app.py

import gradio as gr
import os
import torch

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

# class names
class_names = ['A-10', 'C-130', 'F-16']

model, transform = load_model()

# predict function
def predict(img):
    
    start_time = timer()
    
    img = transform(img).unsqueeze(0)
    
    model.eval()
    with torch.inference_mode():
        pred_probs = torch.softmax(model(img), dim=1)
        
        pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
        
        end_time = timer()
        
        pred_time = round(end_time - start_time, 4)
        
        return pred_labels_and_probs, pred_time

    
    
title = "Military Aircraft predictor - Efficinet_B2 Computer Vision Model (PyTorch)"

description = "An EfficientNetB2 feature extractor computer vision model to classify Custom Dataset of F-16 Fighter Jet, C-130 Hercules, A-10 Warthog"

article = "Created in SageMaker Studio"    

example_list = [["examples/" + example] for example in os.listdir("examples")] 
    
# Gradio app
demo = gr.Interface(fn=predict,
                    inputs=gr.Image(type="pil"),
                    outputs=[gr.Label(num_top_classes=10, label="Predictions"),
                            gr.Number(label="Prediction time (s)")],
                    examples=example_list,
                    title=title,
                    description=description,
                    article=article)

demo.launch()