app.py

# creating app.py

########## imports ############
import torch
import torch.nn as nn
from torchvision import models, transforms
import gradio as gr
from model import create_model 
import PIL 
from PIL import Image
import os
from pathlib import Path
###############################

def predict(img):
    swinb, transform = create_model()
    class_names = []
    with open('classes.txt', 'r') as f:
        class_names = [foodname.strip() for foodname in f.readlines()] 
    swinb.load_state_dict(torch.load(f = 'models/swin40%newer.pth', map_location = torch.device('cpu'))) 
    
    img = transform(img).unsqueeze(0)

    swinb.eval()
    with torch.inference_mode():
        pred_label = class_names[swinb(img).softmax(dim = 1).argmax(dim = 1)]
    print(pred_label)
    return pred_label

###############################
title = 'FoodVision Project'
description = 'FoodVision is an image classification model based on EfficientNet_B2 which has been trained on a 101 different classes using the Food101 dataset'
###############################

example_list = [['examples/' + example] for example in os.listdir('examples')] ##############
for examples in example_list:
    print(examples)
demo = gr.Interface(
    fn = predict,
    inputs = gr.Image(type = 'pil'),
    outputs = [gr.Textbox()],
    examples = example_list,
    title = title,
    description = description 
)

demo.launch(debug = True)