Upload app.py

app.py

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+import gradio as gr
+import fastai
+from fastai.vision import *
+from fastai.utils.mem import *
+from fastai.vision import open_image, load_learner, image, torch
+import numpy as np4
+import urllib.request
+import PIL.Image
+from io import BytesIO
+import torchvision.transforms as T
+from PIL import Image
+import requests
+from io import BytesIO
+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
+import urllib.request
+from urllib.request import urlretrieve
+import PIL.Image
+from io import BytesIO
+import torchvision.transforms as T
+import torchvision.transforms as tfms
+
+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/daf70v42oo93kym/Legacy_best.pkl?dl=1"
+urllib.request.urlretrieve(MODEL_URL, "Legacy_best.pkl")
+path = Path(".")
+learn=load_learner(path, 'Legacy_best.pkl')
+
+urlretrieve("https://s.hdnux.com/photos/01/07/33/71/18726490/5/1200x0.jpg","soccer1.jpg")
+urlretrieve("https://cdn.vox-cdn.com/thumbor/4J8EqJBsS2qEQltIBuFOJWSn8dc=/1400x1400/filters:format(jpeg)/cdn.vox-cdn.com/uploads/chorus_asset/file/22466347/1312893179.jpg","soccer2.jpg")
+urlretrieve("https://cdn.vox-cdn.com/thumbor/VHa7adj0Oie2Ao12RwKbs40i58s=/0x0:2366x2730/1200x800/filters:focal(1180x774:1558x1152)/cdn.vox-cdn.com/uploads/chorus_image/image/69526697/E5GnQUTWEAEK445.0.jpg","baseball.jpg")
+urlretrieve("https://baseball.ca/uploads/images/content/Diodati(1).jpeg","baseball2.jpeg")
+
+sample_images = [["soccer1.jpg"],
+                 ["soccer2.jpg"],
+                 ["baseball.jpg"],
+                 ["baseball2.jpeg"]]
+
+
+def predict(input):
+  img_t = T.ToTensor()(input)
+  img_fast = Image(img_t)
+  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)
+  return img 
+
+gr_interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(), outputs="image", title='Legacy-League',examples=sample_images).launch();