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# import the essentials
from demos.foodvision_mini.model import create_vit_b_16_model
import torch
import torchvision
import time
import gradio as gr
import numpy as np
from pathlib import Path
import os
class_names = ['pizza','steak','sushi']
device = 'cuda' if torch.cuda.is_available else 'cpu'
# creating the vit_b_16_model and loading it with state_dict of our trained model
vit_b_16_model,vit_b_16_transform = create_vit_b_16_model(num_classes=3)
vit_b_16_model.load_state_dict(torch.load(f='vit_b_16_20_percent_data.pth'))
# create the predict function
def predict(img):
"""
args:
img: is an image
returns: prediction class, prediction probability, and time taken to make the prediction
"""
# transforming the image
tr_img = vit_b_16_transform(img).unsqueeze(dim=0).to(device)
# make prediction with vit_b_16
model = vit_b_16_model.to(device)
# starting the time
start_time = time.perf_counter()
model.eval()
with torch.inference_mode():
pred_logit = model(tr_img)
pred_label = torch.argmax(pred_logit,dim=1).cpu()
pred_prob = torch.max(torch.softmax(pred_logit,dim=1)).cpu().item()
# ending the time
end_time = time.perf_counter()
# pred_dict = {str(class_names[i]):float(pred_prob[0][i].item()) for i in range(len(class_names))}
pred_prob = float(np.round(pred_prob,3))
pred_class = class_names[pred_label]
time_taken = float(np.round(end_time-start_time,3))
return pred_class,pred_prob,time_taken
# create example list
example_dir = Path('demos/foodvision_mini/examples')
example_list = [['examples/' + str(filepath)] for filepath in os.listdir(example_dir)]
# create Gradio interface
description = 'A machine learning model to classify images into pizza,steak and sushi appropriately'
title = 'Image Classifier'
demo = gr.Interface(fn=predict, # this function maps the inputs to the output
inputs=gr.Image(type='pil'), # pillow image
outputs=[gr.Label(num_top_classes=1,label='Prediction'),
gr.Number(label='prediction probability'),
gr.Number(label='prediction time(s)')],
examples=example_list,
description=description,
title=title
)
demo.launch(debug=False, # print errors locally?
share=True) # share to the public?
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