App itself

app.py

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+import numpy as np
+import gradio as gr
+from sklearn.svm import LinearSVC
+from sklearn.datasets import load_iris
+from sklearn.pipeline import make_pipeline
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import label_binarize, StandardScaler
+
+import utils
+
+
+def app_fn(n_random_features: int, test_size: float, random_state_val: int):
+    X, y = load_iris(return_X_y=True)
+
+    # Add noisy features
+    random_state = np.random.RandomState(random_state_val)
+    n_samples, n_features = X.shape
+    X = np.concatenate([X, random_state.randn(n_samples, n_random_features)], axis=1)
+
+    # Solving Binary Problem
+    X_train, X_test, y_train, y_test = train_test_split(
+        X[y < 2], y[y < 2], test_size=test_size, random_state=random_state
+    )
+
+    clf_bin = make_pipeline(StandardScaler(), LinearSVC(random_state=random_state))
+    clf_bin.fit(X_train, y_train)
+
+    fig_bin = utils.plot_binary_pr_curve(clf_bin, X_test, y_test)
+
+    # Solving Multi-Label Problem
+    Y = label_binarize(y, classes=[0, 1, 2])
+    X_train_multi, X_test_multi, Y_train, Y_test = train_test_split(
+        X, Y, test_size=test_size, random_state=random_state
+    )
+
+    clf = OneVsRestClassifier(
+        make_pipeline(StandardScaler(), LinearSVC(random_state=random_state))
+    )
+    clf.fit(X_train_multi, Y_train)
+
+    fig_multi = utils.plot_multi_label_pr_curve(clf, X_test_multi, Y_test)
+
+    return fig_bin, fig_multi
+
+def app_fn_multi():
+    ...
+
+
+title = "Precision-Recall Curves"
+with gr.Blocks(title=title) as demo:
+    gr.Markdown(f"# {title}")
+    gr.Markdown(
+        """
+        ### This demo shows the precision-recall curves on the Iris dataset \
+        using a Linear SVM classifier + StandardScaler. \
+        Noise is added to the dataset to make the problem more challenging. \
+        The dataset is split into train and test sets. \
+        The model is trained on the train set and evaluated on the test set. \
+        Two separate problems are solved: 
+        
+        ### Binary classification: class 0 vs class 1
+        ### Multi-label classification: class 0 vs class 1 vs class 2
+
+        [Original Example](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py)
+        """
+    )
+
+    with gr.Row():
+        n_random_features = gr.inputs.Slider(0, 1000, 50, 800,label="Number of Random Features")
+        test_size = gr.inputs.Slider(0.1, 0.9, 0.3, 0.5, label="Test Size")
+        random_state_val = gr.inputs.Slider(0, 100, 5, 0,label="Random State")
+
+    btn = gr.Button("Run")
+
+    with gr.Column():
+        fig_bin = gr.Plot(label="Binary PR Curve")
+        fig_multi = gr.Plot(label="Multi-Label PR Curve")
+
+    btn.click(fn=app_fn, inputs=[n_random_features, test_size, random_state_val], outputs=[fig_bin, fig_multi])
+    demo.load(fn=app_fn, inputs=[n_random_features, test_size, random_state_val], outputs=[fig_bin, fig_multi])
+
+demo.launch()
+    
+