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| #!usr/bin/env python | |
| import os | |
| import torch | |
| import gradio as gr | |
| from model import create_effnetb2_model | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| from PIL import Image | |
| class_names = ["pizza", "steak", "sushi"] | |
| # create effnetb2 model | |
| effnetb2, effnetb2_transforms = create_effnetb2_model( | |
| num_classes=len(class_names), | |
| ) | |
| # load saved weights | |
| effnetb2.load_state_dict( | |
| torch.load( | |
| f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth", | |
| map_location=torch.device("cpu"), | |
| ) | |
| ) | |
| # predict function | |
| def predict(img: Image) -> Tuple[Dict, float]: | |
| """Transforms and performs a prediction on an image and returns the prediction | |
| and the time taken | |
| Parameters | |
| ---------- | |
| img : Image | |
| Image to classify | |
| Returns | |
| ------- | |
| Tuple[Dict, float] | |
| tuple with a dictionary that contains the probability that img belongs to | |
| each class and the time taken to make the prediction | |
| Example: ({"class1": 0.95, "class2": 0.02, "class3": 0.03}, 0.026) | |
| """ | |
| start = timer() | |
| # transform target image and add batch dimension | |
| img = effnetb2_transforms(img).unsqueeze(0) | |
| # put model into eval mode | |
| effnetb2.eval() | |
| with torch.inference_mode(): | |
| preds_probs = torch.softmax(effnetb2(img), dim=1) | |
| # create a prediction label and pred prob dictionary | |
| pred_labels_and_probs = { | |
| class_names[i]: float(preds_probs[0][i]) for i in range(len(class_names)) | |
| } | |
| # get prediction time | |
| pred_time = round(timer() - start, 5) | |
| return pred_labels_and_probs, pred_time | |
| ### Gradio app ### | |
| title = "FoodVision Mini" | |
| description = "An EfficientNetB2 feature extractor computer vision model to classify\ | |
| images of pizza, steak and sushi" | |
| article = "test" | |
| # create exmaples list from "examples" directory | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| def main(): | |
| # create Gradio demo | |
| 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) | |
| # launch demo | |
| demo.launch() | |
| if __name__ == '__main__': | |
| main() |