app.py

import gradio as gr
import torch

from model import create_effnetb2_model
from timeit import default_timer as timer

# Setup class names
class_names = ["pizza", "steak", "sushi"]

# Create model
model, transforms = create_effnetb2_model(
    num_classes=3,
)

# Load saved weights
model.load_state_dict(
    torch.load(
        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
        map_location=torch.device("cpu"),  # load to CPU
    )
)

# Create prediction code
def predict(img):
    start_time = timer()
    img = transforms(img).unsqueeze(0)
    model.eval()
    with torch.inference_mode():
        pred_probs = torch.softmax(model(img), dim=1)
    pred_labels_and_probs = {
        class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
    }
    pred_time = round(timer() - start_time, 5)
    return pred_labels_and_probs, pred_time


# Create Gradio app
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/)."
example_dir = "demo/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="demo/foodvision_mini/examples",
    interpretation="default",
    title=title,
    description=description,
    article=article,
)

demo.launch()