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import gradio as gr | |
import os, torch | |
from model import create_effnetb2_model | |
from timeit import default_timer as timer | |
from typing import Tuple, Dict | |
with open('class_names.txt', 'r') as f: | |
class_names = [food_name.strip() for food_name in f.readlines()] | |
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101) | |
effnetb2.load_state_dict(torch.load( | |
f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth", | |
map_location=torch.device('cpu') | |
)) | |
def predict(img) -> Tuple[Dict, float]: | |
""" | |
Transforms and performs a prediction on img and returns | |
prediction and time taken. | |
""" | |
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)) | |
} | |
pred_time = round(timer()-start_time, 5) | |
return pred_labels_and_probs, pred_time | |
title = "FoodVision Big 🍔👁" | |
description = 'An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt).' | |
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)." | |
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=5, label='Predictions'), | |
gr.Number(label='Prediction time (s)') | |
], | |
examples=example_list, | |
title=title, | |
description=description, | |
article=article | |
) | |
demo.launch() | |