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

from timeit import default_timer as timer
from typing import Tuple, Dict
import torchvision

from torch import nn


def create_effnetb2_model(num_classes: int = 1,
                          seed: int = 42):
    """Creates an EfficientNetB2 feature extractor model and transforms.

    Args:
        num_classes (int, optional): number of classes in the classifier head. 
            Defaults to 3.
        seed (int, optional): random seed value. Defaults to 42.

    Returns:
        model (torch.nn.Module): EffNetB2 feature extractor model. 
        transforms (torchvision.transforms): EffNetB2 image transforms.
    """
    # Create EffNetB2 pretrained weights, transforms and model
    weights = torchvision.models.AlexNet_Weights.DEFAULT
    transforms = weights.transforms()
    model = torchvision.models.alexnet(weights=weights)

    # Freeze all layers in base model
    for param in model.parameters():
        param.requires_grad = False

    # Change classifier head with random seed for reproducibility
    torch.manual_seed(seed)
    model.classifier = nn.Sequential(
        nn.Dropout(p=0.2,),
        nn.Linear(in_features=9216, out_features=1),
    )

    return model, transforms


# Setup class names
class_names = ["Normal", "Pneumonia"]

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

# Create EffNetB2 model
effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes=1,  # len(class_names) would also work
)

# Load saved weights
effnetb2.load_state_dict(
    torch.load(
        f="alexnet_pretrained.pth",
        map_location=torch.device("cpu"),  # load to CPU
    )
)


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 = effnetb2_transforms(img).unsqueeze(0)

    # Put model into evaluation mode and turn on inference mode
    effnetb2.eval()
    with torch.inference_mode():
        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
        pred_probs = torch.sigmoid(effnetb2(img)).squeeze()

    # 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 = {
        'Normal': 1-pred_probs.item(), 'Pneumonia': pred_probs.item()}

    # 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


example_list = [[f"examples/example{i+1}.jpg"] for i in range(3)]
# Create title, description and article strings
title = "ChestXray Classification"
description = "An Alexnet computer vision model to classify images of Xray Chest images as Normal or Pneumonia."
article = "Created at (https://github.com/azizche/chest_xray_Classification)."

# Create the Gradio demo
demo = gr.Interface(fn=predict,  # mapping function from input to output
                    inputs=gr.Image(type="pil"),  # what are the inputs?
                    outputs=[gr.Label(num_top_classes=2, label="Predictions"),  # what are the outputs?
                             gr.Number(label="Prediction time (s)")],  # our fn has two outputs, therefore we have two outputs
                    examples=example_list,
                    title=title,
                    description=description,
                    article=article)

# Launch the demo!
demo.launch()