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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() | |