Update app.py

app.py

@@ -1,14 +1,16 @@
-
 """
-========================================
-Plot multi-class SGD on the iris dataset
-========================================
+=========================================================
+Gradio Demo to plot multi-class SGD on the iris dataset
+=========================================================
 
 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.
 
+Created by Syed Affan <[email protected]>
+
 """
+
 import gradio as gr
 import numpy as np
 import matplotlib.pyplot as plt
@@ -17,10 +19,10 @@ from sklearn.linear_model import SGDClassifier
 from sklearn.inspection import DecisionBoundaryDisplay
 import matplotlib.cm
 
-
-def plot(alpha):
+def make_plot(alpha,max_iter,Standardize):
 # import some data to play with
     iris = datasets.load_iris()
+    fig = plt.figure()
 
 # we only take the first two features. We could
 # avoid this ugly slicing by using a two-dim dataset
@@ -36,16 +38,21 @@ def plot(alpha):
     y = y[idx]
 
 # standardize
-    mean = X.mean(axis=0)
-    std = X.std(axis=0)
-    X = (X - mean) / std
+    if Standardize:
+        mean = X.mean(axis=0)
+        std = X.std(axis=0)
+        X = (X - mean) / std
+
 
-    clf = SGDClassifier(alpha=alpha, max_iter=100).fit(X, y)
-    #ax = plt.gca()
+    clf = SGDClassifier(alpha=alpha, max_iter=max_iter).fit(X, y)
+    accuracy = clf.score(X,y)
+    acc = f'## The Accuracy on the entire dataset: {accuracy}'
+    #fig,ax = subplots()
+    ax = plt.gca()
     DecisionBoundaryDisplay.from_estimator(
         clf,
         X,
-        cmap=plt.cm.Paired,
+        cmap=matplotlib.cm.Paired,
         ax=ax,
         response_method="predict",
         xlabel=iris.feature_names[0],
@@ -80,20 +87,19 @@ def plot(alpha):
             return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
 
         plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)
-    
+
+
     for i, color in zip(clf.classes_, colors):
         plot_hyperplane(i, color)
     plt.legend()
-    #plt.show()
-    return plt
-
-#pl = plot(0)
-#pl.show()
-with gr.Blocks() as demo:
-    alpha = gr.Slider(minimum=0.0001, maximum=10, step=0.01, value=0.0001, label="Alpha Value")
-    with gr.Row():
-        plt = gr.Plot()
-
-    alpha.change(fn=plot, inputs=[alpha],outputs=[plt])
-
-demo.launch()
+    
+    return fig,acc
+
+demo = gr.Interface(
+    title = 'Plot multi-class SGD on the iris dataset',
+    fn = make_plot,
+    inputs = [gr.Slider(0.0001,5,step = 0.001,value = 0.001),
+        gr.Slider(1,1000,step=10,value=100),
+        gr.Checkbox(value=True)],
+    outputs = [gr.Plot(),gr.Markdown()]
+        ).launch()