Update app.py

app.py

@@ -20,7 +20,6 @@ import piexif.helper
 
 TITLE = 'DeepDanbooru String'
 
-TOKEN = os.environ['TOKEN']
 MODEL_REPO = 'yo2266911/DeepDanbooru_string'
 MODEL_FILENAME = 'model-resnet_custom_v3.h5'
 LABEL_FILENAME = 'tags.txt'
@@ -47,33 +46,30 @@ def load_sample_image_paths() -> list[pathlib.Path]:
         dataset_repo = 'hysts/sample-images-TADNE'
         path = huggingface_hub.hf_hub_download(dataset_repo,
                                                'images.tar.gz',
-                                               repo_type='dataset',
-                                               use_auth_token=TOKEN)
+                                               repo_type='dataset')
         with tarfile.open(path) as f:
             f.extractall()
     return sorted(image_dir.glob('*'))
 
 
 def load_model() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           MODEL_FILENAME,
-                                           use_auth_token=TOKEN)
+    path = huggingface_hub.hf_hub_download(MODEL_REPO, MODEL_FILENAME)
     model = tf.keras.models.load_model(path)
     return model
 
 
 def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           LABEL_FILENAME,
-                                           use_auth_token=TOKEN)
+    path = huggingface_hub.hf_hub_download(MODEL_REPO, LABEL_FILENAME)
     with open(path) as f:
         labels = [line.strip() for line in f.readlines()]
     return labels
 
+
 def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
     return text
 
+
 def predict(image: PIL.Image.Image, score_threshold: float,
             model: tf.keras.Model, labels: list[str]) -> dict[str, float]:
     rawimage = image
@@ -93,13 +89,13 @@ def predict(image: PIL.Image.Image, score_threshold: float,
         if prob < score_threshold:
             continue
         res[label] = prob
-    b = dict(sorted(res.items(),key=lambda item:item[1], reverse=True))
-    a = ', '.join(list(b.keys())).replace('_',' ').replace('(','\(').replace(')','\)')
+    b = dict(sorted(res.items(), key=lambda item: item[1], reverse=True))
+    a = ', '.join(list(b.keys())).replace('_', ' ').replace('(', '\(').replace(')', '\)')
     c = ', '.join(list(b.keys()))
-    
+
     items = rawimage.info
     geninfo = ''
-    
+
     if "exif" in rawimage.info:
         exif = piexif.load(rawimage.info["exif"])
         exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
@@ -107,16 +103,16 @@ def predict(image: PIL.Image.Image, score_threshold: float,
             exif_comment = piexif.helper.UserComment.load(exif_comment)
         except ValueError:
             exif_comment = exif_comment.decode('utf8', errors="ignore")
-    
+
         items['exif comment'] = exif_comment
         geninfo = exif_comment
-    
+
         for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
                       'loop', 'background', 'timestamp', 'duration']:
             items.pop(field, None)
-    
+
     geninfo = items.get('parameters', geninfo)
-    
+
     info = f"""
 <p><h4>PNG Info</h4></p>    
 """
@@ -126,13 +122,13 @@ def predict(image: PIL.Image.Image, score_threshold: float,
 <p><b>{plaintext_to_html(str(key))}</b></p>
 <p>{plaintext_to_html(str(text))}</p>
 </div>
-""".strip()+"\n"
-    
+""".strip() + "\n"
+
     if len(info) == 0:
         message = "Nothing found in the image."
         info = f"<div><p>{message}<p></div>"
-    
-    return (a,c,res,info)
+
+    return (a, c, res, info)
 
 
 def main():
@@ -154,14 +150,14 @@ def main():
                              label='Score Threshold'),
         ],
         [
-            gr.outputs.Textbox(label='Output (string)'), 
-            gr.outputs.Textbox(label='Output (raw string)'), 
+            gr.outputs.Textbox(label='Output (string)'),
+            gr.outputs.Textbox(label='Output (raw string)'),
             gr.outputs.Label(label='Output (label)'),
             gr.outputs.HTML()
         ],
         examples=[
-        ['miku.jpg',0.5],
-        ['miku2.jpg',0.5]
+            ['miku.jpg', 0.5],
+            ['miku2.jpg', 0.5]
         ],
         title=TITLE,
         description='''
@@ -180,4 +176,4 @@ PNG Info code forked from [AUTOMATIC1111/stable-diffusion-webui](https://github.
 
 
 if __name__ == '__main__':
-    main()
+    main()