### 1. Import and class names setup ###
import gradio as gr
import os
import torch

from model import create_effnet_b2_model
from timeit import default_timer as timer
from typing import Tuple, Dict

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


### 2. Model and transforms preparation ###

effnet_b2, effnet_b2_transforms = create_effnet_b2_model(
    num_classes= len(class_names))

# Load save weights
effnet_b2.load_state_dict(
    torch.load(f"09_pretrained_effnetb2_feature_extractor_steak_sushi_20_percent.pth",
               map_location = torch.device("cpu"))
)


### 3. Predict function ###

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

  # Tranform the input image for use with EffNetB2and add a batch dimension
  img = effnet_b2_transforms(img).unsqueeze(0)

  # Put model into eval mode, make prediction
  effnet_b2.eval()
  with torch.inference_mode():
    # Pass transformed image through the model and turn the prediction logits into probabilites
    pred_probs = torch.softmax(effnet_b2(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 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 [EffNetB2 feature extractor](https://docs.pytorch.org/vision/0.21/models/generated/torchvision.models.efficientnet_b2.html#efficientnet-b2) computer vision model to classify images as pizza, steak or sushi"
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, # function we want to use
    inputs =gr.Image(type="pil"),
    outputs=[
        gr.Label(num_top_classes = 3, label = "Predictions"),
        gr.Number(label = "Prediction time (s)")],
    examples = example_list,
    title = title,
    description = description,
    article = article

)

# Launch the demo
demo.launch(debug = False,
            share = True)