app.py

import gradio as gr
import torch
import os

from PIL import Image
from typing import Tuple, Dict, List
from timeit import default_timer as timer

from model import create_vit_b_16_swag

class_names = ['Pizza', 'Steak', 'Sushi']

# Creating new instance of saved model's architecture and pre-trained model data transformation pipeline
vit_swag_model, vit_swag_transforms = create_vit_b_16_swag(num_classes=len(class_names))

# Load weights from trained and saved model
vit_swag_model.load_state_dict(torch.load('foodvision_mini_vit_swag_model.pt',
                                          map_location=torch.device('cpu')))


# -------------- Model Predicting Function --------------

# Create Predicting Function
def predict(img) -> Tuple[Dict, float]:

  # Start the timer
  start_time = timer()

  # Transform image
  vit_swag_transformed_img = vit_swag_transforms(img)

  # Making predictions with ViT SWAG model
  vit_swag_model.eval()
  with torch.inference_mode():
    vit_swag_probs = torch.softmax(vit_swag_model(vit_swag_transformed_img.to("cpu").unsqueeze(dim=0)), dim=1)

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

  # Calculate the prediction time
  pred_time = round(timer() - start_time, 5)

  return pred_probs, pred_time


# -------------- Building Gradio App --------------

# Create title, description and article strings
title = "FoodVision Mini 🍕🥩🍣"
description = "A ViT (Vision Transformer) SWAG weighted feature extractor computer vision model to classify images of food as pizza, steak or sushi."
article = "Created by Harsh Singh [-Github-](https://github.com/HarshSingh2009/)"

example_list = example_list = ['example-pizza_img.jpeg', 'example-steak-img.jpeg', 'example-sushi-img.jpeg']

# Create the Gradio demo
demo = gr.Interface(fn=predict, # mapping function from input to output
                    inputs=gr.Image(type="pil"), # what are the inputs?
                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
                    examples=example_list,
                    title=title,
                    description=description,
                    article=article)

# Launch the demo!
demo.launch()