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

from timeit import default_timer as timer
from model import create_effnetb2_model
from typing import Tuple, Dict

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

### 2. Model and transforms preparation
effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes = len(class_names))

# Load the saved weights
effnetb2.load_state_dict(torch.load(f="09_pretrained_effnetb2_feature_extractor_20_percent.pth",
                                    map_location=torch.device("cpu")))

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

    # Transform the input image for use with EffNetB2
    img = effnetb2_transforms(img).unsqueeze(0)

    # Put model into eval mode to make prediction
    effnetb2.eval()
    with torch.inference_mode():
        # Pass transformed image through the model
        pred_probs = torch.softmax(effnetb2(img), dim=1).squeeze()

    # Create a prediction label and prediction probability dictionary
    pred_labels_and_probs = {food: float(pred_probs[i]) for i, food in enumerate(class_names)}

    # Calculate pred time
    pred_time = round(timer() - start_time, 4)

    # Return pred dict and pred time
    return pred_labels_and_probs, pred_time

### 4. Create the Gradio app
title = "FoodVision Mini🍕🥩🍣"
description = "An [EfficientNetB2 Feature Extractor](https://pytorch.org/vision/main/models/efficientnet.html#efficientnet_b2) computer vision model to classify images as pizza, steak and 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,
                    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=False) # generate a publically available URL // Not needed in huggingface