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import gradio as gr
import os
import torch
from model import create_effnet_b2
from timeit import default_timer as timer
from typing import Tuple, Dict
#setup class names
class_names = ['pizza', 'steak', 'sushi']
#model and transforms preparation
effnetb2, effnetb2_transforms = create_effnet_b2(
num_classes = 3)
#load saved weights
effnetb2.load_state_dict(
torch.load(f = 'pretrained_effnetb2_feature_extractor.pth',
map_location = torch.device('cpu')) #hardcoding to load state dict onto the cpu
)
#Predict function
def predict(img) -> Tuple[Dict, float]:
#Start a timer
start_time = timer()
#transform the input image for use with effnetb2
transformed_image = effnetb2_transforms(img).unsqueeze(0)
#put model into deval mode, make preiction
effnetb2.eval()
with torch.inference_mode():
pred_logits = effnetb2(transformed_image)
pred_probs = torch.softmax(pred_logits, dim = 1)
# create a prediction label and pred prob dictionary
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i])
for i in range(len(class_names))}
#calculate pred time
end_time = timer()
pred_time = end_time - start_time
#return pred dict and pred time
print(pred_probs[0])
return pred_labels_and_probs, pred_time
# Gradio app
import gradio as gr
#Create title, description and article
title = 'FoodVision Mini'
description = 'An EfficientNetB2 feature extractor to classify food as pizza, steak, and sushi'
#Create example list
example_list = [['examples/' + example] for example in os.listdir('examples')]
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)
demo.launch(debug = False)
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