import streamlit as st
import urllib.request
import PIL.Image
from PIL import Image
import requests
import fastai
from fastai.vision import *
from fastai.utils.mem import *
from fastai.vision import open_image, load_learner, image, torch
import numpy as np
from urllib.request import urlretrieve
from io import BytesIO
import numpy as np
import torchvision.transforms as T
from PIL import Image,ImageOps,ImageFilter
from io import BytesIO
import os



class FeatureLoss(nn.Module):
    def __init__(self, m_feat, layer_ids, layer_wgts):
        super().__init__()
        self.m_feat = m_feat
        self.loss_features = [self.m_feat[i] for i in layer_ids]
        self.hooks = hook_outputs(self.loss_features, detach=False)
        self.wgts = layer_wgts
        self.metric_names = ['pixel',] + [f'feat_{i}' for i in range(len(layer_ids))
              ] + [f'gram_{i}' for i in range(len(layer_ids))]

    def make_features(self, x, clone=False):
        self.m_feat(x)
        return [(o.clone() if clone else o) for o in self.hooks.stored]
    
    def forward(self, input, target):
        out_feat = self.make_features(target, clone=True)
        in_feat = self.make_features(input)
        self.feat_losses = [base_loss(input,target)]
        self.feat_losses += [base_loss(f_in, f_out)*w
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3
                             for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
        self.metrics = dict(zip(self.metric_names, self.feat_losses))
        return sum(self.feat_losses)
    
    def __del__(self): self.hooks.remove()
    

MODEL_URL = "https://www.dropbox.com/s/vxgw0s7ktpla4dk/SkinDeep2.pkl?dl=1"
urlretrieve(MODEL_URL, "SkinDeep2.pkl")
path = Path(".")
learn = load_learner(path, 'SkinDeep2.pkl')


def predict(image):
  img_fast = open_image(image)
  a = PIL.Image.open(image).convert('RGB')
  st.image(a, caption='Input')
  p,img_hr,b = learn.predict(img_fast)
  x = np.minimum(np.maximum(image2np(img_hr.data*255), 0), 255).astype(np.uint8)
  img = PIL.Image.fromarray(x).convert('RGB')
  return st.image(img, caption='Tattoo')
  

SIDEBAR_OPTION_DEMO_IMAGE = "Select a Demo Image"
SIDEBAR_OPTION_UPLOAD_IMAGE = "Upload an Image"

SIDEBAR_OPTIONS = [SIDEBAR_OPTION_DEMO_IMAGE, SIDEBAR_OPTION_UPLOAD_IMAGE]

app_mode = st.sidebar.selectbox("Please select from the following", SIDEBAR_OPTIONS)
photos = ["tatoo.jpg","tattoo2.jpg"]

if app_mode == SIDEBAR_OPTION_DEMO_IMAGE:
  st.sidebar.write(" ------ ")
  option = st.sidebar.selectbox('Please select a sample image and then click PoP button', photos)
  pressed = st.sidebar.button('Predict')
  if pressed:
    st.empty()
    st.sidebar.write('Please wait for the magic to happen! This may take up to a minute.')
    predict(option)
    
    
elif app_mode == SIDEBAR_OPTION_UPLOAD_IMAGE:
  uploaded_file = st.file_uploader("Choose an image...")
  if uploaded_file is not None:
    pressed = st.sidebar.button('Predict')
    if pressed:
      st.empty()
      st.sidebar.write('Please wait for the magic to happen! This may take up to a minute.')
      predict(uploaded_file)