import gradio as gr
import os
import torch

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

# Setup class names
class_names = ['art_nouveau',
 'baroque',
 'expressionism',
 'impressionism',
 'post_impressionism',
 'realism',
 'renaissance',
 'romanticism',
 'surrealism',
 'ukiyo_e']

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

# Create EfficientNet_B2 model
EfficientNetB2_model, EfficientNetB2_transforms = create_effnetb2_model(num_classes=10,is_TrivialAugmentWide=False)

# Load saved weights
EfficientNetB2_model.load_state_dict(
    torch.load(
        f="EfficientNet_B2_FT.pth",
        map_location=torch.device("cpu"),  # load to CPU
    )
)

### 3. Classifier function ###

# Create Classifier function
def classifier(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 = EfficientNetB2_transforms(img).unsqueeze(0)
    
    # Put model into evaluation mode and turn on inference mode
    EfficientNetB2_model.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.softmax(EfficientNetB2_model(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 = "Art Classification 🖼️🎨🖌️"
description = "An EfficientNetB2 computer vision model to classify Artworks."
article = "Created with Pytorch."

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

# Create the Gradio demo
demo = gr.Interface(fn=classifier, # mapping function from input to output
                    inputs=gr.Image(type="pil"), 
                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), #1st output: pred_probs
                             gr.Number(label="Prediction time (s)")], #2nd output; pred_time
                    # Create examples list from "examples/" directory
                    examples=example_list, 
                    title=title,
                    description=description,
                    article=article)

# Launch the demo!
demo.launch()