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=3)
effnetb2.load_state_dict(
    torch.load(
        f="effnetb2_food_classifier.pth",
        map_location = torch.device("cpu"),
        weights_only = True,
    )
)

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))}
    pred_time = round(timer() - start_time, 5)
    return pred_labels_and_probs, pred_time

title = "FoodVision Mini"
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
article = "Create at Mastering PyTorch"
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=3, label="Predictions"),
                               gr.Number(label="Prediction time(s)")],
                    examples = example_list,
                    title = title,
                    description = description,
                    article = article)
demo.launch(share=True)