(
torch.load(
f="vit_cifar10_state_dict.pth",
map_location=torch.device("cpu") # load the model to the cpu
)
)
### 3. Predict function ###
def predict(img) -> Tuple[Dict, float]:
# Timer
start_time = timer()
# Transform the input image to work with ViT
img = vit_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
# Eval mode and torch inference mode on
vit_model.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn prediction logits into probabilities
pred_probs = torch.softmax(vit_model(img), dim = 1)
# Create 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 prediction time
end_time = timer()
pred_time = round(end_time - start_time, 3)
# Return pred dict and pred time
return pred_labels_and_probs, pred_time
### 4. Gradio app ###
# Create title for the gradio
title = "Object Classifier - Erdem Atak Version"
description = "ViT computer vision model to classify CIFAR-10 objects"
# Create example list
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the gradio demo
demo = gr.Interface(fn = predict, # it maps inputs to outputs
inputs = gr.Image(type = "pil"),
outputs = [gr.Label(num_top_classes = 3,
label = "Predictions"),
gr.Number(label = "Prediction Time (s)")],
examples = example_list, # example list above
title = title,
description = description,
)
# launch the demo
demo.launch(debug = False,
share = True ) # public shareable URL
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