### 1. Imports and class names setup ### 
import gradio as gr
import os
import torch
from pathlib import Path 
from timeit import default_timer as timer
from typing import Tuple, Dict
from torchvision import transforms

class_names=['meme', 'non-meme']


model_path=Path("efficientNet_clf.pt")
model = torch.jit.load(model_path,map_location=torch.device('cpu'))
image_transform = transforms.Compose([
            transforms.Resize((224,224)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225]),
        ])
print(image_transform)

def predict(img) -> Tuple[Dict, float]:
    """Transforms and performs a prediction on img and returns prediction and time taken.
    """

    #print("---img path is: ",img)
    start_time = timer()    
    model.to("cpu")
    model.eval()
    with torch.inference_mode():
        img = image_transform(img).unsqueeze(dim=0)
        pred_probs = torch.softmax(model(img).to("cpu"), dim=1)
    
    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
    pred_time = round(timer() - start_time, 5)
    
    return pred_labels_and_probs, pred_time
    
        #print(e)
        #return "error",0
        
        
        
title = "Meme classifiication"
description = "An EfficientNetB2 model to classify images of food into 2 classes:meme and non-meme"


example_list = ["./example_imgs/"+i for i in os.listdir("./example_imgs")]
#print(example_list)

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Label(num_top_classes=2, label="Predictions"),
        gr.Number(label="Prediction time (s)"),
    ],
    examples=example_list,
    title=title,
    description=description,
)

demo.launch()
#predict(example_list[0])