import gradio as gr import os import torch from model import create_efficientnet from timeit import default_timer as timer from typing import Tuple, Dict # Setup class names with open("class_names.txt", "r") as f: class_names = [food_name.strip() for food_name in f.readlines()] ### Model and transforms preparation ### # Create model and transforms effnet, effnet_transforms = create_efficientnet(output_shape=101) # Load saved weights effnet.load_state_dict( torch.load(f="effnetv2L_100_percent.pth", map_location=torch.device("cpu")) # load to CPU ) ### Predict function ### def predict(img) -> Tuple[Dict, float]: # Start a timer start_time = timer() # Transform the input image for use with EffNetB2 img = effnet_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index # Put model into eval mode, make prediction effnet.eval() with torch.inference_mode(): # Pass transformed image through the model and turn the prediction logits into probaiblities pred_probs = torch.softmax(effnet(img), dim=1) # Create a prediction label and prediction probability dictionary pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} # Calculate pred time end_time = timer() pred_time = round(end_time - start_time, 4) # Return pred dict and pred time return pred_labels_and_probs, pred_time ### 4. Gradio app ### # Create title, description and article title = "Food101 classifier" description = "An [EfficientNetV2 feature extractor](https://pytorch.org/vision/main/models/efficientnetv2.html) computer vision model to classify images [101 classes of food from the Food101 dataset](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt)." article = "Built with [Gradio](https://github.com/gradio-app/gradio) and [PyTorch](https://pytorch.org/)." # Create example list example_list = [["examples/" + example] for example in os.listdir("examples")] # Create the Gradio demo demo = gr.Interface(fn=predict, # maps inputs to outputs 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) # Launch the demo demo.launch()