import os
import gradio as gr
import torch
import torchvision
from typing import Tuple, Dict
from timeit import default_timer as timer
from model import create_effnetb2_model

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

effnetb2, effnetb2_transforms = create_effnetb2_model(
    num_classes=101
)

effnetb2.load_state_dict(
    torch.load(f="pretrained_effnetb2_feature_extractor_food101.pth",
               map_location=torch.device("cpu")) # Load the model to the CPU
)

def predict(img) -> Tuple[Dict, float]:

  start_time = timer()

  transformed_image = effnetb2_transforms(img).unsqueeze(dim=0)

  effnetb2.eval()

  with torch.inference_mode():

    pred_probs = torch.softmax(effnetb2(transformed_image), dim=1)

  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}

  end_time = timer()

  pred_time = round(end_time-start_time, 4)

  return pred_labels_and_probs, pred_time


title = "Food101 Classification App 🍔"
description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model trained on [Food101 dataset](https://pytorch.org/vision/main/generated/torchvision.datasets.Food101.html) which classifies 101 different food categories."
article = "How to Use: Upload a food image in the upload section above or select an images from the 'Examples' section. " \
          "Click on the 'Submit' button and the model will detect which" \
          "food catagory the image belongs to."

example_list = [["examples/" + example] for example in os.listdir("examples")]

food101_app = gr.Interface(fn=predict,
                            inputs=gr.Image(type="pil"),
                            outputs=[gr.Label(num_top_classes=5, label="Predictions"),
                              gr.Number(label="Prediction Time (s)")],
                            examples=example_list,
                            title=title,
                            description=description,
                            article=article)

food101_app.launch()