Create app.py

app.py

@@ -0,0 +1,90 @@
+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 = ["fight.jpg","shaolin-kung-fu.jpg","unnamed.jpg","michael-jackson.png"]
+
+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('PoP')
+  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('PoP')
+    if pressed:
+      st.empty()
+      st.sidebar.write('Please wait for the magic to happen! This may take up to a minute.')
+      predict(uploaded_file)