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

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

effnetb2 , effnetb2_transforms = create_effnetb2_model(num_classes=101)

effnetb2.load_state_dict(torch.load(f='09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth',
                                    map_location= torch.device('cpu')))

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

  start_time = timer()

  img = effnetb2_transforms(img).unsqueeze(0)

  effnetb2.eval()
  with torch.inference_mode():
    pred_prob  = torch.softmax(effnetb2(img),dim=1)
  pred_labels_and_probs = {class_names[i]:float(pred_prob[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




# Create title, description and article strings
title = "FoodVision Big 🍔👁"
description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes]"
example_list = [['examples/'+example] for example in os.listdir('examples') ]

# 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=3, 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,
                    )

# Launch the demo!
demo.launch() # generate a publically shareable URL?