app.py

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

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

# Set up class names
with open("class_names.txt", "r") as f:
    class_names = [food_name.strip() for food_name in f.readlines()]

### 2. Model and transforms preparation ###
# Create mode and transforms
vit, vit_transforms = create_vit_model(num_classes = 101)

# Load saved weights
vit.load_state_dict(
    torch.load(f = "09_pretrained_vit_feature_extractor_food101_20_percent.pth",
               map_location = torch.device("cpu")) # load to CPU
)

### 3. Predict function ###
def predict(img) -> Tuple[Dict, float]:
    # Start a timer
    start_time = timer()

    # Transform the input image for use with ViT
    img = vit_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index

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

    # Create a prediction label and prediction probability dictionary
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}

    # Calculate pred time
    end_time = timer()
    pred_time = round(end_time - start_time, 4)

    # Return pred dict and pred time
    return pred_labels_and_probs, pred_time

### 4. Gradio app ###
# Create title, description, and article
title = "Big Food Image Classifier 🍔👁️💪"
description = "A [ViT transformer feature extractor](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#vit-b-16) computer vision model to classify [101 classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt) of food images (from Food101 dataset)."
article = "Created at [turtlemb's GitHub](https://github.com/turtlemb)."

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

# Create the Gradio demo
demo = gr.Interface(fn = predict, # maps inputs to outputs
                    inputs = gr.Image(type = "pil"),
                    outputs = [gr.Label(num_top_classes = 5, label = "Predictions"),
                               gr.Number(label = "Prediction time (s)")],
                    examples = example_list,
                    title = title,
                    description = description,
                    article = article)

# Launch the demo
demo.launch()