### 1. Imports and class names setup ###
import gradio as gr
import os
import torch
from model import create_effnetb2_model, create_vit_model
from timeit import default_timer as timer
from typing import Tuple, Dict
  # Setup class names
class_names = ['cardboard', 'glass', 'metal', 'organic', 'paper', 'plastic', 'trash']
### 2. Model and transforms preparation ###
effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes=len(class_names),
)
vit, vit_transforms = create_vit_model(
    num_classes=len(class_names),
)
  # Load saved weights
effnetb2.load_state_dict(
    torch.load(
        f="effnetb2_augmented_dataset_10_epochs.pth",
        map_location=torch.device("cpu")
    )
)
vit.load_state_dict(
    torch.load(
        f="vit_b_16_augmented_dataset_10_epochs.pth",
        map_location=torch.device("cpu")
    )
)
### 3. Predict function ###
def predict(img, model_str: str) -> Tuple[Dict, float]:
  # Start a timer
  start_time = timer()
  if model_str == "effnetb2":
    # Transform the image
    img = effnetb2_transforms(img).unsqueeze(0)
    model = effnetb2
    model.eval()
    # Put model into eval mode, make prediction
    with torch.inference_mode():
        # Pass transformed image through the model and turn the prediction logits into probabilities
        pred_probs = torch.softmax(model(img), dim=1)
    # Create a prediciton 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
    pred_time = round(timer() - start_time, 4)
    # Return pred labels and pred time
    return pred_labels_and_probs, pred_time
  else:
    # Transform the image
    img = vit_transforms(img).unsqueeze(0)
    model = vit
    model.eval()
    # Put model into eval mode, make prediction
    with torch.inference_mode():
        # Pass transformed image through the model and turn the prediction logits into probabilities
        pred_probs = torch.softmax(model(img), dim=1)
    # Create a prediciton 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
    pred_time = round(timer() - start_time, 4)
    # Return pred labels and pred time
    return pred_labels_and_probs, pred_time
### 4. Gradio app - Gradio interface + launch command ###
  # Create title, description and article
title = "Rubbish Classifier 🗑️"
description = "An [EfficientNetb2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) and a [ViT feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16) model to classify rubbish images."
article = "Created by me"
  # 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"),
                            gr.Dropdown(choices=['effnetb2', 'vit'], label='Model To Use', value='effnetb2')],
                    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)