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import gradio as gr | |
import os | |
import torch | |
from model import create_effnetb2_model | |
from timeit import default_timer as timer | |
from typing import Tuple,Dict | |
with open('class_names.txt','r') as f: | |
class_names = [food.strip() for food in f.readlines()] | |
effnetb2 , effnetb2_transforms = create_effnetb2_model(num_classes=101) | |
effnetb2.load_state_dict(torch.load(f='09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth', | |
map_location= torch.device('cpu'))) | |
def predict(img) -> Tuple[Dict,float]: | |
start_time = timer() | |
img = effnetb2_transforms(img).unsqueeze(0) | |
effnetb2.eval() | |
with torch.inference_mode(): | |
pred_prob = torch.softmax(effnetb2(img),dim=1) | |
pred_labels_and_probs = {class_names[i]:float(pred_prob[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 | |
# Create title, description and article strings | |
title = "FoodVision Big ππ" | |
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes]" | |
example_list = [['examples/'+example] for example in os.listdir('examples') ] | |
# Create the Gradio demo | |
demo = gr.Interface(fn=predict, # mapping function from input to output | |
inputs=gr.Image(type="pil"), # what are the inputs? | |
outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs? | |
gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs | |
examples=example_list, | |
title=title, | |
description=description, | |
) | |
# Launch the demo! | |
demo.launch() # generate a publically shareable URL? | |