app.py

import gradio as gr 
import torch
import os
import torch.nn as nn
import torchvision
from model import create_model
from pathlib import Path

from timeit import default_timer as timer
from typing import Tuple, Dict 

#################
title = 'FoodVisionMini - Diljith'
description = 'Classifies an image of food item into either one of 3 classes : pizza, steak or sushi'

effnetb2, transforms, optimizer, lossFunc = create_model(num_classes = 3)
effnetb2.load_state_dict(torch.load(f = 'effnet_b2-20%-10epochs.pth', map_location = torch.device('cpu')))


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

def predict(img):
    img = transforms(img).unsqueeze(0) 
    effnetb2.eval()
    probs_dict = {}
    start = timer()
    with torch.inference_mode():
        pred_probs = effnetb2(img).softmax(dim = 1)
        pred_label = pred_probs.argmax(dim = 1)

        probs_dict = {class_names[i] : float(pred_probs[0][i]) for i in range(len(class_names))} 
    end = timer()

    return probs_dict, end-start 


examples_path = Path('examples/')
example_list = [['examples/' + example] for example in os.listdir(examples_path)]

projectApp = gr.Interface(
    fn = predict,
    inputs = gr.Image(type = 'pil'),
    outputs = [gr.Label(num_top_classes = len(class_names), label = 'Predictions'),
               gr.Number(label = 'Prediction time(s)')],
    examples = example_list,
    title = title,
    description = description
)

projectApp.launch(debug = False)