app.py

import torch
import os
import gradio as gr
from model import create_swin
from timeit import default_timer as timer

with open("class_names.txt", "r") as f:
    class_names = [food.strip() for food in f.readlines()]

swin, swin_transforms = create_swin(len(class_names))
swin.load_state_dict(
    torch.load(
        f="pretrained_swin_food101_dataset.pth",
        map_location=torch.device("cpu"),  # load to CPU
    )
)

def predict(img):
    start = timer()
    img = swin_transforms(img).unsqueeze(0)
    swin.eval()
    with torch.inference_mode():
        pred_probs = torch.softmax(swin(img), dim=1)
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
    return pred_labels_and_probs, round(timer() - start, 2)

title = "FoodVision 💻👁️"
description = "A Swin Transformer feature extractor computer vision model for classifying images of food"

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=5, label="Predictions"), gr.Number(label="Prediction time (s)")],
    examples = example_list,
    title=title,
    description=description
)

demo.launch()