app.py

### 1. Imports and class names setup ###
import gradio as gr
import os
import torch

from model import create_vit_b_16_swag
from timeit import default_timer as timer
from typing import Tuple, Dict

# Setup class names
class_names = ['apple_pie',
 'baby_back_ribs',
 'baklava',
 'beef_carpaccio',
 'beef_tartare',
 'beet_salad',
 'beignets',
 'bibimbap',
 'bread_pudding',
 'breakfast_burrito',
 'bruschetta',
 'caesar_salad',
 'cannoli',
 'caprese_salad',
 'carrot_cake',
 'ceviche',
 'cheese_plate',
 'cheesecake',
 'chicken_curry',
 'chicken_quesadilla',
 'chicken_wings',
 'chocolate_cake',
 'chocolate_mousse',
 'churros',
 'clam_chowder',
 'club_sandwich',
 'crab_cakes',
 'creme_brulee',
 'croque_madame',
 'cup_cakes',
 'deviled_eggs',
 'donuts',
 'dumplings',
 'edamame',
 'eggs_benedict',
 'escargots',
 'falafel',
 'filet_mignon',
 'fish_and_chips',
 'foie_gras',
 'french_fries',
 'french_onion_soup',
 'french_toast',
 'fried_calamari',
 'fried_rice',
 'frozen_yogurt',
 'garlic_bread',
 'gnocchi',
 'greek_salad',
 'grilled_cheese_sandwich',
 'grilled_salmon',
 'guacamole',
 'gyoza',
 'hamburger',
 'hot_and_sour_soup',
 'hot_dog',
 'huevos_rancheros',
 'hummus',
 'ice_cream',
 'lasagna',
 'lobster_bisque',
 'lobster_roll_sandwich',
 'macaroni_and_cheese',
 'macarons',
 'miso_soup',
 'mussels',
 'nachos',
 'omelette',
 'onion_rings',
 'oysters',
 'pad_thai',
 'paella',
 'pancakes',
 'panna_cotta',
 'peking_duck',
 'pho',
 'pizza',
 'pork_chop',
 'poutine',
 'prime_rib',
 'pulled_pork_sandwich',
 'ramen',
 'ravioli',
 'red_velvet_cake',
 'risotto',
 'samosa',
 'sashimi',
 'scallops',
 'seaweed_salad',
 'shrimp_and_grits',
 'spaghetti_bolognese',
 'spaghetti_carbonara',
 'spring_rolls',
 'steak',
 'strawberry_shortcake',
 'sushi',
 'tacos',
 'takoyaki',
 'tiramisu',
 'tuna_tartare',
 'waffles']

### 2. Model and transforms preparation ###

# Create EffNetB0 model
vit_b_16_swag, vit_b_16_swag_transforms = create_vit_b_16_swag()

# Load saved weights
vit_b_16_swag.load_state_dict(
    torch.load(
        f="vit_b_16_swag_20percent_10epoch.pth",
        map_location=torch.device("cpu"),  # load to CPU
    )
)


### 3. Predict function ###

# Create predict function
def predict(img) -> Tuple[Dict, float]:
    """Transforms and performs a prediction on img and returns prediction and time taken.
    """
    # Start the timer
    start_time = timer()

    # Transform the target image and add a batch dimension
    img = vit_b_16_swag_transforms(img).unsqueeze(0)

    # Put model into evaluation mode and turn on inference mode
    vit_b_16_swag.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(vit_b_16_swag(img), dim=1)

    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}

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

    # Return the prediction dictionary and prediction time
    return pred_labels_and_probs, pred_time


### 4. Gradio app ###

# Create title, description and article strings
title = "Food Classifier V1"
description = " 20 Percent Food 101 on Vit_b_16 SWAG"
article = "Created at google collab. Documentation at https://medium.com/me/stories/public, Code repository at https://github.com/Alyxx-The-Sniper/CNN "

# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]

# 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=4, label="Predictions"),  # what are the outputs?
                             gr.Number(label="Prediction time (s)")],
                    # our fn has two outputs, therefore we have two outputs
                    # Create examples list from "examples/" directory
                    examples=example_list,
                    title=title,
                    description=description,
                    article=article)

# Launch the demo!
demo.launch()