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

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

# Setup class names 
class_names = ["pizza","steak","sushi"]


### 2. Model and transdorms preparation ###
effnetb2, effnetb2_transforms = create_effnetb2_model()

# Load save weights
effnetb2.load_state_dict(torch.load("09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
map_location = torch.device('cpu')# Load the model to the CPU
                                    ) 
)

### 3. Predict function ###
def predict(img) -> Tuple[Dict, float]:
  #Start timer
  start_time = timer()

  # Transform the input image for use with EffNetB2
  img = effnetb2_transforms(img).unsqueeze(0)

  # Put the model in eval mode, make prediction
  effnetb2.eval()
  with torch.inference_mode():
    # Pass transformed image trough the model abd turn the prediction logits into prediction probs
    pred_probs = torch.softmax(effnetb2(img), dim = 1)
    # Create a prediction label and prediction probability dictionary
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names)) }

  # Calculate pre 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


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


### 4. Gradio App
# Create title, description and article strings
title = "FoodVision Mini 🍕🥩🍣"
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/)."

# 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
                    examples=example_list, 
                    title=title,
                    description=description,
                    article=article)

# Launch the demo!
demo.launch(debug=False) # generate a publically shareable URL?