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app.py

@@ -23,19 +23,26 @@ class ADA_SKIN(object):
       self._pp("Author is", self.author)
       self._ph()
     #
-    self.article = '<div><h3>Citation:</h3><ul><li>'
+    self.article = '<div><h3>Warning:</h3>Do NOT use this for any medical diagnosis.<br>' 
+    self.article += 'I am not a dermatologist, and NO dermatologist has endorsed it. '
+    self.article += 'This DL model is for my independent research. <br>Please refer to the GPL 3.0 for usage and license.'
+    self.article += '<h3>Citation:</h3><ul><li>'
     self.article += 'Author/Dev: Duc Haba, 2022.</li>'
     self.article += '<li><a target="_blank" href="https://linkedin.com/in/duchaba">https://linkedin.com/in/duchaba</a></li>'
-    self.article += '<li>The training dataset the combination of three datasets</li>'
+    self.article += '<li>The training dataset are from the International Skin Imaging Collaboration (ISIC)</li>'
+    self.article += '<li>The Skin Cancer Identification are from 3 seperate datasets.</li>'
     self.article += '<ol>'
     self.article += '<li>https://www.kaggle.com/datasets/surajghuwalewala/ham1000-segmentation-and-classification</li>'
     self.article += '<li>https://www.kaggle.com/datasets/andrewmvd/isic-2019</li>'
     self.article += '<li>https://www.kaggle.com/datasets/jnegrini/skin-lesions-act-keratosis-and-melanoma</li>'        
+    self.article += '<ul><li>'
+    self.article += 'The Malignant versus Benign dataset</li>'
+    self.article += '<ol><li>https://www.kaggle.com/datasets/fanconic/skin-cancer-malignant-vs-benign</li>'
     self.article += '</ol></ul>'
     self.article += '<h3>Articles:</h3><ul>'
-    self.article += '<li><a target="_blank" href="https://www.linkedin.com/pulse/120-dog-breeds-hugging-face-duc-haba/">'
+    self.article += '<li><a target="_blank" href="https://huggingface.co/spaces/duchaba/skin_cancer_diagnose">'
     self.article += '"Skin Cancer Diagnose"</a> on LinkedIn, on <a target="_blank" href='
-    self.article += '"https://duchaba.medium.com/120-dog-breeds-on-hugging-face-75288c7952d6">Medium.</a></li>'
+    self.article += '"https://huggingface.co/spaces/duchaba/skin_cancer_diagnose">Medium.</a></li>'
     self.article += '</ul>'  
     self.article += '<h3>Example Images: (left to right)</h3><ol>'
     self.article += '<li>Bowen Disease (AKIEC)</li>'
@@ -59,7 +66,7 @@ class ADA_SKIN(object):
     self.article += '<li>Fast.ai, PyTorch</li>'
     self.article += '</ul>'
     self.article += '<h3>Licenses:</h3><ul>'
-    self.article += '<li>GNU GPL 3.0, https://www.gnu.org/licenses/gpl-3.0.txt</li>'        
+    self.article += '<li><a target="_blank" href="https://www.gnu.org/licenses/gpl-3.0.txt">GNU GPL 3.0</a></li>'        
     self.article += '</ul></div>'
     self.examples = ['akiec1.jpg','bcc1.jpg','bkl1.jpg','df1.jpg','mel1.jpg',
       'nevi1.jpg','scc1.jpg','vl1.jpg','benign1.jpg','benign3.jpg']
@@ -86,8 +93,8 @@ class ADA_SKIN(object):
   #  
   def _draw_pred(self,df_pred, df2):
     canvas, pic = matplotlib.pyplot.subplots(1,2, figsize=(12,6))
-    ti = df_pred["breeds"].head(3).values
-    ti2 = df2["breeds"].head(2).values
+    ti = df_pred["vocab"].head(3).values
+    ti2 = df2["vocab"].head(2).values
     # special case
     #if (matplotlib.__version__) >= "3.5.2":
     try:
@@ -137,14 +144,13 @@ class ADA_SKIN(object):
     d = self._predict_image(img,self.categories)
     df = pandas.DataFrame(d, index=[0])
     df = df.transpose().reset_index()
-    df.columns = ["breeds", "pred"]
+    df.columns = ["vocab", "pred"]
     df.sort_values("pred", inplace=True,ascending=False, ignore_index=True)
     #
     d2 = self._predict_image2(img,self.categories2)
     df2 = pandas.DataFrame(d2, index=[0])
     df2 = df2.transpose().reset_index()
-    df2.columns = ["breeds", "pred"]
-    #df2.sort_values("pred", inplace=True,ascending=False, ignore_index=True)
+    df2.columns = ["vocab", "pred"]
     #
     canvas = self._draw_pred(df,df2)
     return canvas