from pydoc import describe
import re
import numpy as np
from PIL import Image
import torch
from torchvision import transforms
import gradio as gr
from model import TransformerNet

style_model = TransformerNet()
device=torch.device("cpu")

styles_map = {"Kandinsky, Several circles": "kand_circles.model", 
              "Haring, Dance": "haring_dance.model", 
              "Picasso, The weeping woman": "picasso_weeping.model",
              "Van Gogh, Wheatfield with crows": "vangogh_crows.model"}

content_transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Lambda(lambda x: x.mul(255))
])

def run(content_image, style):
    content_image.thumbnail((1080, 1080))
    img = content_transform(content_image)
    img = img.unsqueeze(0).to(device)

    model = styles_map[style]
    state_dict = torch.load(f"./models/{model}")
    
    for k in list(state_dict.keys()):
        if re.search(r'in\d+\.running_(mean|var)$', k):
            del state_dict[k]
    style_model.load_state_dict(state_dict)
    style_model.to(device)

    with torch.no_grad():
        output = style_model(img)

    img = output[0].clone().clamp(0, 255).numpy()
    img = img.transpose(1, 2, 0).astype("uint8")
    
    img = Image.fromarray(img)
    return img

content_image_input = gr.inputs.Image(label="Content Image", type="pil")
style_input = gr.inputs.Dropdown(list(styles_map.keys()), type="value", default="Kandinsky, Several circles", label="Style")

description="Fast Neural Style Transfer demo (trained from scratch!). Upload a content image. Select an artwork. Enjoy."
article="""
**References**\n\n
You can find <a href='https://francescopochetti.com/fast-neural-style-transfer-deploying-pytorch-models-to-aws-lambda/' target='_blank'>here</a> a post I put together 
describing the approach I used to train models and deploy them on <a href='http://visualneurons.com/fast.html' target='_blank'>visualneurons.com</a> using AWS Lambda. \n
<a href='https://github.com/FraPochetti/examples/blob/master/fast_neural_style/neural_style/FastStyleTransferPytorch.ipynb' target='_blank'>Here</a> is instead the Jupyter notebook
with the training logic. \n
<br>
<hr>
**Kandinsky, Several circles**
<img src='https://style-transfer-webapptest.s3.eu-west-1.amazonaws.com/small_images_hf/Several_Circles.jpeg'>
<hr>
**Haring, Dance**
<img src='https://style-transfer-webapptest.s3.eu-west-1.amazonaws.com/small_images_hf/Haring.jpeg'>
<hr>
**Picasso, The weeping woman**
<img src='https://style-transfer-webapptest.s3.eu-west-1.amazonaws.com/small_images_hf/weeping.png'>
<hr>
**Van Gogh, Wheatfield with crows**
<img src='https://style-transfer-webapptest.s3.eu-west-1.amazonaws.com/small_images_hf/Wheatfield_with_crows.jpeg'>
"""
example = ["dog.jpeg", "Kandinsky, Several circles"]

app_interface = gr.Interface(fn=run,
                             inputs=[content_image_input, style_input],
                             outputs="image",
                             title="Fast Neural Style Transfer",
                             description=description,
                             examples=[example],
                             article=article)
app_interface.launch()