# 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

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

# Model and transforms preparation
# Create EffNetB2 model
effnetb2 , effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))

# load and save weights
<<<<<<< HEAD
effnetb2.load_state_dict(torch.load(os.path.join("effnetb2.pth"),map_location=torch.device('cpu')))
=======
effnetb2.load_state_dict(torch.load("effnetb2.pth",map_location=torch.device('cpu')))
>>>>>>> f57d3888756f20e9db37eb8ce02739685876fb20

# Predict function
def predict(img):
  """
  Transforms and performs a prediction on img and returns prediction and time taken.
  """
  # Start timer
  start_time = timer()

  # transform the target image and add a batch dimension
  img = effnetb2_transforms(img).unsqueeze(0)

  # put model into evaluation mode and turn on inference mode
  effnetb2.eval()
  with torch.inference_mode():
    # pass the transformed image through the model and turn the pred logits into prediction probabilities
    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 time
  pred_time = round(timer() - start_time , 5)
  # return the prediction dictionary
  return pred_labels_and_probs, pred_time

## 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 "

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

# 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
                    # Create examples list from "examples/" directory
                    #examples=example_list,
                    title=title,
                    description=description,
                    article=article)

# Launch the demo!
demo.launch()