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import gradio as gr | |
from model import create_effnetb2_model | |
import os | |
import torch | |
from torch import nn | |
from typing import List, Dict, Tuple | |
from timeit import default_timer as timer | |
with open('food101_classes.txt', 'r') as f: | |
class_names = f.read().splitlines() | |
model, transforms = create_effnetb2_model(num_classes=len(class_names)) | |
ckpt = torch.load('effnetb2_stepdecay_50epochs.tar', map_location='cpu') | |
model.load_state_dict(ckpt['model_state_dict']) | |
model.to('cpu') | |
def predict(img) -> Tuple[Dict, float]: | |
start = timer() | |
img = transforms(img) | |
img = img.unsqueeze(0) | |
img = img.to('cpu') | |
model.to('cpu') | |
model.eval() | |
with torch.inference_mode(): | |
pred_logits = model(img) | |
pred_probs = nn.Softmax(dim=1)(pred_logits).squeeze(0) | |
pred_probs_dict = {class_names[i]: pred_probs[i].item() for i in range(len(class_names))} | |
end = timer() | |
return pred_probs_dict, round(end - start, 4) | |
examples_dir = 'examples' | |
examples = [[os.path.join(examples_dir, f)] for f in os.listdir(examples_dir)] | |
import gradio as gr | |
title = "Food101 Image Classifier 🥘" | |
description = "This efficientnetb2 model finetuned on Food101 dataset for 50 epochs with step decay scheduler." | |
article = "Udemy PyTorch Bootcamp: Created for practice using [Gradio](https://www.gradio.app/)" | |
demo = gr.Interface(fn=predict, | |
inputs=gr.Image(type="pil", label="Image"), | |
outputs=[gr.Label(label="Predictions", num_top_classes=5), | |
gr.Number(label="Prediction Time (s)")], | |
examples=examples, | |
title=title, | |
description=description, | |
article=article) | |
demo.launch(share=True) | |