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| 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 | |
| class_names = ["pizza", "steak", "sushi"] | |
| effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names)) | |
| effnetb2.load_state_dict(torch.load("09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth", map_location=torch.device("cpu"))) | |
| def predict(img) -> Tuple[Dict, float]: | |
| start_time = timer() | |
| img = effnetb2_transforms(img).unsqueeze(0) | |
| effnetb2.eval() | |
| with torch.inference_mode(): | |
| pred_probs = torch.softmax(effnetb2(img), dim=1) | |
| pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))} | |
| end_timer = timer() | |
| pred_time = round(end_timer-start_time, 4) | |
| return pred_labels_and_probs, pred_time | |
| example_list =[["examples/" + example] for example in os.listdir("examples")] | |
| import gradio as gr | |
| title="FoodVision Mini 🍕🥩🍣" | |
| description = "An EfficientNetB2 feature extractor model that predicts pizza, steak and sushi" | |
| article= "Created as a test" | |
| demo = gr.Interface(fn=predict, | |
| 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) | |
| demo.launch(debug=False, share=False) | |