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9fb0651 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | import gradio as gr
import os
import torch
from model import create_effnetb2_model
from timeit import default_timer as timer
from typing import Tuple, Dict
class_names = ['cat', 'dog', 'pizza', 'steak', 'sushi']
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=5)
effnetb2.load_state_dict(
torch.load(
f="Mult-class_classifier_98_percent_accuracy.pth",
map_location=torch.device("cpu") # load the model to the CPU
)
)
def predict(img) -> Tuple[Dict, float]:
# Start a timer
start_time = timer()
img = effnetb2_transforms(img).unsqueeze(0)
effnetb2.eval()
with torch.inference_mode():
pred_probs = torch.softmax(effnetb2(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 = "Image classification 🐈🐕🍕🥩🍣"
description = "An computer vision model to classify images as cat, dog, pizza, steak or sushi."
article = "Image classification"
example_list = [["examples/" + example] for example in os.listdir("examples")]
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)")],
examples=example_list,
title=title,
description=description,
article=article)
demo.launch()
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