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


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

# Create the pretrained EffNetB2 model
effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes=3,
)

# Load to the CPU the EffNetB2 model's saved weights
effnetb2.load_state_dict(
    torch.load(
        f="pretrained_effnetb2_feature_extractor_20_percent.pth",
        map_location=torch.device("cpu"),
    )
)


def predict(img) -> Tuple[Dict, float]:
    """This function transforms and performs a prediction on an image, and returns
    the prediction and time taken.

    Returns: the prediction dictionary and prediction time.
    """
    # Begin the prediction's timer
    start_time = timer()

    # Transform the target image with the pretrained EffNetB2 model's transforms, and add a batch dimension
    img = effnetb2_transforms(img).unsqueeze(0)

    # Set the model to evaluation model
    effnetb2.eval()

    # Activate the inference mode
    with torch.inference_mode():
        # Pass the transformed image through the model, and transform the prediction logits (the model's outputs) into prediction probabilities
        pred_probs = torch.softmax(effnetb2(img), dim=1)

    # Create a prediction label and prediction probability dictionary for each prediction class
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}

    # Calculating the prediction's time
    pred_time = round(timer() - start_time, 5)

    return pred_labels_and_probs, pred_time


# Define the title
title = "DishVision - Multi Class Food Image Classifier"
# Define the description
description = "An EfficientNetB2-based transfer learning model for feature extraction in computer vision, designed to classify images into three distinct food categories."
# Define the article
article = "Computer Vision Project"

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

# Create the Gradio deom interface
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)

demo.launch(debug=False, share=True)