### 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 ###

# Create ViT model
vit, vit_transforms = create_vit_model(
    num_classes=3,  # len(class_names) would also work
)

# Load saved weights
vit.load_state_dict(
    torch.load(
        f="classify_vit_b_16_20_percent_pizza_sushi_steak.pth",
        map_location=torch.device("cpu"),  # load to CPU
    )
)

### 3. Predict function ###


# Create predict function
def predict(img) -> Tuple[Dict, float]:
    """Transforms and performs a prediction on img and returns prediction and time taken."""
    # Start the timer
    start_time = timer()

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

    # Put model into evaluation mode and turn on inference mode
    vit.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(vit(img), dim=1)

    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
    pred_labels_and_probs = {
        class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))
    }

    # Calculate the prediction time
    pred_time = round(timer() - start_time, 5)

    # Return the prediction dictionary and prediction time
    return pred_labels_and_probs, pred_time


### 4. Gradio app ###

# Create title, description and article strings
title = "Food Classification"
description = "A vision transformer feature extractor model to classify images of food as pizza, steak or sushi."
article = "Reference: [Zero to Mastery Learn PyTorch for Deep Learning](https://www.learnpytorch.io/)."

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

# Create the Gradio demo
print(torch.__version__)
demo = gr.Interface(
    fn=predict,  # mapping function from input to output
    inputs=gr.Image(type="pil"), 
    outputs=[
        gr.Label(num_top_classes=3, label="Predictions"), 
        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()