### 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']

### 2. Model adn transforms preparation ###
effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes = 3
    )

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

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

  # Transform the input image for use with EffNetB2
  transformed_img = effnetb2_transforms(img).unsqueeze(0) #unsqueeze = add batch dimension on 0th index

  #Put model into eval mode, make prediciton
  effnetb2.eval()
  with torch.inference_mode():
    # Pass the transformed image through the model and turn the prdiciton logits into probability
    # pred_logit = effnetb2(transformed_img)
    pred_probs = torch.softmax(effnetb2(transformed_img), dim = 1)
    # pred_label = torch.argmax(pred_probs, dim = 1)
    # class_name = class_names[pred_label]

  # 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 pred 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


### 4. Gradio App ###


# Create title, description and article
title = "FoodVision Mini 🍕🥩🍣"
description = "An [EfficientNetB2 feature extractor] (https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images as pizza, steak or sushi."
article = "Created at 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, #maps inputs to outputs
                    inputs = gr.Image(type = "pil"),
                    outputs = [gr.Label(num_top_classes = 3, label = "predictions"),
                               gr.Number(label="Prediciton time (s)")],
                    examples = example_list,
                    title = title,
                    description = description,
                    article = article
                    )

#Launch the demo:
demo.launch() # Don't need share = True in Hugging Face Spaces