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
foodvision101_class_names_path = "class_names.txt"
with open(foodvision101_class_names_path, "r") as f:
  class_names = [food.strip() for food in f.readlines()]

# Model and transforms
effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes=101)

# Load save weights
effnetb2.load_state_dict(
    torch.load(
        f="09_pretrained_effnetb2_feature_extractor_food101.pth",
        map_location=torch.device("cpu")
    )
)

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

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

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

  # Return pred dict and pred time

  return pred_labels_and_probs, pred_time

# Gradio app
# Create title, description and article
title = "FoodVision 101 Learning Practice"
description = "An EfficientNetB2 feature extractor computer vision model to classify images"
article = "Created at 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="Predicitons"),
                            gr.Number(label="Prediction Time (s)")],
                   examples=example_list,
                   title=title,
                   description=description,
                   article=article)

# Launching the demo
demo.launch()