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app.py

@@ -14,25 +14,37 @@ class ADA_DOGS(object):
   # initialize the object
   def __init__(self, name="Wallaby",verbose=True,*args, **kwargs):
     super(ADA_DOGS, self).__init__(*args, **kwargs)
+    self.author = "Duc Haba"
+    self.name = name
     if (verbose):
-      self.author = "Duc Haba"
-      self.name = name
       self._ph()
       self._pp("Hello from class", str(self.__class__) + " Class: " + str(self.__class__.__name__))
       self._pp("Code name", self.name)
       self._pp("Author is", self.author)
       self._ph()
     #
-    self.article = '<div><h3>Information:</h3><ul><li>'
-    self.article += 'Author: Duc Haba, 2022.</li>'
+    self.article = '<div><h3>Citation:</h3><ul><li>'
+    self.article += 'Author/Dev: Duc Haba, 2022.</li>'
     self.article += '<li>https://linkedin.com/in/duchaba</li>'
     self.article += '<li>The training dataset is from the Data Scientist at Department of Health '
     self.article += 'and Social Care London, England, United Kingdom.</li>'
     self.article += '<li>https://www.kaggle.com/datasets/amandam1/120-dog-breeds-breed-classification</li>'
+    self.article += '</ul>'
+    self.article += '<h3>Train Result:</h3><ul>'
+    self.article += '<li>F1-Score, Precision, and Recall -- Take the output from method sklearn.metrics.classification_report(), import to Pandas Data Fame, sorted, and graph it.</li>'
+    self.article += '<li><img src="file/ada_f1.png" alt="F1-Score, Precision, and Recall Graph" width="640"</li>'
+    self.article += '</ul>'
+    self.article += '<h3>Dev Stack:</h3><ul>'
+    self.article += '<li>Jupyter Notebook, Python, Pandas, Matplotlib, Sklearn</li>'
+    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 += '</ul></div>'
     self.examples = ['dog1.jpg','dog2.jpg','dog3.jpg','dog4.jpg','dog5.png','dog6.jpg', 'dog7.jpg','duc.jpg']
+    self.title = "120 Dog Breeds Prediction"
     return
-  #
+  # 
   # pretty print output name-value line
   def _pp(self, a, b):
     print("%34s : %s" % (str(a), str(b)))
@@ -93,7 +105,7 @@ intf = gradio.Interface(fn=maxi.predict_donut,
   inputs=hf_image, 
   outputs=["plot"], 
   examples=maxi.examples,
-  title="120 Dog Breeds Prediction",
+  title=maxi.title,
   live=True,
   article=maxi.article)
 intf.launch(inline=False,share=True)