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| ### 1. Imports and class names setup ### | |
| 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 | |
| # Setup class names | |
| class_names=["pizza","steak","sushi"] | |
| ### 2. Model and transforms perparation ### | |
| effnetb2,effnetb2_transforms=create_effnetb2_model(num_classes=len(class_names)) | |
| # Load save weights | |
| effnetb2.load_state_dict( | |
| torch.load( | |
| f="effnetb2_feature_extractor_food101_mini.pth", | |
| map_location=torch.device("cpu") # load the model to the CPU | |
| ) | |
| ) | |
| ### 3. Predict function ### | |
| def predict(img)->Tuple[Dict,float]: | |
| # start a timer | |
| start_time = timer() | |
| # Transform the input image for use with EffNetB2 | |
| img=effnetb2_transforms(img).unsqueeze(0) # unsqueeze = add batch dimension on 0th index | |
| # Put model into eval mode, make prediction | |
| effnetb2.eval() | |
| with torch.inference_mode(): | |
| # Pass transformed image through the model and turn the prediction logits into probaiblities | |
| pred_probs=torch.softmax(effnetb2(img),dim=1) | |
| # Create a prediction label and prediction probability dictionary | |
| pred_labels_and_probs = {} | |
| # pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} | |
| # comment loop below to un-comment line above | |
| for i,class_name in enumerate(class_names): | |
| pred_labels_and_probs[class_name]=pred_probs[0][i] | |
| # 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 Mini Classification" | |
| description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images as pizza, steak or sushi." | |
| article = "Created at [Food101 Mini Classification](https://github.com/MRameezU/Food-101-Mini-Classification.git)." | |
| # Create example list | |
| # 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=3, label="Predictions"), | |
| gr.Number(label="Prediction time (s)")], | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article) | |
| # Launch the demo! | |
| demo.launch() | |