Add application file

app.py

@@ -0,0 +1,59 @@
+from functools import partial
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn import svm, datasets
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import ConfusionMatrixDisplay
+import gradio as gr
+
+
+def train_model(normalize):
+    # import some data to play with
+    iris = datasets.load_iris()
+    X = iris.data
+    y = iris.target
+    class_names = iris.target_names
+
+    # Split the data into a training set and a test set
+    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+
+    # Run classifier, using a model that is too regularized (C too low) to see
+    # the impact on the results
+    classifier = svm.SVC(kernel="linear", C=0.01).fit(X_train, y_train)
+
+    np.set_printoptions(precision=2)
+
+    title = (
+        "Normalized confusion matrix" if normalize
+        else "Confusion matrix, without normalization"
+    )
+
+    disp = ConfusionMatrixDisplay.from_estimator(
+        classifier,
+        X_test,
+        y_test,
+        display_labels=class_names,
+        cmap=plt.cm.Blues,
+        normalize='true' if normalize else None,
+    )
+    disp.ax_.set_title(title)
+
+    return disp.figure_
+
+
+title = "Confusion matrix"
+description = "Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set"
+with gr.Blocks() as demo:
+    gr.Markdown(f"## {title}")
+    gr.Markdown(description)
+
+    normalize = gr.Checkbox(label="Normalize")
+    plot = gr.Plot(label="Confusion matrix")
+
+    fn = partial(train_model)
+    normalize.change(fn=fn, inputs=[normalize], outputs=plot)
+
+
+demo.launch(enable_queue=True, debug=True)