Update app.py with plotly figures

In this example push code with plotly figures

app.py

@@ -3,16 +3,11 @@
 
 # This code is a MOD with Gradio Demo
 import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib
+import plotly.graph_objects as go
 
 from sklearn import decomposition
 from sklearn import datasets
 
-# unused but required import for doing 3d projections with matplotlib < 3.2
-import mpl_toolkits.mplot3d  # noqa: F401
-matplotlib.use('agg')
-
 import gradio as gr
 
 np.random.seed(5)
@@ -22,11 +17,8 @@ def PCA_Pred(x1, x2, x3, x4):
     #Load Data from iris dataset:
     iris = datasets.load_iris()
     X = iris.data
-    y = iris.target
-
-    fig, ax = plt.subplots(1, subplot_kw={'projection': '3d', 'elev': 48, 'azim': 134})
-    ax.set_position([0, 0, 0.95, 1])
-    plt.cla()
+    Y = iris.target
+    label_data = [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]
 
     #Create the model with 3 principal components:
     pca = decomposition.PCA(n_components=3)
@@ -35,29 +27,45 @@ def PCA_Pred(x1, x2, x3, x4):
     pca.fit(X)
     X = pca.transform(X)
 
-    #Set labels to data clusters
-    for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]:
-        ax.text3D(
-            X[y == label, 0].mean(),
-            X[y == label, 1].mean() + 1.5,
-            X[y == label, 2].mean(),
-            name,
-            horizontalalignment="center",
-            bbox=dict(alpha=0.5, edgecolor="w", facecolor="w"),
-        )
-    # Reorder the labels to have colors matching the cluster results
-    y = np.choose(y, [1, 2, 0]).astype(float)
-    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral, edgecolor="k")
+    #Create figure with plotly
+    fig = go.Figure()
 
+    for name, label in label_data:
+        fig.add_trace(go.Scatter3d(
+            x=X[Y == label, 0],
+            y=X[Y == label, 1],
+            z=X[Y == label, 2],
+            mode='markers',
+            marker=dict(
+                size=8,
+                color=label,            
+                colorscale='Viridis',   
+                opacity=0.8),
+            name=name
+            ))
+    
     user_iris_data = np.array([[x1, x2, x3, x4]], ndmin=2)
 
     #Perform reduction to user data
     pc_output = pca.transform(user_iris_data)
-    ax.scatter(pc_output[0, 0], pc_output[0, 1], pc_output[0, 2], c='r', marker='*')
-
-    ax.xaxis.set_ticklabels([])
-    ax.yaxis.set_ticklabels([])
-    ax.zaxis.set_ticklabels([])
+    fig.add_traces([go.Scatter3d(
+            x=np.array(pc_output[0, 0]),
+            y=np.array(pc_output[0, 1]),
+            z=np.array(pc_output[0, 2]),
+            mode='markers',
+            marker=dict(
+                size=12,
+                color=4,                # set color
+                colorscale='Viridis',   # choose a colorscale
+                opacity=0.8),
+            name="User data"
+            )])
+    fig.update_layout(scene = dict(
+                            xaxis_title="1st PCA Axis",
+                            yaxis_title="2nd PCA Axis",
+                            zaxis_title="3th PCA Axis"),
+                     legend_title="Species"
+                    )
 
     return [pc_output, fig]