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### 1. Imports and class names setup ### | |
import gradio as gr | |
import os | |
import torchvision | |
import torch | |
from model import create_effnetb2_model | |
from timeit import default_timer as timer | |
from typing import Tuple, Dict | |
# Setup class names | |
class_names = ["pizza","steak","sushi"] | |
### 2. Model and transdorms preparation ### | |
effnetb2, effnetb2_transforms = create_effnetb2_model() | |
# Load save weights | |
effnetb2.load_state_dict(torch.load("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 timer | |
start_time = timer() | |
# Transform the input image for use with EffNetB2 | |
img = effnetb2_transforms(img).unsqueeze(0) | |
# Put the model in eval mode, make prediction | |
effnetb2.eval() | |
with torch.inference_mode(): | |
# Pass transformed image trough the model abd turn the prediction logits into prediction probs | |
pred_probs = torch.softmax(effnetb2(img), dim = 1) | |
# Create a prediction label and prediction probability dictionary | |
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names)) } | |
# Calculate pre time | |
end_time = timer() | |
pred_time = round(end_time -start_time, 4) | |
# return pred dict and pred time | |
return pred_labels_and_probs, pred_time | |
# Create example list | |
example_list = [["examples/"+example] for example in os.listdir("examples")] | |
example_list | |
### 4. Gradio App | |
# Create title, description and article strings | |
title = "FoodVision Mini 🍕🥩🍣" | |
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi." | |
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)." | |
# 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, | |
article=article) | |
# Launch the demo! | |
demo.launch(debug=False) # generate a publically shareable URL? | |