app.py

### Imports and class names setup ---------------------------------------------------- ###
import os
import torch
import torchvision
import gradio as gr

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

# Setup class names
class_names = ["pizza", "steak", "sushi"]

# Device agnostic code
if torch.backends.mps.is_available():
    device = "mps"
elif torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

### Model and transforms preparation ---------------------------------------------------- ###
vit_model, vit_transforms = create_vit(
    pretrained_weights=torchvision.models.ViT_B_16_Weights.DEFAULT,
    model=torchvision.models.vit_b_16,
    in_features=768,
    out_features=3,
    device="cpu",
)

# Load save weights
vit_model.load_state_dict(
    torch.load(f="pretrained_vit_foodvision.pth", map_location=torch.device("cpu"))
)  # load the model to the CPU


### Predict function ---------------------------------------------------- ###
def predict(img) -> Tuple[Dict, float]:
    # Start a timer
    start_time = timer()
    # Transform the input image for use with ViT Model
    img = vit_transforms(img).unsqueeze(
        0
    )  # unsqueeze = add batch dimension on 0th index (3, 224, 224) into (1, 3, 224, 224)
    # Put model into eval mode, make prediction
    vit_model.eval()
    with torch.inference_mode():
        # Pass transformed image through the model and turn the prediction logits into probabilities
        pred_logits = vit_model(img)
        pred_probs = torch.softmax(pred_logits, 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_timer = timer()
    pred_time = round(end_timer - start_time, 4)

    # Return pred dict and pred time
    return pred_labels_and_probs, pred_time


### Gradio interface and launch ------------------------------------------------------------------ ###

# Create title and description
title = "FoodVision Mini: ViT Model"
description = "A ViT model trained on 20% of the Food101 dataset to classify images of pizza, steak or sushi."

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

# Create the Gradio demo
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Label(num_top_classes=3, label="Predictions"),
        gr.Number(label="Prediction time(s)"),
    ],
    title=title,
    description=description,
    examples=example_list,
)
demo.launch(debug=False)  # print errors locally