Update app.py

incorporated review comments

app.py

@@ -43,19 +43,65 @@ def plot_loss_func():
   plt.ylabel("$L(y=1, f(x))$")
   return fig
 
+
+
+
 title = "SGD convex loss functions"
 
-# def greet(name):
-#     return "Hello " + name + "!"
+detail = "This plot shows the convex loss functions supported by SGDClassifiers(Linear classifiers (SVM, logistic regression, etc.) with SGD training)."
+
+def explain(name):
+    # print("name=",name)
+    if name == "0-1 loss":
+      docstr = "Explanation for " + name + ": " +\
+                " This is the simplest loss function used in classification problems. It counts how many mistakes a hypothesis function makes on a training set. " +\
+                " A loss of 1 is accounted if its mispredicted and a loss of 0 for the correct prediction. " +\
+                " This function is non differentiable and hence not used in Optimization problems. "
+    elif name == "Hinge loss":
+      docstr = "Explanation for " + name + ": " +\
+                " This is the loss function used in maximum-margin classification in SVMs. "+\
+                " Z_i = y_i*(w.T * x_i + b), if Z_i > 0 the point x_i is correctly classified and Z_i < 0 , x_i is incorrectly classified "+\
+                " Z_i >= 1, hinge loss =0 , Z_i < 1 , hinge loss = 1- Z_i "
+    elif name == "Perceptron loss":
+      docstr = "Explanation for " + name + ": " +\
+                " This is the linear loss function used in perceptron algorithm. "+\
+                " The binary classifier function which decides whether the input represented by vector of numbers belongs to a class or not. "
+
+    elif name == "Squared Hinge loss":
+      docstr = "Explanation for " + name + ":" +\
+                " This represents the square verison of Hinge loss and used in classification algorithms where Performance is important. "+\
+                " If we want a more fine decision boundary where we want to punish larger errors more significantly than the smaller errors. "
+    
+    elif name == "Modified Huber loss":
+      docstr = "Explanation for " + name + ":" +\
+                " The Huber loss function balances the best of both Mean Squared Error and Mean Absolute Error. "+\
+                " Its a piecewise function and hyper parameter delta is to be found first and then loss optimization step."
+    
+    else:
+      docstr = " Logistic Loss is a loss function used for Logistic Regression. Please refer wikipedia for the Log loss equation." +\
+                " L2 regularization is most important for logistic regression models. "
+    
+
+    return docstr 
+
+
+
 with gr.Blocks(title=title) as demo:
+  
     gr.Markdown(f"# {title}")
-
+    gr.Markdown(f"# {detail}")
+    
 
     gr.Markdown(" **[Demo is based on sklearn docs](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_loss_functions.html#sphx-glr-auto-examples-linear-model-plot-sgd-loss-functions-py)**")
 
-    btn = gr.Button(value="SGD convex loss functions")
-    btn.click(plot_loss_func, outputs= gr.Plot() ) # 
+    with gr.Column(variant="panel"):
+        btn = gr.Button(value="SGD convex loss functions")
+        btn.click(plot_loss_func, outputs= gr.Plot() ) # 
 
+        dd = gr.Dropdown(["0-1 loss", "Hinge loss", "Perceptron loss", "Squared Hinge loss", "Modified Huber loss", "Log Loss"], label="loss", info="Select a Loss from the dropdown for a detailed explanation")
+        # inp = gr.Textbox(placeholder="Select a Loss from the dropdown for a detailed explanation")
+        out = gr.Textbox(label="explanation of the loss function")
+        dd.change(explain, dd, out)
     
 
-demo.launch()
+demo.launch()