app.py

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

from model import createVITModel
from pathlib import Path
from timeit import default_timer as timer
from typing import Tuple, Dict

# Setup class names
with open("classes.txt", "r") as f: # reading them in from class_names.txt
    class_names = [food_name.strip() for food_name in  f.readlines()]

model, vit_transform = createVITModel(out_features=len(class_names))
model.load_state_dict(torch.load('VIT_32_20_003.pth'))
model = model.to('cpu')

# Create a list of example inputs to our Gradio demo
examples_source_dir = Path("examples")
examples_source_paths = list(examples_source_dir.glob("*.jpg"))
example_list = [str(filepath) for filepath in examples_source_paths]

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_transform(img).unsqueeze(dim=0)
    
    # Put model into evaluation mode and turn on inference mode
    model.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(model(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


# Create title, description and article strings
title = "Food Image Classifier 🍰 🎂"
description = "A VIT Food Classifier."
article = ""

# 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=5, 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(debug=False) # generate a publically shareable URL?