app.py

### 1. Imports and class names setup ###

# Imports
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

# Setup class names
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

### 2. Model and Transforms Preparation ###
vit_model, vit_transforms = create_vit_model(num_classes = 10)

# Load save weights
vit_model.load_state_dict(
    torch.load(
        f="vit_cifar10_state_dict.pth",
        map_location=torch.device("cpu") # load the model to the cpu
    )
)

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

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

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

  # Create 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 prediction time
  end_time = timer()
  pred_time = round(end_time - start_time, 3)

  # Return pred dict and pred time
  return pred_labels_and_probs, pred_time

### 4. Gradio app ###

# Create title for the gradio
title = "Object Classifier - Erdem Atak Version"
description = "ViT computer vision model to classify CIFAR-10 objects"

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

# Create the gradio demo
demo = gr.Interface(fn = predict, # it maps inputs to outputs
                    inputs = gr.Image(type = "pil"),
                    outputs = [gr.Label(num_top_classes = 3,
                                       label = "Predictions"),
                              gr.Number(label = "Prediction Time (s)")],
                    examples = example_list, # example list above
                    title = title,
                    description = description,
                    )

# launch the demo
demo.launch(debug = False,
            share = True ) # public shareable URL