import gradio as gr import os import torch from model import create_effnetb2_model from timeit import default_timer as timer from PIL import Image from typing import Tuple, Dict class_names = ['pizza', 'steak', 'sushi'] effnetb2, effnetb2_transforms = create_effnetb2_model(3, 42) # Load save weights: effnetb2.load_state_dict( torch.load(f='09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_precent.pth', map_location=torch.device('cpu') ) ) ### Prediction function: EffNetB2 ### def predict(img: Image.Image) -> Tuple[Dict[str, float], float]: # Start a timer start_time = timer() # Transform the input image for use with EffNetB2 img = effnetb2_transforms(img).unsqueeze(0) # Put model into eval mode, make prediction effnetb2.eval() with torch.no_grad(): pred_probs = torch.softmax(effnetb2(img), dim=1) # Create a prediction labal and prediction probability dictionary pred_labels_and_probs = {class_names[i]:float(pred_probs[0][i]) for i in range(len(class_names))} # Calculated 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 ### Gradio app ### # Create title, description and article strings title = "FoodVision Mini 🍕🥩🍣" description = 'An [EfficientNetB2 feature extractor](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html)' article = 'Created by Arik Kodenzov with 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) # Launch the demo! demo.launch()