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### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
from model import create_effnetb2_model
from timeit import default_timer as timer
from typing import Dict, Tuple
# Setup class names
class_names = ['pizza', 'steak', 'sushi']
### 2. Model and transforms perparation ###
effnetb2, effnetb2_transforms = create_effnetb2_model()
# Load save weights
effnetb2.load_state_dict(torch.load(f='09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth',
map_location=torch.device('cpu'))) # load the model to the CPU
### 3. Predict function ###
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
# Transform the input image for use with EffNetB2
transformed_img = effnetb2_transforms(img).unsqueeze(dim=0) # unsqueeze = add batch dimension on 0th
# Put model into eval mode, make prediction
with torch.inference_mode():
effnetb2.eval()
# Pass the transformed image through the model and turn the prediction logits into probabilities
pred_prob = effnetb2(transformed_img).softmax(dim=1)
# Create a prediction label and prediction probability dictionary
pred_labels_and_probs = {class_names[i]: pred_prob[0][i].item() for i in range(len(class_names))}
# Calcualte pred time
end_time = timer()
inference_time = round(end_time - start_time, 4)
# Return pred dict and pred time
return pred_labels_and_probs, inference_time
### 4. Gradio app ###
# Create title, description and aritcle
title = 'FoodVision Mini 🍕🥩🍣'
description = 'An [EfficientNetB2 feature extractor](https://pytorch.org/vision/0.16/models/generated/torchvision.models.efficientnet_b2.html#efficientnet-b2) computer vision model to classify images as pizza, steak, sushi.'
article = 'Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/#74-building-a-gradio-interface).'
# Create example list
example_list = [['examples/' + example] for example in os.listdir('examples')]
# Create the Gradio demo
demo = gr.Interface(fn=predict, # maps inputs to outputs
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)
# Launch the demo!
demo.launch(debug=False, # print errors locally?
share=True) # generate a publically shareable URL
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