###1. Import classnames setup###
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
class_names=["pizza","steak","sushi"]

##2. model and transforms preparation
effnetb2,effnetb2_transforms=create_effnetb2_model(
    num_classes=3)

#load save weight
effnetb2.load_state_dict(
    torch.load(
        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
        map_location=torch.device("cpu")#load the model to the CPU
    )
)

###3. Predict function (predict())###
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)#unsqueeze = add batch dimension on 0th

    #Put model into eval mode, make prediction
    effnetb2.eval()
    with torch.inference_mode():
        #pass transformed image through the model and turn the prediction logits into probabilities
        pred_probs=torch.softmax(effnetb2(img),dim=1)


    #create a 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

### 4. Gradio app ###
#create title,description and article
title = "FoodVision Mini"
description="An [EfficientNetB2 feature extractor](https://pytorch.org/vision/main/models/efficientnet.html) computer vision model to classify images as pizza,steak or sushi"
article="Create at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_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,#maps inputs to outputs
                    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
                    )

#Launch the demo!
demo.launch()