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import gradio as gr
import os
import torch
#from demos.foodvision_mini.model import create_effnetb2_model
from model import create_effnetb2_model
from timeit import default_timer as timer
from typing import Tuple, Dict
#setup classnames
class_names = ['pizza', 'steak', 'sushi']
# model and trandorms preparations
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3)
#load save weights
effnetb2.load_state_dict(
torch.load(f = "09_preptrained_effnetb2_20_percent (2).pth",
map_location = torch.device('cpu'))
)
# make predictions
def predict(img) -> Tuple[Dict,float] :
start_time = timer()
# this returns the prediction, and then, time
#start a timers
# transform the input image for use with effnetb2
#put model into eval mode
# create a prediction label and prediction probability dictionary
img = effnetb2_transforms(img).unsqueeze(0)
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
import os
example_list = [["examples/" + example] for example in os.listdir("examples")]
title = "FoodVision Mini"
Description = "An EfficientNetB2 feature computer vision model to classify images as pizza, steak or sushi"
article = "Cretated at......"
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= example_list,
title = title,
description=Description,
article=article)
demo.launch(debug=False,share = True) # print errors locally, generate a publically shareable URL
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