### 1. Imports and class names setup ###
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

# Setting up the class names
with open("class_names.txt", "r") as f:
  class_names = [food.strip() for food in f.readlines()]

### 2. Model and transforms preparation ###
# Create model and transforms
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101)

# Load the saved Weights
effnetb2.load_state_dict(
    torch.load(f="09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
               map_location = torch.device("cpu"))
)

### 3. Predict Function ###
def predict(img) -> Tuple[Dict, float]:

  start_time = timer()

  img = effnetb2_transforms(img).unsqueeze(0) # Unsqueeze == Add batch dimension on 0th index

  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

### 4. Gradio app ###
# Create title,, description and articcle
title = "FoodVision Big 🍔👁"
description = "An [EfficientNetB2 Feature Extractor](https://pytorch.org/vision/stable/models/efficientnet.html) computer vision model to classify [101 classes of food from the Food101 Dataset](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/)."

# Create example list
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the Gradio demo
demo =gr.Interface(fn=predict,
                  inputs=gr.Image(type="pil"),
                  outputs=[gr.Label(num_top_classes=5, label=("Predicitions")),
                           gr.Number(label="Prediction Time (s)")],
                  examples=example_list,
                  title=title,
                  description=description,
                  article=article)
# Launch the demo!
demo.launch()