File size: 1,643 Bytes
30301a0 14685c2 30301a0 db81c19 30301a0 04dab16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
import gradio as gr
import os
import torch
from model import create_effnet_b2_model
from timeit import default_timer as timer
from typing import Dict, Tuple
class_names = ['pizza', 'steak', 'sushi']
effnetb2, effnetb2_transforms = create_effnet_b2_model(
num_classes=3)
#load weigths
effnetb2.load_state_dict(
torch.load(
f='09_pretrained_effnet_b2_feature_extractor_20%.pth',
map_location=torch.device('cpu')
)
)
#predict
def predict(img) -> Tuple[Dict, float]:
#start a timer
start_time = timer()
#transform input image
img = effnetb2_transforms(img).unsqueeze(0)
#set model to eval mode
effnetb2.eval()
with torch.inference_mode():
pred_probs = torch.softmax(effnetb2(img), dim=1)
pred_labels_and_probs = {class_names[i] :float(pred_probs[0,i]) for i in \
range(len(class_names))}
end_time = timer()
pred_time = round(end_time - start_time, 4)
return pred_labels_and_probs, pred_time
examples_list = [['examples/' + example] for example in os.listdir('examples')]
examples_list
title = 'foodvision mini'
description = 'effnet feature extractor for image classification'
article = 'course type-along'
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)')],
examples=examples_list,
title=title,
description=description,
article=article)
#launch demo
demo.launch() |