#Imports
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import tensorflow_hub as hub

#Load Magenta Arbitrary Image Stylization network
hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/1')
"""
def inference(input_image):
    preprocess = transforms.Compose([
        transforms.Resize(260),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        #transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    input_tensor = preprocess(input_image)
    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model

    # move the input and model to GPU for speed if available
    if torch.cuda.is_available():
        input_batch = input_batch.to('cuda')
        model.to('cuda')
    else:
        model.to('cpu')

    with torch.no_grad():
        output = model(input_batch)
    # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
    probabilities = torch.nn.functional.softmax(output[0], dim=0)

    # Read the categories
    with open("artist_classes.txt", "r") as f:
        categories = [s.strip() for s in f.readlines()]
        
    categories = {
        0:"vanGogh",
        1:"Monet",
        2:"Leonardo da Vinci",
        3:"Rembrandt",
        4:"Pablo Picasso",
        5:"Salvador Dali"
    }
    
    # Show top categories per image
    top5_prob, top5_catid = torch.topk(probabilities, 6)
    result = {}
    for i in range(top5_prob.size(0)):
        result[categories[top5_catid[i].item()]] = top5_prob[i].item()
    return result"""

inputs = gr.Image(type='pil')
outputs = gr.Label(type="confidences",num_top_classes=5)

title = "Artist Classifier"
description = "Gradio demo for MOBILENET V2, Efficient networks optimized for speed and memory, with residual blocks. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1801.04381'>MobileNetV2: Inverted Residuals and Linear Bottlenecks</a> | <a href='https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenet.py'>Github Repo</a></p>"

#examples = [
#            ['dog.jpg']
#]
#gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch()
gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False).launch()