app.py

import gradio as gr
import os
import torch

from model import create_effnetb2_model
from timeit import default_timer as timer

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

# Model and transforms
effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes=3,
)

# Load saved weights
effnetb2.load_state_dict(
    torch.load(f="09_effnetb2_sushi_steak_pizza_20.pth", map_location=("cpu")) # load the model to the CPU
)

# prediction function
def predict(img) -> tuple:
    start_time = timer()
    img = effnetb2_transforms(img).unsqueeze(0)
    effnetb2.eval()
    with torch.inference_mode():
        pred_probs = torch.softmax(effnetb2(img), dim=1)

    pred_labesl_and_probs = {
        class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
    }
    end_time = timer()
    pred_time = round(end_time - start_time, 4)
    return pred_labesl_and_probs, pred_time


# gradio app
title = "FoodVision Mini 🍕🥩🍣"
description = "An EfficientNetB2 feature extractor computer vision model to classify images as pizza, steak, sushi."
article = "Created at 09 PyTorch Model Deployment."

# create example list
foodvision_min_examples_path = "examples"
example_list = [
    [os.path.join(foodvision_min_examples_path, file)]
    for file in os.listdir(foodvision_min_examples_path)
    if file.lower().endswith((".jpg", ".jpeg", ".png"))
]

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)")],
    title=title,
    description=description,
    article=article,
    examples=example_list
)
demo.launch(share=False, server_name="0.0.0.0", debug=False)