import gradio as gr
import os, torch
from model import create_effnetb2_model
from timeit import default_timer as timer
from typing import Tuple, Dict

with open('class_names.txt', 'r') as f:
    class_names = [food_name.strip() for food_name in f.readlines()]

effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101)
effnetb2.load_state_dict(torch.load(
    f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
    map_location=torch.device('cpu')
))

def predict(img) -> Tuple[Dict, float]:
    """
    Transforms and performs a prediction on img and returns
    prediction and time taken.
    """
    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 Big 🍔👁"
description = 'An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt).'
article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."

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=5, label='Predictions'),
        gr.Number(label='Prediction time (s)')
    ],
    examples=example_list,
    title=title,
    description=description,
    article=article
)

demo.launch()