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| import torch | |
| from model import create_resnet | |
| import numpy as np | |
| import gradio as gr | |
| import os | |
| from timeit import default_timer as timer | |
| from typing import Tuple, Dict | |
| model = create_resnet() | |
| model.load_state_dict(torch.load(f="ResNet18_epoch-14.pth", | |
| map_location=torch.device("cpu"))) | |
| from torchvision import datasets, transforms | |
| transform = transforms.Compose([ | |
| transforms.Resize(256), | |
| transforms.CenterCrop(224), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |
| def predict(img): | |
| start_time = timer() | |
| transformed_image = transform(img) | |
| transformed_image = transformed_image.unsqueeze(0) | |
| model.eval() | |
| with torch.no_grad(): | |
| output = model(transformed_image) | |
| predicted_label = int(torch.sigmoid(output).item()) | |
| end_time = timer() | |
| pred_time = round(end_time - start_time, 4) | |
| output = "Good" if predicted_label == 1 else "Bad" | |
| return output, pred_time | |
| # Gradio Interface | |
| title = "🍋 Lemon Quality Classifier 🍋" | |
| description = "A [ResNet18](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet18.html) computer vision model to classify lemons as good or bad in quality." | |
| article = "Created for practice and learning." | |
| example_list = [["examples/" + example] for example in os.listdir("examples")] | |
| demo = gr.Interface(fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=[gr.Label(num_top_classes=1, label="Prediction"), | |
| gr.Number(label="Prediction time (s)")], | |
| examples=example_list, | |
| title=title, | |
| description=description, | |
| article=article) | |
| demo.launch() | |