#1
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']

#2
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')))
#3
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_time = timer()
  pred_time = round(end_time-start_time, 4)

  return pred_labels_and_probs, pred_time

example_list = [["examples/" + example] for example in os.listdir("examples")]

#4
title = 'FoodVisionMini 🍕🥩🍣'
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/)."

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)