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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 Tuple, Dict
# Setup class names
class_names = ['pizza', 'steak', 'sushi']
### 2. MOdel and transforms perparation ###
effnetb2, effnetb2_ransforms = create_effnetb2_model(
num_classes=3)
# 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
img = effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
# Put model into eval mode, make prediction
effnetb2.eval()
with torch.inference_mode():
# Pass transformed image through the model and turn the prediction logit into probability
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 pred 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
### 4. Gradio app ###
# Create title, description and article
title = "FoodVision Mini 🍕🥩🍣"
description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images as pizza, steak or 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 output
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 erros locally?
share=True) # generate a publically shareable URL
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