app.py

import torch
import torchvision
import random
import gradio as gr
import os

from torch import nn
from typing import Tuple, Dict
from timeit import default_timer as timer
from model import create_effnetb2

# Hardcoding the class names
class_names = ['pizza', 'steak', 'sushi']

# Creating the EffnetB2 model and transforms
effnetb2, effnetb2_transforms = create_effnetb2(num_class = len(class_names),
                                                seed = 42)

effnetb2.load_state_dict(torch.load(f = 'effnetb2.pth',
                                    map_location = torch.device('cpu')))

# Defining the example images list
example_list = [["examples/" + example] for example in os.listdir('examples')]

# Defining the predict function
def predict(img) -> Tuple[Dict, float]:

  start_time = timer()

  transformed_img = effnetb2_transforms(img).unsqueeze(0)

  effnetb2.eval()
  with torch.inference_mode():
    y_logits = effnetb2(transformed_img)
    y_preds = torch.softmax(y_logits, dim = 1)
    y_label = torch.argmax(y_preds, dim = 1)
    pred_labels_probs = {class_names[i]: float(y_preds[0][i]) for i in range(len(class_names))}

  end_time = timer()
  pred_time = round(end_time - start_time, 4)

  return pred_labels_probs, pred_time

# Creating the gradio app
title = 'FoodVision Mini'
description = 'An EfficientNetB2 feature extractor CV model to classify images of food as pizza, steak or sushi.'

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

demo.launch(share = True)