Update app.py

app.py

@@ -97,20 +97,31 @@ title = "Plot multi-class SGD on the iris dataset"
 
 model_card = f"""
 ## Description
-Plot decision surface of multi-class SGD on iris dataset.
-The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines.
+This interactive demo is based on the [Plot multi-class SGD on the iris dataset](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_iris.html#sphx-glr-auto-examples-linear-model-plot-sgd-iris-py) example from the popular [scikit-learn](https://scikit-learn.org/stable/)  library, which is a widely-used library for machine learning in Python. 
+This demo plots the decision surface of multi-class SGD on the iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines.
+You can play with the following hyperparameters:
+`alpha` is a constant that multiplies the regularization term. The higher the value, the stronger the regularization. 
+`max_iter` is the maximum number of passes over the training data (aka epochs). 
+`Standardise` centers the dataset
+
 ## Dataset
 [Iris Dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set)
+
+## Model
+currentmodule: [sklearn.linear_model](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model)
+class:`SGDClassifier` is the estimator used in this example.
+
 """
 
 with gr.Blocks(title=title) as demo:
     gr.Markdown('''
             <div>
-            <h1 style='text-align: center'>⚒ Plot multi-class SGD on iris dataset 🛠</h1>
+            <h1 style='text-align: center'>Plot multi-class SGD on iris dataset</h1>
             </div>
         ''')
  
     gr.Markdown(model_card)
+    gr.Markdown("Author: <a href=\"https://huggingface.co/sulpha\">sulpha</a>")
     d0 = gr.Slider(0.001,5,step=0.001,value=0.001,label='alpha')
     d1 = gr.Slider(1,1001,step=10,value=100,label='max_iter')
     d2 = gr.Checkbox(value=True,label='Standardize')