# Imports 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 import random class_names=["pissa", "steak", "sushi"] effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3) effnetb2.load_state_dict( torch.load(f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth", map_location=torch.device("cpu") ) ) #Predict fn def predict(img): start_time = timer() img = effnetb2_transforms(img).unsqueeze(0) effnetb2.eval() with torch.inference_mode(): preds = torch.softmax(effnetb2(img), dim=1) pred_labels_and_probs = {class_names[i]: float(preds[0][i]) for i in range(len(class_names))} pred_time = round(timer()-start_time, 5) return pred_labels_and_probs, pred_time #Gradio app # Create title, description and article strings title = "FoodVision Mini 🍕🥩🍣" description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi." article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)." # Create examples list from "examples/" directory example_list = [["examples/" + example] for example in os.listdir("examples")] # Create the Gradio demo demo = gr.Interface(fn=predict, # mapping function from input to output inputs=gr.Image(type="pil"), # what are the inputs? outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs? gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs # Create examples list from "examples/" directory examples=example_list, title=title, description=description, article=article) demo.launch()