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| #!usr/bin/env python | |
| 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 | |
| from PIL import Image | |
| 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=len(class_names) | |
| ) | |
| effnetb2.load_state_dict( | |
| torch.load( | |
| f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth", | |
| map_location=torch.device("cpu") | |
| ) | |
| ) | |
| 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 | |
| an Image | |
| Returns | |
| ------- | |
| Tuple[Dict, float] | |
| Tuple[prediction probabilities, time taken] | |
| """ | |
| start = timer() | |
| # trasnform and add batch dimension | |
| img = effnetb2_transforms(img).unsqueeze(0) | |
| # put model on eval mode and turn on inference | |
| effnetb2.eval() | |
| with torch.inference_mode(): | |
| pred_probs = torch.softmax(effnetb2(img), dim=1) | |
| # create a prediction label: prediction prob dict | |
| pred_labels_and_probs = { | |
| class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names)) | |
| } | |
| # calculate pred time | |
| pred_time = round(timer() - start, 5) | |
| return pred_labels_and_probs, pred_time | |
| ### Gradio App | |
| title = "FoodVision Big" | |
| description = "An EfficientNetB2 frature extractor CV model to classify images of food" | |
| article = "TODO" | |
| # Create examples list from "examples/" directory | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| def main(): | |
| # create Gradio interface | |
| 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() | |
| if __name__ == "__main__": | |
| main() | |