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

from model import create_vit_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 ###
vit, vit_transforms = create_vit_model(num_classes = 3)

# Load saved weights
vit.load_state_dict(
    torch.load(
        f = "09_pretrained_vit_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 a timer
    start_time = timer()

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

    # Put model into eval mode, make prediction
    vit.eval()
    with torch.inference_mode():
        # Pass transformed image through the model and turn the prediction logits into probabilities
        pred_probs = torch.softmax(vit(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 = "Mini Food Image Classifier 🍕🥩🍣"
description = "A [ViT transformer feature extractor](https://docs.pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#vit-b-16) computer vision model to classify images as pizza, steak, or sushi."
article = "Created at [turtlemb's GitHub](https://github.com/turtlemb)."

# 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 = "Prediction time (s)")],
                    examples = example_list,
                    title = title,
                    description = description,
                    article = article)

# Launch the demo
demo.launch(debug = False, # print errors locally?
            share = True, # generate a publicly shareable URL
            inline = False) # to fix blank screen when embedding Gradio app in Jupyter Lab