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| ### 1. Imports and class names setup ### | |
| import gradio as gr | |
| import os | |
| import torch | |
| from pathlib import Path | |
| from zipfile import ZipFile | |
| 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. Handle examples.zip ### | |
| # Define the zip file and the target extraction folder | |
| zip_file_path = Path("examples.zip") | |
| extracted_folder_path = Path("examples") | |
| # Extract .zip file if it exists and is not already extracted | |
| if zip_file_path.exists() and not extracted_folder_path.exists(): | |
| print(f"Extracting {zip_file_path} to {extracted_folder_path}...") | |
| with ZipFile(zip_file_path, "r") as zf: | |
| zf.extractall(extracted_folder_path) | |
| print(f"Extraction complete. Files extracted to {extracted_folder_path}.") | |
| else: | |
| print(f"ZIP file not found or examples folder already exists.") | |
| ### 3. Model and transforms preparation ### | |
| effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3) | |
| # 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") # Load the model to the CPU | |
| ) | |
| ) | |
| ### 4. 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 probabilities | |
| pred_probs = torch.softmax(effnetb2(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 prediction 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 | |
| ### 5. Gradio app ### | |
| # Create title, description, and article | |
| title = "Food Extractor 🍕🥩🍣" | |
| 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 by [Prof. Sajad Ahmad Rather, IIT Roorkee, PARIMAL LAB](https://github.com/SajadAHMAD1)." | |
| # Create example list | |
| example_list = [[str(filepath)] for filepath in extracted_folder_path.glob("*")] # Get all files in the examples folder | |
| # 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(debug=False) # Don't need share=True in Hugging Face Spaces | |