app.py

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

class_names = ['pizza', 'steak', 'sushi']

effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes=3)

effnetb2.load_state_dict(
    torch.load(
        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
        map_location=torch.device("cpu")
    )
)

def predict(img) -> Tuple[Dict, float]:
    start_time = timer()

    img = effnetb2_transforms(img).unsqueeze(0)

    effnetb2.eval()
    with torch.inference_mode():
        pred_probs = torch.softmax(effnetb2(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

title = "FoodVision Mini 🍕🥩🍣"
description = "An EfficientNetB2 feature extractor computer vision model to classify images as pizza, steak, or sushi."
article = "Created at 09. PyTorch Model Deployment."
example_list = [["examples/" + example] for example in os.listdir("examples")]

# Create the Gradio demo
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,
                    article=article)
demo.launch()